dust2024-03-25T10:49:57+00:00Nick MallesonFinal Project Report2024-02-20T00:00:00+00:00/2024/02/FinalReport<h1 id="final-project-report">Final Project Report</h1>
<p>The dust project has come to an end! It officially finished at the end of December 2023 and I am in the process of compiling the final reports.</p>
<p>If you would like to learn more about what the project did, the best places to look are on the <a href="/dust/publications.html">publications</a> and <a href="/dust/presentations.html">presentations</a> pages.</p>
<p>Here is a high-level summary of what the project was about:</p>
<h2 id="context-and-motivation">Context and Motivation</h2>
<p>When developing computer models of human systems, there are many situations where it is important to model the individual entities that drive the system, rather than using aggregate methods that combine individuals into homogeneous groups. In such circumstances, the technique of ‘agent-based modelling’ has been shown to be useful. In an agent-based model, individual, autonomous ‘agents’ are created. These agents can be given rules or objectives that determine how they will behave in a given situation. They can also interact with each other and with their environment. In this manner it is possible to simulate many of the interesting outcomes that emerge as social systems evolve which might be missed using aggregate methods that do not account for individuals, or their interactions, specifically.</p>
<p>There is a drawback with agent-based models though; they cannot incorporate real-time data into their simulations. This means that it is not possible to use agent-based modelling to simulate systems in real time. Agent-based modelling is an ideal tool for modelling systems such as traffic congestion, crowd dynamics, economic behaviour, disease spread, etc., but it is limited in its ability to make short term, real-world predictions or forecasts because it is not able to react to the most up to date data.</p>
<h2 id="project-aims">Project Aims</h2>
<p>The aim of this project was to develop new methods that will allow real-time data to be fed in to agent-based models to bring them in to line with reality. It leveraged existing ‘data assimilation’ methods, that have been established in fields such as meteorology, and tested how applicable the methods are when adapted for agent-based modelling. The main difficulty that the project encountered was that, due to the complexity of an agent-based model, very large numbers of individual models were often required to be run simultaneously. This requires extremely large amounts of computing power. Therefore the algorithms that were the most useful were typically those that could conduct data assimilation while running the smallest number of models.</p>
<h2 id="results">Results</h2>
<p>The results of the project are largely methodological; it has adapted methods and has shown them to be capable of feeding real-time data into agent-based models at runtime. This has important implications for society because it means that new types of models, such as social ‘digital twins’, could be possible if sufficient computing power and data were available. Immediate future work for the project will be to start to implement the methods in real situations.</p>
<h2 id="main-challenges">Main Challenges</h2>
<p>The project largely focussed on methodological development, with some preliminary empirical applications in later stages. It adapted a number of methods that are commonly used in other fields and has tested how well they are able to allow us to conduct data assimilation for use agent-based models. Specifically, we looked at techniques called Particle Filters, Unscented Kalman Filters and Ensemble Kalman Filters. We also developed an entirely new methods based on quantum field theory and created a way to build agent-based models that allows Markov Chain Monte-Carlo sampling to take place (this is a very efficient way to incorporate observational data).</p>
<p>There were a number of challenges that the project faced.</p>
<p>Firstly, agent-based models are extremely computationally expensive so can take a long time to run. Most data assimilation methods also require a large number of models to be run simultaneously, which significantly exacerbates the problem. Some of the methods, such as the Kalman Filter variations, required smaller numbers of models to be run simultaneously so these delivered the most useful results. In addition, we also looked at emulator/surrogate methods that should allow the full agent-based model to run more quickly, albeit at a lower resolution (rather than having individual agents in the model, we create aggregate approximations that behave as if they were made of individuals). We have tested Random Forests and Gaussian Process Emulators; both of which hold promise.</p>
<p>Another challenge is that data assimilation methods are typically designed for models with continuous variables (e.g. air pressure, wind speed, etc.) but agent-based models often have discrete variables (e.g. an agent’s age, or their destination). Therefore we have adapted the standard approaches to allow the data assimilation methods to include categorical methods.</p>
<p>In the later stages of work the project has also begun to apply the new methods to a variety of different types of model to explore their use in different systems. These include crowding in busy public places, the spread of disease, and international policy diffusion.</p>
<h2 id="future-work">Future work</h2>
<p>Future work will continue to develop these kinds of applications. In particular, the results of the project could make an important contribution in the area of ‘digital twins’ by allowing real-time agent-based models to become part of larger ‘twin’-like simulations.</p>
Sources of Footfall / Pedestrian data2022-10-13T00:00:00+00:00/2022/10/FootfallSensorData<p><a href="https://github.com/masher92">Molly Ahser</a> has just put together a table that summarises some of the best (as far as we can tell) sources of information for estimating footfall (the number of people passing particular places during the day).</p>
<p>The location of the sensors in the cities for which data can be accessed can be viewed at: <a href="https://nbviewer.org/github/masher92/footfall/blob/master/Locations_with_sensors.ipynb?flush_cache=True">Locations_with_sensors.ipynb</a></p>
<table>
<thead>
<tr>
<th>City</th>
<th>Sensor type</th>
<th>Number sensors</th>
<th>Resolution</th>
<th>Time period</th>
<th>Data link</th>
<th> </th>
</tr>
</thead>
<tbody>
<tr>
<td>Leeds</td>
<td>Camera</td>
<td>10</td>
<td>Hourly</td>
<td>2014-2022</td>
<td>Weekly csvs: https://tinyurl.com/4y3dxxzb</td>
<td> </td>
</tr>
<tr>
<td>Bologna</td>
<td>Phone sensing</td>
<td>10</td>
<td>Hourly</td>
<td>11/19-08/22</td>
<td>Monthly csvs: https://tinyurl.com/2p8ty9f2</td>
<td> </td>
</tr>
<tr>
<td>Skopje</td>
<td>Phone sensing</td>
<td>19</td>
<td>Hourly</td>
<td>04/20-04/21</td>
<td>Monthly csvs: https://tinyurl.com/yc6b5xr8</td>
<td> </td>
</tr>
<tr>
<td>Turin</td>
<td>Phone sensing</td>
<td>8</td>
<td>Hourly</td>
<td>11/19-08/22</td>
<td>Monthly csvs:https://tinyurl.com/yc7sdme5</td>
<td> </td>
</tr>
<tr>
<td>Cluj-Napoca</td>
<td>Phone sensing</td>
<td>6</td>
<td>Hourly</td>
<td>11/19-08/22</td>
<td>Monthly csvs: https://tinyurl.com/2z7a3m3k</td>
<td> </td>
</tr>
<tr>
<td>Dublin</td>
<td>Cameras</td>
<td>23 (some added later)</td>
<td>Hourly</td>
<td>2007-2014</td>
<td>Yearly csvs: https://tinyurl.com/2n5he5rv.<br />Existing Analysis: https://tinyurl.com/5n8ht2zt</td>
<td> </td>
</tr>
<tr>
<td>York</td>
<td>Cameras</td>
<td>6</td>
<td>Hourly</td>
<td>2009-2022</td>
<td>One csv: https://tinyurl.com/ymj68ke6.<br />Existing Analysis: https://tinyurl.com/bdz24use</td>
<td> </td>
</tr>
<tr>
<td>Liverpool (Australia)</td>
<td>Cameras / Wifi-sensors</td>
<td>13 cameras/4 sensors</td>
<td>Not sure</td>
<td>2021-22</td>
<td>One csv: https://tinyurl.com/3kekyzs7 (only includes bikes/cars and not pedestrians</td>
<td> </td>
</tr>
<tr>
<td>Melbourne</td>
<td>Sensor detecting movement.</td>
<td>65 (some added later)</td>
<td>Hourly</td>
<td>2009-present</td>
<td>One csv: https://tinyurl.com/45jnppsa <br /> Existing analysis: https://tinyurl.com/4xdd6epx</td>
<td> </td>
</tr>
<tr>
<td>Edinburgh</td>
<td>Drakewell counters; not documented how they work</td>
<td>58 (including cycle counters)</td>
<td>Hourly</td>
<td>2015-present</td>
<td>Dashboard with data for specific days https://tinyurl.com/yk2dafse.<br />Raw data not available</td>
<td> </td>
</tr>
<tr>
<td>Glasgow</td>
<td>Springboard (fixed footfall sensor)</td>
<td>7</td>
<td>Hourly</td>
<td>2010-present</td>
<td>Data: https://tinyurl.com/y24bpa36 (not sure on format as date is not specified</td>
<td> </td>
</tr>
<tr>
<td> </td>
<td>CCTV cameras</td>
<td>40</td>
<td>15/30 mins</td>
<td>October 2021-present</td>
<td>Daily data: https://tinyurl.com/ms5nyzb3 (not sure how to get 15/30 min data)</td>
<td> </td>
</tr>
<tr>
<td>San Diego</td>
<td>Infrared sensor using body temp</td>
<td>11 walking counters (more for cycling)</td>
<td>15 mins</td>
<td>2012-present</td>
<td>Dashboard: https://tinyurl.com/5xmz9e9k.<br />Raw data not available</td>
<td> </td>
</tr>
<tr>
<td>Bath</td>
<td>Cameras</td>
<td>10</td>
<td>Hourly</td>
<td>03/17-05/19</td>
<td>Data summary: https://tinyurl.com/yck2asyp.<br />Raw data not available. <br />Existing Analysis: https://tinyurl.com/2p93hztd</td>
<td> </td>
</tr>
<tr>
<td> </td>
<td>Phone sensing</td>
<td>1</td>
<td>Daily</td>
<td>01/19-03/20</td>
<td>Data analysis: https://tinyurl.com/2p8xxt8h.<br />Raw data not available</td>
<td> </td>
</tr>
<tr>
<td>Helsinki</td>
<td>Bluetooth</td>
<td>N/A</td>
<td> </td>
<td> </td>
<td>Data heat map: https://tinyurl.com/bdfjhssm.<br />Raw data not available</td>
<td> </td>
</tr>
</tbody>
</table>
New ABM papers on data assimilation and emulation2022-06-06T00:00:00+00:00/2022/06/New_Dust_Publications<h1 id="new-abm-papers-on-data-assimilation-and-emulation">New ABM papers on data assimilation and emulation</h1>
<figure style="float:right; width:60%; padding:10px;">
<img src="https://www.nickmalleson.co.uk/figures/paper_figures/emulators-sensors.jpeg" alt=" Counts of pedestrians at different sensors." />
</figure>
<p>We have recently published two new papers from the DUST project. The first, by Dan Tang, introduces a new method that allows ABMs to be sampled using efficient Markov chain sampling. The second, by Minh Kieu, explores the possibilities of emulating ABMs using machine learning.</p>
<ul>
<li>
<p>Tang, D. and N. Malleson (2022). Data assimilation with agent-based models using Markov chain sampling. <em>Open Research Europe</em> 2(70). DOI: <a href="https://doi.org/10.12688/openreseurope.14800.1">10.12688/openreseurope.14800.1</a> (open access)</p>
</li>
<li>
<p>Kieu, M., H. Nguyen, J.A. Ward and N. Malleson (2022). Towards real-time predictions using emulators of agent-based models. <em>Journal of Simulation</em> 1–18. DOI: <a href="https://doi.org/10.1080/17477778.2022.2080008">10.1080/17477778.2022.2080008</a>.</p>
</li>
</ul>
Emulating Stochastic Models2022-01-10T00:00:00+00:00/2022/01/StochasticEmulators<h1 id="exploration-of-gaussian-processes-for-emulation-of-stochastic-models">Exploration of Gaussian processes for emulation of stochastic models</h1>
<figure style="float:right; width:60%; padding:10px;">
<a href="https://github.com/Urban-Analytics/stochastic-gp/"><img src="/dust/figures/stochastic_emulation_example.png" alt="Example graph showing uncertainty in bus predictions" /></a>
</figure>
<p>Working with Turing Research Software Engineer <a href="https://github.com/LouiseABowler">Louise Bowler</a>, we have recently finished a short project that experimented with the use Gaussian Processes (GPs) to emulate agent-based models. It was developed as part of the project <a href="https://www.turing.ac.uk/research/research-projects/uncertainty-agent-based-models-smart-city-forecasts">Uncertainty in agent-based models for smart city forecasts</a>, funded by the <a href="https://www.turing.ac.uk/">Alan Turing Institute</a>.</p>
<p>The results are available in full from the <a href="https://github.com/Urban-Analytics/stochastic-gp">GitHub Repo</a>. The emulators worked well at emulating the behaviour of deterministic agent-based models. But when a degree of randomness was introduced into the model, as is often the case in agent-based modelling, they struggled to cope with the additional uncertainty and did not emulate the behaviour of the underlying models particularly well. The project has identified a number of interesting questions that need further research.</p>
New paper: Real-Time Crowd Modelling2021-08-16T00:00:00+00:00/2021/08/UKF_SIMPAT<!-- # Quantifying the uncertainty in agent-based models (aka what the \*$&^% is going on in my model and why?) -->
<figure style="float:right; width:30%; padding:10px;">
<a href="https://www.sciencedirect.com/journal/simulation-modelling-practice-and-theory"><img src="https://ars.els-cdn.com/content/image/1-s2.0-S1569190X21X00056-cov200h.gif" alt="Journal cover picture" /></a>
</figure>
<p>We have just published a new paper in <a href="www.elsevier.com/locate/simpat"><em>Simulation Modelling Practice and Theory</em></a>. The paper presents a new method to allow a crowd simulation to be optimised with real data <em>while the model is running</em>. The full details are:</p>
<p>Clay, Robert, J. A. Ward, P. Ternes, Le-Minh Kieu, N. Malleson (2021) Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter. <em>Simulation Modelling Practice and Theory</em> 113 (102386) DOI: <a href="https://doi.org/10.1016/j.simpat.2021.102386">10.1016/j.simpat.2021.102386</a></p>
<blockquote>
Commonly-used data assimilation methods are being adapted for use with agent-based models with the aim of allowing optimisation in response to new data in real-time. However, existing methods face difficulties working with categorical parameters, which are common in agent-based models. This paper presents a new method, the RJUKF, that combines the Unscented Kalman Filter (UKF) data assimilation algorithm with elements of the Reversible Jump (RJ) Markov chain Monte Carlo method. The proposed method is able to conduct data assimilation on both continuous and categorical parameters simultaneously. Compared to similar techniques for mixed state estimation, the RJUKF has the advantage of being efficient enough for online (i.e. real-time) application. The new method is demonstrated on the simulation of a crowd of people traversing a train station and is able to estimate both their current position (a continuous, Gaussian variable) and their chosen destination (a categorical parameter). This method makes a valuable contribution towards the use of agent-based models as tools for the management of crowds in busy places such as public transport hubs, shopping centres, or high streets.
</blockquote>
SocSim Keynote: Quantifying the uncertainty in agent-based models2021-03-22T00:00:00+00:00/2021/03/SocSim_Keynote<!-- # Quantifying the uncertainty in agent-based models (aka what the \*$&^% is going on in my model and why?) -->
<figure style="float:right; width:30%; padding:10px;">
<img src="/dust/figures/socsimfest/socsimfest_logo.png" alt="SocSim Fest 2021 logo" />
</figure>
<p>My colleague <a href="https://environment.leeds.ac.uk/geography/staff/1046/professor-alison-heppenstall">Alison</a> and I recently gave a keynote presentation to the <a href="http://www.essa.eu.org/">ESSA</a> <a href="https://www.socsimfest21.eu/">Social Simulation Fest</a> 2021 called <strong><a href="/dust/p/2021-03-17-ESSA_SocSimFest.html">Quantifying the uncertainty in agent-based models (aka what the @$&^% is going on in my model and why?)</a></strong>. We talked about some of the agent-based modelling work that we (and our colleagues) are doing, particularly with respect to understanding the uncertainties in our models.</p>
<p>The full abstract is:</p>
<blockquote>
Agent-based modelling is maturing as a method for capturing and simulating individual behaviour and activity. Whilst there are a dazzling array of applications appearing in the literature, there is less work that focusses on important methodological issues such as the handling of uncertainty in these models. We discuss (and demonstrate) how approaches from the field of Uncertainty Quantification can be adapted for use in agent-based models so that models can become robust enough to be used in important policy decisions.
</blockquote>
<figure style="width:70%; padding:10px;">
<a href="https://github.com/Urban-Analytics/RAMP-UA/">
<img src="/dust/figures/ramp/ramp_gui_2.png" alt="GUI produced for the RAMP project" />
</a>
<figcaption>A graphical user interface produced for the <a href="https://github.com/Urban-Analytics/RAMP-UA/">RAMP</a> project. Understanding the uncertainties in models of disease spread can be particularly difficult.</figcaption>
</figure>
Ambient populations: Developing robust estimates2021-03-03T00:00:00+00:00/2021/03/Ambient_Pop_Bristol<!-- # Ambient populations: Developing robust estimates-->
<p>My colleague <a href="https://environment.leeds.ac.uk/geography/pgr/2493/annabel-whipp">Annabel</a> and I recently gave a talk as part of the <a href="https://seis.bristol.ac.uk/~nb14397/seminars.html">Engineering Mathematics Seminar Series</a> at the <a href="https://www.bristol.ac.uk/engineering/departments/engineering-mathematics/">Department of Engineering Mathematics, University of Bristol</a>. It was called <strong><a href="/dust/p/2021-03-03-Ambient_Pop_Bristol.pdf">Ambient populations: Developing robust estimates</a></strong> and presented our work on estimating the ambient population for uses in crime analysis as well as our ideas for new methods to integrate various datasets to create a more accurate measure of the ambient population. The slides are available <a href="/dust/p/2021-03-03-Ambient_Pop_Bristol.pdf">here</a> and the abstract is:</p>
<blockquote>There is typically an abundance of information about where people live (the ‘residential’ population), but much less information regarding the non-residential, or ‘ambient’ population. The ability to produce estimates of the ambient population is integral to the management and planning of urban areas and is a precursor to the development of insights into socioeconomic and environmental issues that impact cities. In this presentation we provide examples that illustrate why a robust quantification of the ambient population is essential to properly understand many social phenomena, drawing particularly on the analysis of crime patterns. We also review a number of new data sources that have emerged in recent years that might help to shed light on the ambient population and propose a means of incorporating disparate data to estimate the ambient population using geographically-weighted regression.</blockquote>
Building Cities from Slime Mould, Agents and Quantum Field Theory2020-05-27T00:00:00+00:00/2020/05/Alison_AAMAS<figure style="float:right; width:30%; padding:10px;">
<img src="https://cpb-ap-se2.wpmucdn.com/blogs.auckland.ac.nz/dist/d/603/files/2019/05/aamas_logov3.png" alt="AAMAS 2020 logo" />
</figure>
<p>My colleague <a href="https://environment.leeds.ac.uk/geography/staff/1046/professor-alison-heppenstall">Alison Heppenstall</a> was recently invited to present our work as a keynote speaker at the International Conference on Autonomous Agents and Multi-Agent Systems (<a href="https://aamas2020.conference.auckland.ac.nz/">AAMAS</a>) entitled <a href="https://aamas2020.conference.auckland.ac.nz/alison-heppenstall/">Building Cities from Slime Mould, Agents and Quantum Field Theory</a>. The full paper is available <a href="https://dl.acm.org/doi/abs/10.5555/3398761.3398765">here</a> and the abstract is:</p>
<blockquote>Managing the unprecedented growth of cities whilst ensuring that they are sustainable, healthy and equitable places to live, presents significant challenges. Our current thinking conceptualise cities as being driven by processes from the bottom-up, with an emphasis on the role that individual decisions and behaviour play. Multi-agent systems, and agent-based modelling in particular, are ideal frameworks for the analysis of such systems. However, identifying the important drivers within an urban system, translating key behaviours from data into rules, quantifying uncertainty and running models in real time all present significant challenges. We discuss how innovations in a diverse range of fields are influencing empirical agent-based models, and how models designed for the simplest biological systems might transform the ways that we understand and manage real cities.</blockquote>
<p>As the COVID-19 pandemic naturally meant that the conference took place virtually, her keynote is available to view for free from the <a href="https://underline.io/lecture/60-building-cities-from-slime-mould,-agents-and-quantum-field-theory">virtual conference website</a>. The other keynotes and lectures are available <a href="https://underline.io/conferences/19">here</a>.</p>
Special Issue: Innovations in Spatial ABMs2020-04-20T00:00:00+00:00/2020/04/IJGIS_Special_Issue-ABMGIS<figure style="width:40%;float:right; padding: 1em;">
<img src="https://www.mdpi.com/img/journals/ijgi-logo.png" alt="journal logo" />
</figure>
<p>We are pleased to announce a new special issue in the <a href="https://www.mdpi.com/journal/ijgi"><em>ISPRS International Journal of Geo-Information</em></a> on ‘<a href="https://www.mdpi.com/journal/ijgi/special_issues/innovation_agent">Innovations in Agent-Based Modelling of Spatial Systems</a>’. We particularly encourage contributions from <em>early career researchers</em> (details below). This special issue call is also available as a <a href="https://www.mdpi.com/journal/ijgi/special_issue_flyer_pdf/innovation_agent/web">flyer (pdf)</a>. Full details are available from the <a href="https://www.mdpi.com/journal/ijgi/special_issues/innovation_agent">journal website</a>.</p>
<p>Guest Editors: <a href="http://www.nickmalleson.co.uk/">Nick Malleson</a>, <a href="https://environment.leeds.ac.uk/staff/9293/professor-ed-manley">Ed Manley</a>, <a href="https://environment.leeds.ac.uk/geography/staff/1046/professor-alison-heppenstall">Alison Heppenstall</a>.</p>
<p><strong>Innovations in Agent-Based Modelling of Spatial Systems</strong></p>
<p>Agent-Based Modelling is a method that is ideally suited to modelling many social and spatial phenomena, particularly in human-environmental systems where the interactions and behaviours of heterogeneous individuals are key to understanding the wider system-level behaviour. Although it is a relatively new method that understandably receives criticism, significant progress has been made towards: (i) strengthening the reliability of agent-based models through the development of comprehensive <strong>validation methods</strong>; (ii) the use of new <strong>data sources</strong> to better capture observed and simulated patterns at different scales; or (iii) through closer working with the <strong>‘end users’ of research</strong>, such as policy makers and businesses, so that the uncertainties and limitations of any modelled results are properly understood.</p>
<p>This Special Issue aims to capture state-of-the-art findings in agent-based modelling of spatial systems, with a particular emphasis on papers that are advancing the discipline through new approaches to validation, the use of new data, or policy-focussed work that can evidence a strong collaboration with end users. We encourage papers ranging from those that address fundamental methodological issues in toy systems to fully-fledged empirical applications. Submissions are particularly encouraged from early-career researchers, following the European Research Council definition of <strong>within 7 years since the completion of their PhD</strong> (plus additional allowances for career breaks etc.). Research supervisors and senior colleagues are allowed as co-authors, but the lead author must be an early career researcher.</p>
<p>Submissions are invited that cover a range of issues, with topics including (but not limited to):</p>
<ul>
<li>
<p>Methods to support better validation of spatial agent-based models</p>
</li>
<li>
<p>Empirical applications of spatial agent-based models</p>
</li>
<li>
<p>The use of innovative methods to advance agent-based modelling, such as machine learning, AI methods, or Bayesian approaches</p>
</li>
<li>
<p>The incorporation of novel data to inform agent-based models, such as volunteered geographical information or ‘big’ data sources</p>
</li>
<li>
<p>Modelling spatial systems in real time</p>
</li>
<li>
<p>Understanding and/or quantifying the uncertainty associated with spatial agent-based models</p>
</li>
<li>
<p>Applications of agent-based modelling to policy-focussed problems</p>
</li>
<li>
<p>Developments in the visualisation of spatial agent-based models</p>
</li>
<li>
<p>Applications focussed on urban phenomena such as short-term population flows, crowding/congestion, pollution, etc.</p>
</li>
</ul>
<p>Full details are available from the <a href="https://www.mdpi.com/journal/ijgi/special_issues/innovation_agent">journal website</a>.</p>
Pedestrian Simualtion Software Review2020-03-10T00:00:00+00:00/2020/03/PedSimReview<p>Thomas Richard, one of our Data Science Interns at the <a href="https://lida.leeds.ac.uk">Leeds Institute for Data Science</a>, <a href="https://leeds.ac.uk">University of Leeds</a>, has just produced an excellent overview of a large number of agent-based platforms and libraries that can be used to create simulations of pedestrians. The main aim was to find a library that will allow us to use data assimilation to update the state of the model at runtime.</p>
<p>The review is available online or as a PDF:</p>
<ul>
<li>
<p><a href="https://urban-analytics.github.io/dust/docs/ped_sim_review.html">online</a></p>
</li>
<li>
<p><a href="https://urban-analytics.github.io/dust/docs/ped_sim_review.pdf">pdf</a></p>
</li>
</ul>
New Paper: Real-time Bus Simulation with a Particle Filter2020-01-17T00:00:00+00:00/2020/01/BusSim_PF<iframe src="https://urban-analytics.github.io/dust/p/2019-05-14-ABMUS-BusSim-MK.html" style="width:320px; height:210px; float:right"></iframe>
<p>We have just published a new open access paper that is about how data assimilation (using a particle filter) can reduce the uncertainty in a simulation of a bus route. Ultimately we hope that the work will allow us to improve bus servies for passengers by, for example, creating better short-term predictions bus arrival times. You can see the slides for the accompanying presentation on the right.</p>
<p>The citation is:</p>
<ul>
<li>Kieu, Le-Minh, <strong>N. Malleson</strong>, and A. Heppenstall (2019). Dealing with Uncertainty in Agent-Based Models for Short-Term Predictions’. <em>Royal Society Open Science</em> 7(1): 191074. DOI: <a href="https://doi.org/10.1098/rsos.191074">10.1098/rsos.191074</a> (open access)</li>
</ul>
<p>The abstract is:</p>
<ul>
<li>Agent-based models (ABMs) are gaining traction as one of the most powerful modelling tools within the social sciences. They are particularly suited to simulating complex systems. Despite many methodological advances within ABM, one of the major drawbacks is their inability to incorporate real-time data to make accurate short-term predictions. This paper presents an approach that allows ABMs to be dynamically optimized. Through a combination of parameter calibration and data assimilation (DA), the accuracy of model-based predictions using ABM in real time is increased. We use the exemplar of a bus route system to explore these methods. The bus route ABMs developed in this research are examples of ABMs that can be dynamically optimized by a combination of parameter calibration and DA. The proposed model and framework is a novel and transferable approach that can be used in any passenger information system, or in an intelligent transport systems to provide forecasts of bus locations and arrival times.</li>
</ul>
New Preprint: Real-time Crowd Simulation with a Particle Filter2019-09-23T00:00:00+00:00/2019/09/PF_Preprints<iframe src="https://urban-analytics.github.io/dust/p/2019-09-20-Crowd_Simulation-CASA.html" style="width:320px; height:210px; float:right"></iframe>
<p>I have just uploaded a new preprint to the archive <a href="http://arxiv.org/abs/1908.08288">new paper to the archive </a> about using a particle filters to assimilate data into a real-time model of pedestrian crowding:</p>
<p><strong>Malleson, N.</strong>, Kevin Minors, Le-Minh Kieu, Jonathan A. Ward, Andrew A. West, Alison Heppenstall (2019) Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter. <em>Preprint</em>: <a href="https://arxiv.org/abs/1909.09397">arXiv:1909.09397 [cs.MA]</a>.</p>
<p>The abstract is:</p>
<p>Agent-based modelling is a valuable approach for systems whose behaviour is driven by the interactions between distinct entities. They have shown particular promise as a means of modelling crowds of people in streets, public transport terminals, stadiums, etc. However, the methodology faces a fundamental difficulty: there are no established mechanisms for dynamically incorporating real-time data into models. This limits simulations that are inherently dynamic, such as pedestrian movements, to scenario testing of, for example, the potential impacts of new architectural configurations on movements. This paper begins to address this fundamental gap by demonstrating how a particle filter could be used to incorporate real data into an agent-based model of pedestrian movements at run time. The experiments show that it is indeed possible to use a particle filter to perform online (real time) model optimisation. However, as the number of agents increases, the number of individual particles (and hence the computational complexity) required increases exponentially. By laying the groundwork for the real-time simulation of crowd movements, this paper has implications for the management of complex environments (both nationally and internationally) such as transportation hubs, hospitals, shopping centres, etc. </p>
<p>The paper was also recently presented at the <a href="https://www.eventbrite.co.uk/e/the-macarthur-workshop-on-urban-modelling-complexity-science-tickets-69983794413">MacArthur Workshop on Urban Modelling & Complexity Science</a>, <a href="http://www.casa.ucl.ac.uk/">Centre for Advanced Spatial Analysis</a>, <a href="http://www.casa.ucl.ac.uk/">UCL</a>, 19-20 September 2019. The slides are available above (<a href="https://urban-analytics.github.io/dust/p/2019-09-20-Crowd_Simulation-CASA.html">direct link</a>).</p>
New Preprint: Dealing with uncertainty in agent-based models for short-term predictions2019-09-02T00:00:00+00:00/2019/09/BusSim_Paper_arXiv<!--
<img style="float:right; width:50%" src="https://urban-analytics.github.io/dust/figures/bus-bunching.png" alt="Graph showing movement of buses away from the depot and the phenomena of 'bus bunching'"/>-->
<video controls="" autoplay="" style="float:right; width:60%;">
<source src="https://urban-analytics.github.io/dust/videos/bussim-pf-video.mp4" type="video/mp4" />
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<img style="float:right; width:60%" src="https://urban-analytics.github.io/dust/figures/bus-bunching.png" alt="Graph showing movement of buses away from the depot and the phenomena of 'bus bunching'" />
</video>
<p>My colleague <a href="https://environment.leeds.ac.uk/geography/staff/2520/dr-minh-kieu">Minh Kieu</a> has just uploaded a <a href="https://arxiv.org/abs/1908.08288">new paper to the archive</a> about using a particle filter to incorporate real-time information into a model of a bus route. It is currently under peer review in a journal.</p>
<p>Kieu, Le-Minh, Nicolas Malleson, and Alison Heppenstall. 2019. Dealing with Uncertainty in Agent-Based Models for Short-Term Predictions. <em>ArXiv:1908.08288 [Cs]</em>. <a href="http://arxiv.org/abs/1908.08288">http://arxiv.org/abs/1908.08288</a>.</p>
<p>The abstract is:</p>
<p><q><em>Agent-based models (ABM) are gaining traction as one of the most powerful modelling tools within the social sciences. They are particularly suited to simulating complex systems. Despite many methodological advances within ABM, one of the major drawbacks is their inability to incorporate real-time data to make accurate short-term predictions. This paper presents an approach that allows ABMs to be dynamically optimised. Through a combination of parameter calibration and data assimilation (DA), the accuracy of model-based predictions using ABM in real time is increased. We use the exemplar of a bus route system to explore these methods. The bus route ABMs developed in this research are examples of ABMs that can be dynamically optimised by a combination of parameter calibration and DA. The proposed model and framework can also be used in an passenger information system, or in an Intelligent Transport Systems to provide forecasts of bus locations and arrival times.</em></q></p>
<p>The paper was also recently presented at the 4th International Workshop on Agent-Based Modelling of Urban Systems (<a href="http://modelling-urban-systems.com/abmus2019">ABMUS</a>), part of the International Conference on Autonomous Agents and Multiagent Systems (<a href="http://aamas2019.encs.concordia.ca/">AAMAS 2019</a>). 13-17 May, Montreal. The <a href="/dust/p/2019-05-14-ABMUS-BusSim-MK.html">slides</a> from that talk are below:</p>
<iframe src="https://urban-analytics.github.io/dust/p/2019-05-14-ABMUS-BusSim-MK.html" style="width:640px; height:420px"></iframe>
Probabilistic Programming for Dynamic Data Assimilation on an Agent-Based Model: Early Progress2019-08-29T00:00:00+00:00/2019/08/Luke_Probabilistic_Programming<p>Usually in computer programming, variables are assigned to specific values (e.g. a virtual person in a computer simulation might have an ‘age’ variable which stores a number). Probabilistic programming, on the other hand, allows you to represent <em>random variables</em>. These are variables whose values we do not know precisely, so can be represented by a probability distribution rather than a single value.</p>
<p>This is potentially a very elegant way of capturing uncertainty in our models. However, using the probabilistic programming approach for agent-based modelling raises a number of questions. In the following slides, LIDA data science intern Luke Archer introduces his recent work that explores the use of probabilistic programming libraries for building agent-based models.</p>
<p><a href="https://urban-analytics.github.io/dust/p/2019-08-29-Luke_Probabilistic_Programming.pptx">Download Slides (pptx)</a>.</p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/Ds5bDGEF7TN3q8" width="595" height="485" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen=""> </iframe>
<div style="margin-bottom:5px"> <strong> <a href="//www.slideshare.net/NickMalleson/probabilistic-programming-for-dynamic-data-assimilation-on-an-agentbased-model" title="Probabilistic Programming for Dynamic Data Assimilation on an Agent-Based Model" target="_blank">Probabilistic Programming for Dynamic Data Assimilation on an Agent-Based Model</a> </strong> from <strong><a href="https://www.slideshare.net/NickMalleson" target="_blank">Nick Malleson</a></strong> </div>
Project Updates: Probabilistic ABMs and Data Assimilation2019-07-22T00:00:00+00:00/2019/07/Lida_Interns_Update<p>Two of our <a href="http://lida.leeds.ac.uk/">LIDA</a> Data Science Interns have just presented their latest work.</p>
<ul>
<li>
<p>Robert Clay presented an update on the <em>Understanding and Quantifying Uncertainty in Agent-Based Models for Smart City Forecasts</em> project that discusses the use of an Unscented Kalman Filter to try to incorporate real-time data into a crowding model. <a href="https://urban-analytics.github.io/dust/p/2019-07-22-LIDA_Update-UKF.pptx">Download Slides (pptx)</a>.</p>
</li>
<li>
<p>Benjamin Wilson presented his latest work on using a probabilistic programming library (Pyro) to create an agent-based model. <a href="https://urban-analytics.github.io/dust/p/2019-07-22-LIDA_Update-Probabilistic_ABM.pptx">Download Slides (pptx)</a>.</p>
</li>
</ul>
Post-Doctoral Research Fellow: Simulating Urban Systems2019-06-11T00:00:00+00:00/2019/06/PDRA-SimulatingUrbanSystems2<figure style="width:40%;float:right; padding: 1em;">
<img src="https://urban-analytics.github.io/dust/figures/shutterstock_615414464-small.jpg" alt="London at night with blue shiny lines" />
</figure>
<p>We have just started advertising for a 3 year, full time, post-doctoral position in the <a href="http://lida.leeds.ac.uk/">Leeds Institute for Data Analytics</a> (in the UK) on building agent-based models for simulating urban systems. It’s linked to the ERC-funded ‘<a href="http://dust.leeds.ac.uk/">dust</a>’ project.</p>
<p>The main aim of the role is to support the development of an agent-based simulation that will model the dynamic flows of people around urban areas, ultimately incorporating streams of real time ‘big’ data into the model to reduce uncertainty.</p>
<p>Further details below, full details here: <a href="https://jobs.leeds.ac.uk/Vacancy.aspx?ref=ENVGE1092">https://jobs.leeds.ac.uk/Vacancy.aspx?ref=ENVGE1092</a></p>
<p>Please get in touch with me (<a href="mailto:N.S.Malleson@leeds.ac.uk">Nick Malleson</a>) if you’d like any more information, or to discuss an application.</p>
<hr />
<p><strong>Are you an ambitious researcher looking for your next challenge? Do you have a background in computer simulation, statistics, and data analytics? Do you want to further your career in one of the UK’s leading research intensive Universities?</strong></p>
<p>You will work on a new project, funded by the European Research Council, called <a href="http://dust.leeds.ac.uk/">Data Assimilation for Agent-Based Models (DUST)</a>. The utilmate aim of the project is to develop a comprehensive simulation that can be used to model the current state of an urban area and provide valuable information to policy makers. Agent-based modelling is an ideal methodology for this type of simulation but one that suffers from a serious drawback: models are not able to incorporate up-to-date data to reduce uncertainty. There is a wealth of new data being generated in ‘smart’ cities that could inform a model of urban dynamics (e.g. from social media contributions, mobile telephone use, public transport records, vehicle traffic counters, etc.) but we lack the tools to incorporate these streams of data into agent-based models. Instead, models are typically initialised with historical data and therefore their estimates of the current state of a system diverge rapidly from reality.</p>
<p>The research team is lead by Dr Nicolas Malleson and will be located within the Leeds Institute for Data Analytics (LIDA), which has been established with more than £20 million of funding from the University and four major research councils. The city of Leeds is already recognised as a hub for big data analytics in business, health care and academic research. In addition, the University has also recently become a partner in the <a href="https://www.turing.ac.uk/">Alan Turing Institute</a>, which is the UK’s national institute for data science, and this new collaboration offers exciting opportunites to for LIDA researchers to engage with scientific leaders from a range of fields.</p>
Particle Filters for Smart City Forecasts2019-05-01T00:00:00+00:00/2019/05/ParticleFilter-BlogPost-KM<h1 id="understanding-and-quantifying-uncertainty-in-agent-based-models-for-smart-city-forecasts---kevin-minors">Understanding and Quantifying Uncertainty in Agent-Based Models for Smart City Forecasts - Kevin Minors</h1>
<p><em>This post reports on the progress of Kevin Minors’ <a href="http://lida.leeds.ac.uk/">LIDA</a> data science project (Oct 2018 - Apr 2019) that is part of the Data Assimilation for Agent-Based Modelling (<a href="https://urban-analytics.github.io/dust/">DUST</a>) programme. The original iPython Notebook is available <a href="https://github.com/Urban-Analytics/dust/tree/master/Projects/StationSim-py/ParticleFilter_BlogPost-KM">here</a>.</em></p>
<h2 id="introduction">Introduction</h2>
<p><img src="data:image/jpeg;base64, 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" alt="smart%20city.jpeg" /></p>
<p>Smart cities are increasingly using data to become more efficient and sustainable. One of the difficult questions smart cities want to answer is: how do people behave? One way to solve this issue is to use agent based models. These models use simulated individuals to model the way real human behave, including interactions between individuals and interactions between individuals and the environment.</p>
<p><img src="data:image/png;base64, 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" alt="Agent-based%20models.png" /></p>
<p>One common problem with agent based models is that, once started, they evolve independently from changes in the real world. This means they often diverge from the true state of the system. One way to overcome this challenge is to use data assimilation. This process uses observations from the real world to update the agent based model in real time to ensure it remains accurate.</p>
<p><img src="data:image/png;base64, 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alt="Improved-Model-Results-Resized.png" /></p>
<p>One method of data assimilation is called a particle filter. Particle filters run multiple instances of a model and evolve them forward in time. These individual models are known as ‘particles’. When new observations are received from the real world, the particles are reweighted and resampled so that those that are closer to the new information are more likely to be duplicated and particles that are far away are more likely to be discarded. Below is an example of a particle filter. The dots represent the particles, the blue line represent the true state of the model, and the crosses represent the observations of the true state.</p>
<p><img src="data:image/png;base64, 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" alt="particle_filter_example.png" /></p>
<p>The aim of this project is to apply a particle filter to an agent based model to determine how well it can assimilate data. As we’re not ready to test these methods on models of real systems yet, we run the agent-based model and record the results, which are taken as the ‘reality’. When the particle filter produces results, we compare them to the results from the model recorded as ‘reality’. This is called the ‘identical twin experiment’.</p>
<h2 id="adapting-particle-filter-from-r-labbe-book">Adapting particle filter from R. Labbe book</h2>
<p>The project started with background reading of ‘Kalman and Bayesian Filters in Python’ by Roger Labbe.</p>
<p><a href="https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python">https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python</a></p>
<p>The beginning chapters focus on simple algorithms that combine measurements from sensors and predictions from models to estimate the state of a system, known as a ‘filter’, and they gradually progress to more advanced filters, including a variety of Kalman filters and a particle filter. I decided to focus on the particle filter.</p>
<p>The particle filter chapter in the textbook discusses the general algorithm: generate particles, predict particles forward, update the weights of the particles, resample the particles based on the weights, and then compute state estimates using the particles. The following flow diagram outlines the process of a particle filter.</p>
<p><img src="data:image/png;base64, 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" alt="PF_flowchart.png" /></p>
<h3 id="initialise-particle-filter">Initialise particle filter</h3>
<p>Begin by setting up all particle filter parameters, variables, and data structures.</p>
<h3 id="initialise-base-model">Initialise base model</h3>
<p>Initialise the model that the particle filter will be trying to simulate. This underlying model will provide the particle filter with information on where all the individuals are, called an ‘observation’.</p>
<h3 id="step-base-model">Step base model</h3>
<p>Increment the base model by allowing the model to run one step forward in time.</p>
<h3 id="step-particles">Step particles</h3>
<p>Increment the particles by allowing them to run one step forward in time.</p>
<h3 id="new-observation">New observation</h3>
<p>A new observation is received from the base model after a given number of time steps have passed, called a ‘resample window’. This observation provides the particle filter with the lcoation of the agents in the base model.</p>
<h3 id="reweight-particles">Reweight particles</h3>
<p>With the new observation, we weight the particles based on how similar they are to the observation. If a particle is very similar to the observation, we give it a large weight. If the particle is very different from the observation, we give it a small weight.</p>
<h3 id="resample-particles">Resample particles</h3>
<p>Then, using the new weights, we resample the particles. Particles with large weights are more likely to be duplicated while particles with small weights are more likely to be discarded.</p>
<h3 id="simulation-done">Simulation done</h3>
<p>Once all agents in the base model have completed the simulation, the particle filter is finished.</p>
<h3 id="saveplot-details">Save/plot details</h3>
<p>We finally save and plot any results from the particle filter.</p>
<p>The textbook then moves on to examples of an sequential importance resampling (SIR) particle filter and highlights the problems that occur when there are not enough particles near the target. It finishes with a comparison of the different resampling methods.</p>
<p>The actual particle filter in this chapter is written for a robot moving in an environment filled with landmarks. Below is the code for this particle filter estimating the location of a robot based on distances to numerous landmarks.</p>
<h2 id="particle-filter-for-robot-and-landmarks">Particle filter for robot and landmarks</h2>
<p>(<em>This code, which is from Roger Labbe’s <a href="https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python">Kalman and Bayesian Filters in Python</a> (<a href="https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/12-Particle-Filters.ipynb">Chapter 12 - Particle Filters</a>) book, was used as the basis for my own particle filter code</em>).</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">uniform</span>
<span class="kn">from</span> <span class="nn">filterpy.monte_carlo</span> <span class="kn">import</span> <span class="n">systematic_resample</span>
<span class="kn">from</span> <span class="nn">numpy.linalg</span> <span class="kn">import</span> <span class="n">norm</span>
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">randn</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">scipy.stats</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">seed</span>
<span class="k">def</span> <span class="nf">create_uniform_particles</span><span class="p">(</span><span class="n">x_range</span><span class="p">,</span> <span class="n">y_range</span><span class="p">,</span> <span class="n">hdg_range</span><span class="p">,</span> <span class="n">num_of_particles</span><span class="p">):</span>
<span class="s">'''
Create Uniformly Distributed Particles
PARAMETERS
- x_range: Interval of x values for particle locations
- y_range: Interval of y values for particle locations
- hdg_range: Interval of heading values for particles in radians
- num_of_particles: Number of particles
DESCRIPTION
Create N by 3 array to store x location, y location, and heading
of each particle uniformly distributed. Take modulus of heading to ensure heading is
in range [0,2*pi).
Returns particle locations and headings
'''</span>
<span class="n">particles</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">empty</span><span class="p">((</span><span class="n">num_of_particles</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">particles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">uniform</span><span class="p">(</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">size</span> <span class="o">=</span> <span class="n">num_of_particles</span><span class="p">)</span>
<span class="n">particles</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">uniform</span><span class="p">(</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">size</span> <span class="o">=</span> <span class="n">num_of_particles</span><span class="p">)</span>
<span class="n">particles</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">uniform</span><span class="p">(</span><span class="n">hdg_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">hdg_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">size</span> <span class="o">=</span> <span class="n">num_of_particles</span><span class="p">)</span>
<span class="n">particles</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">%=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="p">.</span><span class="n">pi</span>
<span class="k">return</span> <span class="n">particles</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">control_input</span><span class="p">,</span> <span class="n">std</span><span class="p">,</span> <span class="n">dt</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span>
<span class="s">'''
Predict particles
PARAMETERS
- particles: Locations and headings of all particles
- control_input: Heading, x location step, and y location step to predict particles
- std: Standard deviation for noise added to prediction
- dt: Time step
DESCRIPTION
Predict particles forward one time step using control input u
(heading, x step, y step) and noise with standard deviation std.
'''</span>
<span class="n">num_of_particles</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">particles</span><span class="p">)</span>
<span class="c1"># update heading
</span> <span class="n">particles</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+=</span> <span class="n">control_input</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">randn</span><span class="p">(</span><span class="n">num_of_particles</span><span class="p">)</span> <span class="o">*</span> <span class="n">std</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">particles</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">%=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="p">.</span><span class="n">pi</span>
<span class="c1"># calcualte change in x and y directions
</span> <span class="n">xdist</span> <span class="o">=</span> <span class="p">(</span><span class="n">control_input</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">dt</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">randn</span><span class="p">(</span><span class="n">num_of_particles</span><span class="p">)</span> <span class="o">*</span> <span class="n">std</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">ydist</span> <span class="o">=</span> <span class="p">(</span><span class="n">control_input</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">dt</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">randn</span><span class="p">(</span><span class="n">num_of_particles</span><span class="p">)</span> <span class="o">*</span> <span class="n">std</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="c1"># add changes to current x and y locations
</span> <span class="n">particles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="n">xdist</span>
<span class="n">particles</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="n">ydist</span>
<span class="c1"># uncomment below when u = (heading, velocity) to predict
</span> <span class="c1"># particles using a velocity rather than an x and y change.
</span> <span class="c1">#
</span> <span class="c1">#dist = (u[1] * dt) + (randn(num_of_particles) * std[1])
</span> <span class="c1">#particles[:, 0] += np.cos(particles[:,2])*dist
</span> <span class="c1">#particles[:, 1] += np.sin(particles[:,2])*dist
</span>
<span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">observation</span><span class="p">,</span> <span class="n">sensor_std</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">):</span>
<span class="s">'''
Update particle weights
PARAMETERS
- particles: Locations and headings of all particles
- weights: Weights of all particles
- observation: Observation of distances between robot and all landmarks
- sensor_std: Standard deviation for error in sensor for observation
- landmarks: Locations of all landmarks
DESCRIPTION
Set all weights to 1. For each landmark, calculate the distance between
the particles and that landmark. Then, for a normal distribution with mean
= distance and std = sensor_std, calculate the pdf for a measurement of observation.
Multiply weight by pdf. If observation is close to distance, then the
particle is similar to the true state of the model so the pdf is close
to one so the weight stays near one. If observation is far from distance,
then the particle is not similar to the true state of the model so the
pdf is close to zero so the weight becomes very small.
The distance variable depends on the particles while the z parameter depends
on the robot.
'''</span>
<span class="n">weights</span><span class="p">.</span><span class="n">fill</span><span class="p">(</span><span class="mf">1.</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">landmark</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">landmarks</span><span class="p">):</span>
<span class="n">distance</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">linalg</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="n">particles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">landmark</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">weights</span> <span class="o">*=</span> <span class="n">scipy</span><span class="p">.</span><span class="n">stats</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="n">distance</span><span class="p">,</span> <span class="n">sensor_std</span><span class="p">).</span><span class="n">pdf</span><span class="p">(</span><span class="n">observation</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">weights</span> <span class="o">+=</span> <span class="mf">1.e-300</span> <span class="c1"># avoid round-off to zero
</span> <span class="n">weights</span> <span class="o">/=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="c1"># normalize
</span>
<span class="k">def</span> <span class="nf">effective_n</span><span class="p">(</span><span class="n">weights</span><span class="p">):</span>
<span class="s">'''
Calculate effective N
PARAMETERS
- weights: Weights of all particles
DESCRIPTION
Calculates effective N, an approximation for the number of particles
contributing meaningful information determined by their weight. When
this number is small, the particle filter should resample the particles
to redistribute the weights among more particles.
Returns effective N.
'''</span>
<span class="k">return</span> <span class="mf">1.</span> <span class="o">/</span> <span class="n">np</span><span class="p">.</span><span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">square</span><span class="p">(</span><span class="n">weights</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">resample_from_index</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">indexes</span><span class="p">):</span>
<span class="s">'''
Resample particles by index
PARAMETERS
- particles: Locations and headings of all particles
- weights: Weights of all particles
- indexes: Indexes of particles to be resampled
DESCRIPTION
Resample particles and the associated weights using indexes. Reset
weights to 1/N = 1/len(weights).
'''</span>
<span class="n">particles</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">particles</span><span class="p">[</span><span class="n">indexes</span><span class="p">]</span>
<span class="n">weights</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">weights</span><span class="p">[</span><span class="n">indexes</span><span class="p">]</span>
<span class="n">weights</span><span class="p">.</span><span class="n">fill</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">estimate</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">):</span>
<span class="s">'''
Estimate state of system
PARAMETERS:
- particles: Locations and headings of all particles
- weights: Weights of all particles
DESCRIPTION
Estimate the state of the system by calculating the mean and variance
of the weighted particles.
Returns mean and variance of the particle locations
'''</span>
<span class="n">pos</span> <span class="o">=</span> <span class="n">particles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="n">pos</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">var</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">((</span><span class="n">pos</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span>
<span class="k">def</span> <span class="nf">run_textbook_particle_filter</span><span class="p">(</span><span class="n">num_of_particles</span><span class="p">,</span>
<span class="n">num_of_iterations</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span>
<span class="n">sensor_std</span> <span class="o">=</span> <span class="p">.</span><span class="mi">1</span><span class="p">,</span>
<span class="n">do_plot</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span>
<span class="n">plot_particles</span> <span class="o">=</span> <span class="bp">False</span><span class="p">):</span>
<span class="s">'''
Run the particle filter
PARAMETERS
- num_of_particles: Number of particles
- num_of_iterations: Number of iterations for particle filter
- sensor_std: Standard deviation for error of sensor
- do_plot: Boolean variable to plot particle filter results
- plot_particles: Boolean variable to plot each particle
DESCRIPTION
Set locations for landmarks, particle locations, and particle weights.
Plot individual particles initially. Set robot location. For each
iteration, increment the robot location, take observation with noise
for distance between robot and landmarks, predict particles forward,
update particle weights. If effective N is small enough, resample
particles. Calculate estimates and save the particle mean. Plot
results and print final error statistics.
'''</span>
<span class="n">landmarks</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">([[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">15</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">36</span><span class="p">]])</span>
<span class="n">num_of_landmarks</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">figure</span><span class="p">()</span>
<span class="c1"># create particles
</span> <span class="n">particles</span> <span class="o">=</span> <span class="n">create_uniform_particles</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">6.28</span><span class="p">),</span> <span class="n">num_of_particles</span><span class="p">)</span>
<span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_of_particles</span><span class="p">)</span>
<span class="c1"># plot particles
</span> <span class="k">if</span> <span class="n">plot_particles</span><span class="p">:</span>
<span class="n">alpha</span> <span class="o">=</span> <span class="p">.</span><span class="mi">20</span>
<span class="k">if</span> <span class="n">num_of_particles</span> <span class="o">></span> <span class="mi">5000</span><span class="p">:</span>
<span class="n">alpha</span> <span class="o">*=</span> <span class="n">np</span><span class="p">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">5000</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="p">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">num_of_particles</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">particles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">particles</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'g'</span><span class="p">)</span>
<span class="n">means</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">robot_position</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">])</span>
<span class="c1"># loop through iterations
</span> <span class="k">for</span> <span class="n">iteration</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_of_iterations</span><span class="p">):</span>
<span class="c1"># increment robot location
</span> <span class="n">robot_position</span> <span class="o">+=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># distance from robot to each landmark
</span> <span class="n">observation</span> <span class="o">=</span> <span class="p">(</span><span class="n">norm</span><span class="p">(</span><span class="n">landmarks</span> <span class="o">-</span> <span class="n">robot_position</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span>
<span class="p">(</span><span class="n">randn</span><span class="p">(</span><span class="n">num_of_landmarks</span><span class="p">)</span> <span class="o">*</span> <span class="n">sensor_std</span><span class="p">))</span>
<span class="c1"># move diagonally forward
</span> <span class="n">predict</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">control_input</span><span class="o">=</span><span class="p">(</span><span class="mf">0.00</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">),</span> <span class="n">std</span><span class="o">=</span><span class="p">(.</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="c1"># incorporate measurements
</span> <span class="n">update</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">observation</span><span class="o">=</span><span class="n">observation</span><span class="p">,</span> <span class="n">sensor_std</span><span class="o">=</span><span class="n">sensor_std</span><span class="p">,</span>
<span class="n">landmarks</span><span class="o">=</span><span class="n">landmarks</span><span class="p">)</span>
<span class="c1"># resample if too few effective particles
</span> <span class="k">if</span> <span class="n">effective_n</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="o"><</span> <span class="n">num_of_particles</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span>
<span class="n">indexes</span> <span class="o">=</span> <span class="n">systematic_resample</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
<span class="n">resample_from_index</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">indexes</span><span class="p">)</span>
<span class="c1"># calculate and save estimates
</span> <span class="n">mean</span><span class="p">,</span> <span class="n">variance</span> <span class="o">=</span> <span class="n">estimate</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
<span class="n">means</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">mean</span><span class="p">)</span>
<span class="k">if</span> <span class="n">plot_particles</span><span class="p">:</span>
<span class="n">plt</span><span class="p">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">particles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">particles</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
<span class="n">color</span><span class="o">=</span><span class="s">'k'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s">','</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">p1</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">robot_position</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">robot_position</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s">'+'</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s">'k'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">180</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">p2</span> <span class="o">=</span> <span class="n">plt</span><span class="p">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">mean</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mean</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s">'s'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'r'</span><span class="p">)</span>
<span class="n">means</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">means</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">legend</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">],</span> <span class="p">[</span><span class="s">'Actual'</span><span class="p">,</span> <span class="s">'PF'</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">numpoints</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">'final position error, variance:</span><span class="se">\n\t</span><span class="s">'</span><span class="p">,</span>
<span class="n">mean</span> <span class="o">-</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">([</span><span class="n">num_of_iterations</span><span class="p">,</span> <span class="n">num_of_iterations</span><span class="p">]),</span> <span class="n">variance</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">seed</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">run_textbook_particle_filter</span><span class="p">(</span><span class="n">num_of_particles</span><span class="o">=</span><span class="mi">5000</span><span class="p">,</span> <span class="n">plot_particles</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</code></pre></div></div>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>final position error, variance:
[ 0.09280355 -0.17494662] [0.00577109 0.01313016]
</code></pre></div></div>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_16_1.png" alt="png" /></p>
<p>It is very example specific. Hence, my first task was to rewrite the particle filter for a more general model. To do this, I had to determine which parts of the example particle filter were specific to the example and which were necessary for the particle filter itself. Once I detached the particle from that example, I then realised that a particle cannot work without some underlying model. So I created a model of a single object moving in a line. In this code, there are no other methods. Everything is contained within the particle filter.</p>
<p>Below is the code for a particle filter estimating the location of an object moving with linear motion.</p>
<h2 id="particle-filter-with-linear-motion">Particle Filter with linear motion</h2>
<p>(This code was adapted from Roger Labbe’s Kalman and Bayesian Filters in Python (Chapter 12 - Particle Filters) book).</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">uniform</span>
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">randn</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
<span class="k">def</span> <span class="nf">run_linear_particle_filter</span><span class="p">(</span><span class="n">number_of_particles</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span>
<span class="n">number_of_iterations</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span>
<span class="n">initial_particle_x_range</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="n">initial_particle_y_range</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="n">initial_object_location</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
<span class="n">object_location_change</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="n">sensor_std</span> <span class="o">=</span> <span class="p">.</span><span class="mi">1</span><span class="p">,</span>
<span class="n">particle_std</span> <span class="o">=</span> <span class="p">.</span><span class="mi">1</span><span class="p">):</span>
<span class="s">'''
Run particle filter created by Kevin Minors
PARAMETERS
- number_of_particles: Number of particles in filter
- number_of_iterations: Number of iterations in filter
- initial_particle_x_range: Initial x range for particles
- initial_particle_y_range: Initial y range for particles
- initial_object_location: Initial location of object
- object_location_change: Change in object location per step
- sensor_std: Standard deviation for sensor
- particle_std: Standard deviation for particles
DESCRIPTION
A particle filter created to estimate the location of
an object with linear motion. There is uncertainty in
the system behaviour when we move the particles and
there is uncertainty in the sensor when we measure
the distance between particles and the object.
'''</span>
<span class="c1"># Create an empty array for the location of each particle,
</span> <span class="c1"># storing x coordinates in column 0, y coordinates
</span> <span class="c1"># in column 1
</span> <span class="n">particle_locations</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">empty</span><span class="p">((</span><span class="n">number_of_particles</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="c1"># Distribute x and y coordinates uniformly over
</span> <span class="c1"># x range and y range
</span> <span class="n">particle_locations</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">uniform</span><span class="p">(</span><span class="n">initial_particle_x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">initial_particle_x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">size</span> <span class="o">=</span> <span class="n">number_of_particles</span><span class="p">)</span>
<span class="n">particle_locations</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">uniform</span><span class="p">(</span><span class="n">initial_particle_y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">initial_particle_y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">size</span> <span class="o">=</span> <span class="n">number_of_particles</span><span class="p">)</span>
<span class="c1"># Set particle weights
</span> <span class="n">particle_weights</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">)</span>
<span class="c1"># Create array for the mean of the particle locations
</span> <span class="n">object_location</span> <span class="o">=</span> <span class="n">initial_object_location</span>
<span class="n">particle_means</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">number_of_iterations</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="n">object_locations</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">number_of_iterations</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="n">particle_means</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span> <span class="o">=</span> <span class="p">[</span><span class="n">object_location</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">object_location</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="n">object_locations</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span> <span class="o">=</span> <span class="p">[</span><span class="n">object_location</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">object_location</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="c1"># Loop through time steps
</span> <span class="k">for</span> <span class="n">iteration</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_iterations</span><span class="p">):</span>
<span class="c1"># Move object
</span> <span class="n">object_location</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="n">object_location_change</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">object_location</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="n">object_location_change</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">object_locations</span><span class="p">[</span><span class="n">iteration</span><span class="o">+</span><span class="mi">1</span><span class="p">,:]</span> <span class="o">=</span> <span class="p">[</span><span class="n">object_location</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">object_location</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="c1"># Move particles according to object
</span> <span class="c1"># dynamics
</span> <span class="n">particle_locations</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="n">object_location_change</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="o">+</span> <span class="n">randn</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">)</span><span class="o">*</span><span class="n">particle_std</span>
<span class="n">particle_locations</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="n">object_location_change</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="o">+</span> <span class="n">randn</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">)</span><span class="o">*</span><span class="n">particle_std</span>
<span class="c1"># Update particle weights according to
</span> <span class="c1"># measurements
</span> <span class="n">distance_between_particles_object</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span>
<span class="n">np</span><span class="p">.</span><span class="n">linalg</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="n">particle_locations</span> <span class="o">-</span> <span class="n">object_location</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="o">+</span> <span class="n">randn</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">)</span><span class="o">*</span><span class="n">sensor_std</span><span class="p">)</span>
<span class="n">particle_weights</span><span class="p">.</span><span class="n">fill</span><span class="p">(</span><span class="mf">1.</span><span class="p">)</span>
<span class="n">particle_weights</span> <span class="o">*=</span> <span class="mf">1.</span> <span class="o">/</span> <span class="p">(</span><span class="n">distance_between_particles_object</span> <span class="o">+</span> <span class="mf">1.e-300</span><span class="p">)</span>
<span class="n">particle_weights</span> <span class="o">/=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">particle_weights</span><span class="p">)</span>
<span class="c1"># Resample particles based on weights
</span> <span class="c1"># Possibly add in if statement to only resample when weights are small enough
</span> <span class="n">random_offset</span> <span class="o">=</span> <span class="n">random</span><span class="p">.</span><span class="n">random</span><span class="p">()</span>
<span class="n">random_positions</span> <span class="o">=</span> <span class="p">[(</span><span class="n">x</span> <span class="o">+</span> <span class="n">random_offset</span><span class="p">)</span><span class="o">/</span><span class="n">number_of_particles</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">))]</span>
<span class="c1"># Calculate where in the cumsum these
</span> <span class="c1"># random positions lie and pick that
</span> <span class="c1"># particle to be sampled.
</span> <span class="n">cumulative_sum</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">particle_weights</span><span class="p">)</span><span class="o">/</span><span class="nb">sum</span><span class="p">(</span><span class="n">particle_weights</span><span class="p">)</span>
<span class="n">particle_choices</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">,</span> <span class="s">'i'</span><span class="p">)</span>
<span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
<span class="k">while</span> <span class="n">i</span> <span class="o"><</span> <span class="n">number_of_particles</span><span class="p">:</span>
<span class="k">if</span> <span class="n">random_positions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o"><</span> <span class="n">cumulative_sum</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
<span class="n">particle_choices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">j</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">j</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">particle_locations</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">particle_locations</span><span class="p">[</span><span class="n">particle_choices</span><span class="p">]</span>
<span class="n">particle_weights</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">particle_weights</span><span class="p">[</span><span class="n">particle_choices</span><span class="p">]</span>
<span class="c1"># Calculate mean and variance of
</span> <span class="c1"># particles
</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="n">particle_locations</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">particle_weights</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">var</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">((</span><span class="n">particle_locations</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">particle_weights</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># Record particle locations
</span> <span class="n">particle_means</span><span class="p">[</span><span class="n">iteration</span><span class="o">+</span><span class="mi">1</span><span class="p">,:]</span> <span class="o">=</span> <span class="p">[</span><span class="n">mean</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">mean</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="c1"># Plot particle means and object locations
</span> <span class="n">plt</span><span class="p">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">object_locations</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">object_locations</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span> <span class="o">=</span> <span class="s">'k'</span><span class="p">,</span> <span class="n">marker</span> <span class="o">=</span> <span class="s">'+'</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">particle_means</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">particle_means</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span> <span class="o">=</span> <span class="s">'r'</span><span class="p">,</span> <span class="n">marker</span> <span class="o">=</span> <span class="s">'o'</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">legend</span><span class="p">([</span><span class="s">'Object'</span><span class="p">,</span> <span class="s">'Particle Mean'</span><span class="p">])</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Final location error = '</span><span class="p">,</span> <span class="n">np</span><span class="p">.</span><span class="n">linalg</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="n">mean</span> <span class="o">-</span> <span class="n">object_location</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Final variance in particles = '</span><span class="p">,</span> <span class="n">var</span><span class="p">)</span>
<span class="n">run_linear_particle_filter</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_20_0.png" alt="png" /></p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>Final location error = 0.047750971613814246
Final variance in particles = [1.81753553e-27 9.89547120e-27]
</code></pre></div></div>
<p>Detangling the particle filter from the example proved more difficult than expected. Many of the problems I faced initially doing this was not from the particle filter itself but from the underlying model and the communication between the two.</p>
<p>Once I finished this stage of the particle filter and model, I then upgraded the underlying model to include nonlinear movement. These updates made the underlying model closer to the actual model we were working with. Then, we wanted to take it one step further so that the particle filter only interacts with the underlying model through a ‘step’ function that increments the underlying model forward in time. When this step function is called on the base model or a particle, it increments them by one time step. This way, there is very little interaction between the underlying model and the particle filter so the particle filter can easily be attached to a different underlying model. The only requirement is that the model has a ‘step’ function.</p>
<p>Below is the code for a particle filter estimating the location of an object moving with a step function and matrix transition function.</p>
<h2 id="particle-filter-with-a-step-function-and-matrix-transition">Particle Filter with a step function and matrix transition</h2>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">uniform</span>
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">randn</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
<span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="n">state</span><span class="p">):</span>
<span class="s">'''
Step function
PARAMETERS
- model_state: The state of the system to be incremented by one time step
DESCRIPTION
A step function to increment the state by one time step. Depending on the
dimension of the system, the function increments using different matrices.
The state is multiplied by the transition matrix and then some randomness
is added.
Returns the new state of the system.
'''</span>
<span class="n">dimension</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dimension</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="n">state_transition</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.3</span><span class="p">,</span><span class="o">-</span><span class="mf">0.4</span><span class="p">,</span><span class="mf">0.1</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.6</span><span class="p">,.</span><span class="mi">8</span><span class="p">,</span><span class="o">-</span><span class="p">.</span><span class="mi">6</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.7</span><span class="p">,</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">]]</span>
<span class="k">elif</span> <span class="n">dimension</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">state_transition</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,.</span><span class="mi">5</span><span class="p">],</span>
<span class="p">[.</span><span class="mi">5</span><span class="p">,</span><span class="mi">1</span><span class="p">]]</span>
<span class="n">state_std</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">state_noise</span> <span class="o">=</span> <span class="n">randn</span><span class="p">(</span><span class="n">dimension</span><span class="p">)</span>
<span class="k">return</span> <span class="n">np</span><span class="p">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">state_transition</span><span class="p">,</span> <span class="n">state</span><span class="p">)</span> <span class="o">+</span> <span class="n">state_noise</span><span class="o">*</span><span class="n">state_std</span>
<span class="k">def</span> <span class="nf">run_step_particle_filter</span><span class="p">(</span><span class="n">initial_model_state</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="n">number_of_particles</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span>
<span class="n">number_of_iterations</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span>
<span class="n">initial_particle_distribution_std</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">particle_model_behaviour_std</span> <span class="o">=</span> <span class="p">.</span><span class="mi">1</span><span class="p">):</span>
<span class="s">'''
Run particle filter with step function
PARAMETERS
- initial_model_state: Initial state of the model
DESCRIPTION
A particle filter created to estimate the location of
an object with linear or nonlinear motion. There is uncertainty in
the system behaviour when we move the particles and
there is uncertainty in the sensor when we measure
the distance between particles and the object.
'''</span>
<span class="n">dimensions</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">initial_model_state</span><span class="p">)</span>
<span class="n">model_state</span> <span class="o">=</span> <span class="n">initial_model_state</span>
<span class="n">particle_weights</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">ones</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">)</span>
<span class="n">particle_locations</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">empty</span><span class="p">((</span><span class="n">number_of_particles</span><span class="p">,</span> <span class="n">dimensions</span><span class="p">))</span>
<span class="c1"># set particle locations to initial model state with noise
</span> <span class="k">for</span> <span class="n">dimension</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimensions</span><span class="p">):</span>
<span class="n">particle_locations</span><span class="p">[:,</span><span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">initial_model_state</span><span class="p">[</span><span class="n">dimension</span><span class="p">]</span>
<span class="o">+</span> <span class="n">initial_particle_distribution_std</span><span class="o">*</span><span class="n">randn</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">))</span>
<span class="c1"># set mean
</span> <span class="n">particle_means</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">number_of_iterations</span><span class="p">,</span> <span class="n">dimensions</span><span class="p">))</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="n">particle_locations</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">particle_weights</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">particle_means</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">mean</span><span class="p">[:]</span>
<span class="n">plt</span><span class="p">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">model_state</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">model_state</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span> <span class="o">=</span> <span class="s">'k'</span><span class="p">,</span> <span class="n">marker</span> <span class="o">=</span> <span class="s">'+'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">iteration</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_iterations</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
<span class="c1"># step model and particles
</span> <span class="n">model_state</span> <span class="o">=</span> <span class="n">step</span><span class="p">(</span><span class="n">model_state</span><span class="p">)</span>
<span class="k">for</span> <span class="n">particle</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">):</span>
<span class="n">particle_locations</span><span class="p">[</span><span class="n">particle</span><span class="p">,:]</span> <span class="o">=</span> <span class="p">(</span><span class="n">step</span><span class="p">(</span><span class="n">particle_locations</span><span class="p">[</span><span class="n">particle</span><span class="p">,:])</span>
<span class="o">+</span> <span class="n">randn</span><span class="p">(</span><span class="n">dimensions</span><span class="p">)</span><span class="o">*</span><span class="n">particle_model_behaviour_std</span><span class="p">)</span>
<span class="c1"># calculate distance between state and particles and then weights
</span> <span class="n">distance</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">linalg</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="n">particle_locations</span> <span class="o">-</span> <span class="n">model_state</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">particle_weights</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">distance</span> <span class="o">+</span> <span class="mf">1e-99</span><span class="p">)</span>
<span class="c1"># resample particles using systematic resampling
</span> <span class="n">random_offset</span> <span class="o">=</span> <span class="n">random</span><span class="p">.</span><span class="n">random</span><span class="p">()</span>
<span class="n">random_partition</span> <span class="o">=</span> <span class="p">[</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">random_offset</span><span class="p">)</span> <span class="o">/</span> <span class="n">number_of_particles</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">))]</span>
<span class="n">cumulative_sum</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">particle_weights</span><span class="p">)</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">particle_weights</span><span class="p">)</span>
<span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
<span class="n">particle_indexes</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">number_of_particles</span><span class="p">,</span> <span class="s">'i'</span><span class="p">)</span>
<span class="k">while</span> <span class="n">i</span> <span class="o"><</span> <span class="n">number_of_particles</span><span class="p">:</span>
<span class="k">if</span> <span class="n">random_partition</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o"><</span> <span class="n">cumulative_sum</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
<span class="n">particle_indexes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">j</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">j</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">particle_locations</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">particle_locations</span><span class="p">[</span><span class="n">particle_indexes</span><span class="p">]</span>
<span class="n">particle_weights</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">particle_weights</span><span class="p">[</span><span class="n">particle_indexes</span><span class="p">]</span>
<span class="c1"># save mean and variance of resampled particles
</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="n">particle_locations</span><span class="p">,</span><span class="n">weights</span><span class="o">=</span><span class="n">particle_weights</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">variance</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">((</span><span class="n">particle_locations</span><span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">weights</span><span class="o">=</span><span class="n">particle_weights</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">particle_means</span><span class="p">[</span><span class="n">iteration</span><span class="o">+</span><span class="mi">1</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">mean</span><span class="p">[:]</span>
<span class="n">plt</span><span class="p">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">model_state</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">model_state</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span> <span class="o">=</span> <span class="s">'k'</span><span class="p">,</span> <span class="n">marker</span> <span class="o">=</span> <span class="s">'+'</span><span class="p">)</span>
<span class="c1"># Plot everything
</span> <span class="n">plt</span><span class="p">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">particle_means</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">particle_means</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span> <span class="o">=</span> <span class="s">'r'</span><span class="p">,</span> <span class="n">marker</span> <span class="o">=</span> <span class="s">'o'</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">gca</span><span class="p">().</span><span class="n">set_aspect</span><span class="p">(</span><span class="s">'equal'</span><span class="p">,</span> <span class="s">'datalim'</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">gcf</span><span class="p">().</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Final location error = '</span><span class="p">,</span> <span class="n">np</span><span class="p">.</span><span class="n">linalg</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="n">mean</span> <span class="o">-</span> <span class="n">model_state</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Final std in particle location = '</span><span class="p">,</span> <span class="n">variance</span><span class="o">**</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">run_step_particle_filter</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_23_0.png" alt="png" /></p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>Final location error = 0.1053703991747064
Final std in particle location = [0.35683619 0.43830442]
</code></pre></div></div>
<h2 id="rewriting-the-particle-filter-for-stationsim">Rewriting the particle filter for StationSim</h2>
<p>While I was doing this initial work, another colleague was working on creating a Python version of StationSim [A, West et al. (2019) DUST: sspmm Accessed ONLINE 27/03/2019: http://github.com/urban-analytics/dust]. StationSim is an agent based model for people walking through a corridor from left to right. There are three doors on the left that people enter from and two doors on the right that people exit through. In the model, due to the behaviour and number of agents, crowding occurs.</p>
<p>Below is a picture of StationSim from its Java implementation.</p>
<p><img src="attachment:StationSim.PNG" alt="StationSim.PNG" /></p>
<p>Here is some code to run the model, and figures illustrating how the crowd in the model develops. The code is available in full from the accompanying source file ‘<a href="./StationSim.py">StationSim.py</a>’. (<em>The StationSim model is still under development, so for the most up to date version have a look in its main <a href="https://github.com/Urban-Analytics/dust/tree/master/Projects/StationSim-py">development page</a></em>).</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">StationSim</span>
<span class="c1"># These are the parameters to configure the model
</span><span class="n">model_params</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">'width'</span><span class="p">:</span> <span class="mi">200</span><span class="p">,</span>
<span class="s">'height'</span><span class="p">:</span> <span class="mi">100</span><span class="p">,</span>
<span class="s">'pop_total'</span><span class="p">:</span><span class="mi">700</span><span class="p">,</span>
<span class="s">'entrances'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
<span class="s">'entrance_space'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
<span class="s">'entrance_speed'</span><span class="p">:</span> <span class="p">.</span><span class="mi">1</span><span class="p">,</span>
<span class="s">'exits'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
<span class="s">'exit_space'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s">'speed_min'</span><span class="p">:</span> <span class="p">.</span><span class="mi">1</span><span class="p">,</span>
<span class="s">'speed_desire_mean'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s">'speed_desire_std'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s">'separation'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
<span class="s">'batch_iterations'</span><span class="p">:</span> <span class="mi">4000</span><span class="p">,</span>
<span class="s">'do_save'</span><span class="p">:</span> <span class="bp">True</span><span class="p">,</span>
<span class="s">'do_ani'</span><span class="p">:</span> <span class="bp">True</span><span class="p">,</span>
<span class="p">}</span>
<span class="c1"># Now run the model using these parameters:
</span><span class="n">StationSim</span><span class="p">.</span><span class="n">Model</span><span class="p">(</span><span class="n">model_params</span><span class="p">).</span><span class="n">batch</span><span class="p">()</span>
</code></pre></div></div>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_0.png" alt="png" /></p>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_1.png" alt="png" /></p>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_2.png" alt="png" /></p>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_3.png" alt="png" /></p>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_4.png" alt="png" /></p>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_5.png" alt="png" /></p>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_6.png" alt="png" /></p>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_7.png" alt="png" /></p>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_8.png" alt="png" /></p>
<p><img src="https://urban-analytics.github.io/dust/figures/km_blog_post/Full_Project_Blog_post_28_9.png" alt="png" /></p>
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<p>Our goal is to use a particle filter on StationSim for data assimilation. Once StationSim was fully coded in Python, I added the particle filter to it using the step function in StationsSim. This took some time as we had to ensure the model and filter were communicating correctly and doing what we expected. This involved a lot of validation.</p>
<p>Below is the code for the particle filter for StationSim. It doesn’t work in the Jupyter Notebook as it needs to use multiprocessing (each particle runs as a separate process) but that wasn’t supported at the time of writing)</p>
<h2 id="particle-filter-for-stationsim">Particle filter for StationSim</h2>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span>
<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">cKDTree</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="kn">import</span> <span class="n">Pool</span>
<span class="kn">import</span> <span class="nn">multiprocessing</span>
<span class="kn">from</span> <span class="nn">StationSim</span> <span class="kn">import</span> <span class="n">Model</span>
<span class="c1">#%% PARTICLE FILTER
</span>
<span class="c1"># Multiprocessing Methods
</span><span class="k">def</span> <span class="nf">initial_state</span><span class="p">(</span><span class="n">particle</span><span class="p">,</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Set the state of the particles to the state of the
base model.
"""</span>
<span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[</span><span class="n">particle</span><span class="p">,:]</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents2state</span><span class="p">()</span>
<span class="k">return</span> <span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[</span><span class="n">particle</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">assign_agents</span><span class="p">(</span><span class="n">particle</span><span class="p">,</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Assign the state of the particles to the
locations of the agents.
"""</span>
<span class="bp">self</span><span class="p">.</span><span class="n">models</span><span class="p">[</span><span class="n">particle</span><span class="p">].</span><span class="n">state2agents</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[</span><span class="n">particle</span><span class="p">])</span>
<span class="k">return</span> <span class="bp">self</span><span class="p">.</span><span class="n">models</span><span class="p">[</span><span class="n">particle</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">step_particles</span><span class="p">(</span><span class="n">particle</span><span class="p">,</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"""
Step each particle model, assign the locations of the
agents to the particle state with some noise, and
then use the new particle state to set the location
of the agents.
"""</span>
<span class="bp">self</span><span class="p">.</span><span class="n">models</span><span class="p">[</span><span class="n">particle</span><span class="p">].</span><span class="n">step</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[</span><span class="n">particle</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">models</span><span class="p">[</span><span class="n">particle</span><span class="p">].</span><span class="n">agents2state</span><span class="p">()</span>
<span class="o">+</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">particle_std</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
<span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[</span><span class="n">particle</span><span class="p">].</span><span class="n">shape</span><span class="p">))</span>
<span class="bp">self</span><span class="p">.</span><span class="n">models</span><span class="p">[</span><span class="n">particle</span><span class="p">].</span><span class="n">state2agents</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[</span><span class="n">particle</span><span class="p">])</span>
<span class="k">return</span> <span class="bp">self</span><span class="p">.</span><span class="n">models</span><span class="p">[</span><span class="n">particle</span><span class="p">],</span> <span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[</span><span class="n">particle</span><span class="p">]</span>
<span class="k">class</span> <span class="nc">ParticleFilter</span><span class="p">:</span>
<span class="s">'''
A particle filter to model the dynamics of the
state of the model as it develops in time.
'''</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Model</span><span class="p">,</span> <span class="n">model_params</span><span class="p">,</span> <span class="n">filter_params</span><span class="p">):</span>
<span class="s">'''
Initialise Particle Filter
PARAMETERS
- number_of_particles: The number of particles used to simulate the model
- number_of_iterations: The number of iterations to run the model/particle filter
- resample_window: The number of iterations between resampling particles
- agents_to_visualise: The number of agents to plot particles for
- particle_std: The standard deviation of the noise added to particle states
- model_std: The standard deviation of the noise added to model observations
- do_save: Boolean to determine if data should be saved and stats printed
- do_ani: Boolean to determine if particle filter data should be animated
and displayed
DESCRIPTION
Firstly, set all attributes using filter parameters. Set time and
initialise base model using model parameters. Initialise particle
models using a deepcopy of base model. Determine particle filter
dimensions, initialise all remaining arrays, and set initial
particle states to the base model state using multiprocessing.
'''</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">filter_params</span><span class="p">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">time</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="p">.</span><span class="n">number_of_iterations</span> <span class="o">=</span> <span class="n">model_params</span><span class="p">[</span><span class="s">'batch_iterations'</span><span class="p">]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">base_model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">model_params</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">models</span> <span class="o">=</span> <span class="nb">list</span><span class="p">([</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">)])</span>
<span class="bp">self</span><span class="p">.</span><span class="n">dimensions</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents2state</span><span class="p">())</span>
<span class="bp">self</span><span class="p">.</span><span class="n">states</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">dimensions</span><span class="p">))</span>
<span class="bp">self</span><span class="p">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">indexes</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">,</span> <span class="s">'i'</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">do_save</span><span class="p">:</span>
<span class="bp">self</span><span class="p">.</span><span class="n">active_agents</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">means</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">mean_errors</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">variances</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">unique_particles</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">states</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">(</span><span class="n">pool</span><span class="p">.</span><span class="n">starmap</span><span class="p">(</span><span class="n">initial_state</span><span class="p">,</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">),[</span><span class="bp">self</span><span class="p">]</span><span class="o">*</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">))))</span>
<span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">'''
Step Particle Filter
DESCRIPTION
Loop through process. Predict the base model and particles
forward. If the resample window has been reached,
reweight particles based on distance to base model and resample
particles choosing particles with higher weights. Then save
and animate the data. When done, plot save figures.
'''</span>
<span class="k">while</span> <span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">time</span> <span class="o"><</span> <span class="bp">self</span><span class="p">.</span><span class="n">number_of_iterations</span><span class="p">)</span> <span class="o">&</span> <span class="nb">any</span><span class="p">([</span><span class="n">agent</span><span class="p">.</span><span class="n">active</span> <span class="o">!=</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">agent</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents</span><span class="p">]):</span>
<span class="bp">self</span><span class="p">.</span><span class="n">time</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c1">#if any([agent.active != 2 for agent in self.base_model.agents]):
# print(self.time/self.number_of_iterations)
</span> <span class="c1"># print(self.number_of_iterations)
</span> <span class="bp">self</span><span class="p">.</span><span class="n">predict</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">time</span> <span class="o">%</span> <span class="bp">self</span><span class="p">.</span><span class="n">resample_window</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="p">.</span><span class="n">reweight</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">resample</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">do_save</span><span class="p">:</span>
<span class="bp">self</span><span class="p">.</span><span class="n">save</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">do_ani</span><span class="p">:</span>
<span class="bp">self</span><span class="p">.</span><span class="n">ani</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">plot_save</span><span class="p">:</span>
<span class="bp">self</span><span class="p">.</span><span class="n">p_save</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">mean_errors</span><span class="p">),</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">mean_errors</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">variances</span><span class="p">),</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">variances</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">'''
Predict
DESCRIPTION
Increment time. Step the base model. Set self as a constant
in step_particles and then use a multiprocessing method to step
particle models, set the particle states as the agent
locations with some added noise, and reassign the
locations of the particle agents using the new particle
states. We extract the models and states from the stepped
particles variable.
'''</span>
<span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">stepped_particles</span> <span class="o">=</span> <span class="n">pool</span><span class="p">.</span><span class="n">starmap</span><span class="p">(</span><span class="n">step_particles</span><span class="p">,</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">),[</span><span class="bp">self</span><span class="p">]</span><span class="o">*</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">)))</span>
<span class="bp">self</span><span class="p">.</span><span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="n">stepped_particles</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stepped_particles</span><span class="p">))]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">states</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">([</span><span class="n">stepped_particles</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stepped_particles</span><span class="p">))])</span>
<span class="k">return</span>
<span class="k">def</span> <span class="nf">reweight</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">'''
Reweight
DESCRIPTION
Add noise to the base model state to get a measured state. Calculate
the distance between the particle states and the measured base model
state and then calculate the new particle weights as 1/distance.
Add a small term to avoid dividing by 0. Normalise the weights.
'''</span>
<span class="n">measured_state</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents2state</span><span class="p">()</span>
<span class="o">+</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="p">.</span><span class="n">model_std</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">.</span><span class="n">shape</span><span class="p">))</span>
<span class="n">distance</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">linalg</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">states</span> <span class="o">-</span> <span class="n">measured_state</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">weights</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">distance</span> <span class="o">+</span> <span class="mf">1e-99</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
<span class="bp">self</span><span class="p">.</span><span class="n">weights</span> <span class="o">/=</span> <span class="n">np</span><span class="p">.</span><span class="nb">sum</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">weights</span><span class="p">)</span>
<span class="k">return</span>
<span class="k">def</span> <span class="nf">resample</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">'''
Resample
DESCRIPTION
Calculate a random partition of (0,1) and then
take the cumulative sum of the particle weights.
Carry out a systematic resample of particles.
Set the new particle states and weights and then
update agent locations in particle models using
multiprocessing methods.
'''</span>
<span class="n">offset_partition</span> <span class="o">=</span> <span class="p">((</span><span class="n">np</span><span class="p">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">)</span>
<span class="o">+</span> <span class="n">np</span><span class="p">.</span><span class="n">random</span><span class="p">.</span><span class="n">uniform</span><span class="p">())</span> <span class="o">/</span> <span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">)</span>
<span class="n">cumsum</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">cumsum</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">weights</span><span class="p">)</span>
<span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
<span class="k">while</span> <span class="n">i</span> <span class="o"><</span> <span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">:</span>
<span class="k">if</span> <span class="n">offset_partition</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o"><</span> <span class="n">cumsum</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
<span class="bp">self</span><span class="p">.</span><span class="n">indexes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">j</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">j</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[:]</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[</span><span class="bp">self</span><span class="p">.</span><span class="n">indexes</span><span class="p">]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">weights</span><span class="p">[:]</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">weights</span><span class="p">[</span><span class="bp">self</span><span class="p">.</span><span class="n">indexes</span><span class="p">]</span>
<span class="bp">self</span><span class="p">.</span><span class="n">unique_particles</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span>
<span class="bp">self</span><span class="p">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">pool</span><span class="p">.</span><span class="n">starmap</span><span class="p">(</span><span class="n">assign_agents</span><span class="p">,</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">),[</span><span class="bp">self</span><span class="p">]</span><span class="o">*</span><span class="bp">self</span><span class="p">.</span><span class="n">number_of_particles</span><span class="p">)))</span>
<span class="k">return</span>
<span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">'''
Save
DESCRIPTION
Calculate number of active agents, mean, and variance
of particles and calculate mean error between the mean
and the true base model state. Plot active agents,mean
error and mean variance.
'''</span>
<span class="bp">self</span><span class="p">.</span><span class="n">active_agents</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="n">agent</span><span class="p">.</span><span class="n">active</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">agent</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents</span><span class="p">]))</span>
<span class="n">active_states</span> <span class="o">=</span> <span class="p">[</span><span class="n">agent</span><span class="p">.</span><span class="n">active</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">agent</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">)]</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">active_states</span><span class="p">):</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[:,</span><span class="n">active_states</span><span class="p">],</span> <span class="n">weights</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">weights</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">variance</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">((</span><span class="bp">self</span><span class="p">.</span><span class="n">states</span><span class="p">[:,</span><span class="n">active_states</span><span class="p">]</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="bp">self</span><span class="p">.</span><span class="n">weights</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">means</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">mean</span><span class="p">)</span>
<span class="bp">self</span><span class="p">.</span><span class="n">variances</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="n">variance</span><span class="p">))</span>
<span class="n">truth_state</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents2state</span><span class="p">()</span>
<span class="bp">self</span><span class="p">.</span><span class="n">mean_errors</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">linalg</span><span class="p">.</span><span class="n">norm</span><span class="p">(</span><span class="n">mean</span> <span class="o">-</span> <span class="n">truth_state</span><span class="p">[</span><span class="n">active_states</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="k">return</span>
<span class="k">def</span> <span class="nf">p_save</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">'''
Plot Save
DESCRIPTION
Plot active agents, mean error and mean variance.
'''</span>
<span class="n">plt</span><span class="p">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">active_agents</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">'Active agents'</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">mean_errors</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">'Mean Error'</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">variances</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">'Mean Variance'</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">plt</span><span class="p">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">unique_particles</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">'Unique Particles'</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Max mean error = '</span><span class="p">,</span><span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">mean_errors</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Average mean error = '</span><span class="p">,</span><span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">mean_errors</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Max mean variance = '</span><span class="p">,</span><span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">variances</span><span class="p">[</span><span class="mi">2</span><span class="p">:]))</span>
<span class="k">print</span><span class="p">(</span><span class="s">'Average mean variance = '</span><span class="p">,</span><span class="n">np</span><span class="p">.</span><span class="n">average</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">variances</span><span class="p">[</span><span class="mi">2</span><span class="p">:]))</span>
<span class="k">def</span> <span class="nf">ani</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">'''
Animate
DESCRIPTION
Plot the base model state and some of the
particles. Only do this if there is at least 1 active
agent in the base model. We adjust the markersizes of
each particle to represent the weight of that particle.
We then plot some of the agent locations in the particles
and draw lines between the particle agent location and
the agent location in the base model.
'''</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">([</span><span class="n">agent</span><span class="p">.</span><span class="n">active</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">agent</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents</span><span class="p">]):</span>
<span class="n">plt</span><span class="p">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">clf</span><span class="p">()</span>
<span class="n">markersizes</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">weights</span>
<span class="k">if</span> <span class="n">np</span><span class="p">.</span><span class="n">std</span><span class="p">(</span><span class="n">markersizes</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">markersizes</span> <span class="o">*=</span> <span class="mi">4</span> <span class="o">/</span> <span class="n">np</span><span class="p">.</span><span class="n">std</span><span class="p">(</span><span class="n">markersizes</span><span class="p">)</span> <span class="c1"># revar
</span> <span class="n">markersizes</span> <span class="o">+=</span> <span class="mi">8</span> <span class="o">-</span> <span class="n">np</span><span class="p">.</span><span class="n">mean</span><span class="p">(</span><span class="n">markersizes</span><span class="p">)</span> <span class="c1"># remean
</span>
<span class="n">particle</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">.</span><span class="n">models</span><span class="p">:</span>
<span class="n">particle</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">markersize</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">clip</span><span class="p">(</span><span class="n">markersizes</span><span class="p">[</span><span class="n">particle</span><span class="p">],</span> <span class="p">.</span><span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
<span class="k">for</span> <span class="n">agent</span> <span class="ow">in</span> <span class="n">model</span><span class="p">.</span><span class="n">agents</span><span class="p">[:</span><span class="bp">self</span><span class="p">.</span><span class="n">agents_to_visualise</span><span class="p">]:</span>
<span class="k">if</span> <span class="n">agent</span><span class="p">.</span><span class="n">active</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">unique_id</span> <span class="o">=</span> <span class="n">agent</span><span class="p">.</span><span class="n">unique_id</span>
<span class="k">if</span> <span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents</span><span class="p">[</span><span class="n">unique_id</span><span class="p">].</span><span class="n">active</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">locs</span> <span class="o">=</span> <span class="n">np</span><span class="p">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents</span><span class="p">[</span><span class="n">unique_id</span><span class="p">].</span><span class="n">location</span><span class="p">,</span> <span class="n">agent</span><span class="p">.</span><span class="n">location</span><span class="p">]).</span><span class="n">T</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="o">*</span><span class="n">locs</span><span class="p">,</span> <span class="s">'-k'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="p">.</span><span class="mi">1</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="p">.</span><span class="mi">3</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="o">*</span><span class="n">agent</span><span class="p">.</span><span class="n">location</span><span class="p">,</span> <span class="s">'or'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="p">.</span><span class="mi">3</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="n">markersize</span><span class="p">)</span>
<span class="k">for</span> <span class="n">agent</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">agents</span><span class="p">:</span>
<span class="k">if</span> <span class="n">agent</span><span class="p">.</span><span class="n">active</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">plt</span><span class="p">.</span><span class="n">plot</span><span class="p">(</span><span class="o">*</span><span class="n">agent</span><span class="p">.</span><span class="n">location</span><span class="p">,</span> <span class="s">'sk'</span><span class="p">,</span><span class="n">markersize</span> <span class="o">=</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">plt</span><span class="p">.</span><span class="n">axis</span><span class="p">(</span><span class="n">np</span><span class="p">.</span><span class="n">ravel</span><span class="p">(</span><span class="bp">self</span><span class="p">.</span><span class="n">base_model</span><span class="p">.</span><span class="n">boundaries</span><span class="p">,</span> <span class="s">'F'</span><span class="p">))</span>
<span class="n">plt</span><span class="p">.</span><span class="n">pause</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="mi">4</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">single_run_particle_numbers</span><span class="p">():</span>
<span class="n">particle_num</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">runs</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">runs</span><span class="p">):</span>
<span class="c1"># Run the particle filter
</span> <span class="n">filter_params</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">'number_of_particles'</span><span class="p">:</span> <span class="n">particle_num</span><span class="p">,</span>
<span class="s">'resample_window'</span><span class="p">:</span> <span class="mi">100</span><span class="p">,</span>
<span class="s">'agents_to_visualise'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
<span class="s">'particle_std'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s">'model_std'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s">'do_save'</span><span class="p">:</span> <span class="bp">True</span><span class="p">,</span>
<span class="s">'plot_save'</span><span class="p">:</span><span class="bp">False</span><span class="p">,</span>
<span class="s">'do_ani'</span><span class="p">:</span> <span class="bp">False</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">pf</span> <span class="o">=</span> <span class="n">ParticleFilter</span><span class="p">(</span><span class="n">Model</span><span class="p">,</span> <span class="n">model_params</span><span class="p">,</span> <span class="n">filter_params</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">particle_num</span><span class="p">,</span> <span class="n">pf</span><span class="p">.</span><span class="n">step</span><span class="p">())</span>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">'__main__'</span><span class="p">:</span>
<span class="n">__spec__</span> <span class="o">=</span> <span class="bp">None</span>
<span class="c1"># Pool object needed for multiprocessing
</span> <span class="n">pool</span> <span class="o">=</span> <span class="n">Pool</span><span class="p">(</span><span class="n">processes</span><span class="o">=</span><span class="n">multiprocessing</span><span class="p">.</span><span class="n">cpu_count</span><span class="p">())</span>
<span class="n">model_params</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">'width'</span><span class="p">:</span> <span class="mi">200</span><span class="p">,</span>
<span class="s">'height'</span><span class="p">:</span> <span class="mi">100</span><span class="p">,</span>
<span class="s">'pop_total'</span><span class="p">:</span><span class="mi">700</span><span class="p">,</span>
<span class="s">'entrances'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
<span class="s">'entrance_space'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
<span class="s">'entrance_speed'</span><span class="p">:</span> <span class="p">.</span><span class="mi">1</span><span class="p">,</span>
<span class="s">'exits'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
<span class="s">'exit_space'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s">'speed_min'</span><span class="p">:</span> <span class="p">.</span><span class="mi">1</span><span class="p">,</span>
<span class="s">'speed_desire_mean'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s">'speed_desire_std'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s">'separation'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
<span class="s">'batch_iterations'</span><span class="p">:</span> <span class="mi">4000</span><span class="p">,</span>
<span class="s">'do_save'</span><span class="p">:</span> <span class="bp">True</span><span class="p">,</span>
<span class="s">'do_ani'</span><span class="p">:</span> <span class="bp">True</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">True</span><span class="p">:</span>
<span class="n">Model</span><span class="p">(</span><span class="n">model_params</span><span class="p">).</span><span class="n">batch</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">single_run_particle_numbers</span><span class="p">()</span>
</code></pre></div></div>
<h2 id="test-particle-filter">Test particle filter</h2>
<p>Once we were happy with the way they were running, we began discussing how we could examine the randomness in the model and in the filter. In the model, the agents move randomly up or down as they try and move around the other agents in their way. In the filter, randomness is added to the state of the particles to prevent particle collapse. Particle collapse occurs when only one particle is resampled during the resample step and so all particles have the same state. This provides a very poor estimate for the state of the base model so it should be avoided. Adding randomness to the particles ensures there is some diversity so it very unlikely only one particle is resampled.</p>
<p>Below is a picture of particle collapse or particle impoverishment.</p>
<p><img src="attachment:particle%20impovershed.png" alt="particle%20impovershed.png" /></p>
<p>Randomness is also added to the observed state of the underlying model to reflect the uncertainty in real life sensors. In addition, we have parameters such as the number of particles and resample window that also affect how the particle filter behaves. While examining how these different parameters affect the filter, we realised it was taking quite a long time. We then thought to consider multiprocessing. This would allow the code to run faster as different particles can be run simultaneously rather than one at a time.</p>
<p>Below are pictures of tasks being completed on a single thread and tasks being completed using multiprocessing. We can see multiprocessing is much faster.</p>
<p><img src="attachment:single_threqad.png" alt="single_threqad.png" /></p>
<p><img src="attachment:multiprocess.png" alt="multiprocess.png" /></p>
<p>In addition, we were also looking into the literature to see what outputs are used to determine if a particle filter is behaving well. We found that the mean location of the particles, the variance of the particles, and the error between the mean location of the particles and the location of the underlying model are going metrics for the filter. One problem we then encountered was that the mean and variance was being calculated for particle agents that were no longer active in the StationSim simulation. Agents will have exited the corridor and be placed at some fixed location outside the boundary and they were still contributing to the mean and variance, which meant they were skewed. Another problem we found is that we could not do the multiprocessing we wanted to do in a Jupyter Notebook so we moved to writing python scripts directly, using the Spyder IDE.</p>
<p>We then moved the testing to the University of Leeds Advanced Resarch Computing (<a href="https://arc.leeds.ac.uk/systems/arc3/">ARC3</a>) high performance computing (HPC) environment. We started with a toy model of only 10 agents in the model to figure out how to actually use the supercomputer. We began by varying the number of particles, then the standard deviation for the particle noise, and finally the standard deviation for the observation noise. Once this was done, we scaled up the number of agents to 700 for the full model and started doing the same testing.</p>
<p>After noticing there were still some bottlenecks and locks in the multiprocessing, we updated the filter to remove these and also step the particles continuously until the next observation. In terms of the results, this changed very little but in terms of speed, this made the code run a lot faster. I did some maths to work out how far an agent could move before hitting the next agent in front of it but the solver function is really slow so we decided not to use that. We then began running experiments to plot number of agents vs number of particles as a contour plot with the height given by the error in the particle filter. We expect the find areas where the particle filter works really well (few agents, many particles) and areas where it doesn’t work well (many agents, few particles).</p>
<h3 id="paper-abstract-for-gisruk-2019">Paper abstract for GISRUK 2019</h3>
<p>http://www.newcastle.gisruk.org/</p>
<p>In recent years, cities have used the increase in data to make decisions that result in the city becoming more efficient and sustainable through automation. These cities are known as smart cities. One of the many important questions smart cities would like to answer is how do people behave? Being able to model the behaviour of a population in a city will prove very fruitful, as it will inform a variety of policy decisions. It is often very difficult to gain insight into human behaviour from a macroscopic point of view in real time.</p>
<p>One way to solve this issue is to use agent based models. These models attempt to simulate the behaviours of a population from a microscopic point of view by using simulated individuals, the ‘agents’. The behaviour of the agents can be programmed to reflect the various dynamics of a particular city. One key component of agent based models is that there is some collective behaviour forming that is not programmed into individuals, known as ‘emergence’. Agent based models are generally given some initial conditions and then they evolve forward in time independently from the real world environment they are simulating. This often results in the models diverging away from reality due to the limited accuracy of the initial conditions and model behaviour. To solve this problem, these models can use real time data from reality to update the state of the model in real time. This is known as data assimilation. New data is used to ensure the model continues to accurately reflect reality.</p>
<p>There are many ways to carry out data assimilation. One such method is known as a particle filter. Particle filters run numerous instances of the agent based model, known as ‘particles’, and allow them to develop forward in time. As new information is received about the true state of reality, the particle filter weights the particles based on how similar they are to the model state determined by the new information. Particles with a large weight are model runs that are very similar to the information received while particles with small weights are model runs that do not agree well with the new data. The particles are then resampled based on their weight, so that particles with a large weight are more likely to be chosen to be duplicated and particles with small weights are more likely to be discarded. In this way, the resampling results in particles with larger weights surviving and particles with small weights dying off. At this stage, the particles run forward in time until the next piece of information is received and the process continues.</p>
<p>This particle filter method allows the models to
run while also being able to update itself with
new data in real time, allowing the model to bet-
ter simulate reality over long periods of time.
One of the advantages of using particle filters
over other types of data assimilation is that it
can handle nonlinear dynamics, which applies
to a wide range of applications. One of the dis-
advantages is that it can be quite computation-
ally expensive as complicated models require a
significant number of particles in order to accu-
rately assimilate the new data. Problems with
particle filters include all particles collapsing to
one state due to a lack of diversity, known as
the degeneracy problem, and all particles fail-
ing to be similar to the new information due to
not enough particles being simulated.</p>
<p>The agent based model we are working on represents an environment like a corridor. In the model, agents enter the corridor from one side and walk through the corridor to exits on the other side. Just like people in real life, the agents have different speeds and different destinations. The emergence we find in this agent based model is crowding. As agents cross paths attempting to go to their exit and as slow agents block the path of fast agents, crowds form. We chose this model because it is simple enough for us to carry out data assimilation on it in a meaningful way and because it is complicated enough to have the emergent behaviour of crowding. In this case, as we do not have data from any corridor in particular, we use the model itself as our reality from which we receive new data. More details can be found on our Github page.</p>
<h2 id="training--workshops--presentations">Training / Workshops / Presentations</h2>
<p>Netlogo practicals, Intro to GIS workshop, Mason tutorial, GIS for crime data training, ONS Safe Researcher training, QGIS workshop, ITS Big Data presentation, Python workshop, Workshop in Manchester, Tableau workshop, Climate meeting. Intern presentation, Data Carpentry workshop, Presentation training, Meeting Improbable in London.</p>
Research Fellow - Simulating Urban Systems2019-03-18T00:00:00+00:00/2019/03/PDRA-SimulatingUrbanSystems<figure style="width:40%;float:right; padding: 1em;">
<img src="https://urban-analytics.github.io/dust/figures/shutterstock_615414464-small.jpg" alt="London at night with blue shiny lines" />
</figure>
<p>I’m delighted to be able to announce a new, 3 year, full time, post-doctoral position in the <a href="http://lida.leeds.ac.uk/">Leeds Institute for Data Analytics</a> (in the UK) on building agent-based models for simulating urban systems. It’s linked to the ERC-funded ‘<a href="http://dust.leeds.ac.uk/">dust</a>’ project.</p>
<p>Further details below, full details here: <a href="https://jobs.leeds.ac.uk/Vacancy.aspx?ref=ENVGE1089">https://jobs.leeds.ac.uk/Vacancy.aspx?ref=ENVGE1089</a></p>
<p>Please get in touch with me (<a href="mailto:N.S.Malleson@leeds.ac.uk">Nick Malleson</a>) if you’d like any more information, or to discuss an application.</p>
<p><strong>Are you an ambitious researcher looking for your next challenge? Do you have a background in computer simulation, statistics, and data analytics? Do you want to further your career in one of the UK’s leading research intensive Universities?</strong></p>
<p>You will work on a new project, funded by the European Research Council, called <a href="http://dust.leeds.ac.uk/">Data Assimilation for Agent-Based Models (DUST)</a>. The utilmate aim of the project is to develop a comprehensive simulation that can be used to model the current state of an urban area and provide valuable information to policy makers. Agent-based modelling is an ideal methodology for this type of simulation but one that suffers from a serious drawback: models are not able to incorporate up-to-date data to reduce uncertainty. There is a wealth of new data being generated in ‘smart’ cities that could inform a model of urban dynamics (e.g. from social media contributions, mobile telephone use, public transport records, vehicle traffic counters, etc.) but we lack the tools to incorporate these streams of data into agent-based models. Instead, models are typically initialised with historical data and therefore their estimates of the current state of a system diverge rapidly from reality.</p>
<p>The research team is lead by Dr Nicolas Malleson and will be located within the Leeds Institute for Data Analytics (LIDA), which has been established with more than £20 million of funding from the University and four major research councils. The city of Leeds is already recognised as a hub for big data analytics in business, health care and academic research. In addition, the University has also recently become a partner in the <a href="https://www.turing.ac.uk/">Alan Turing Institute</a>, which is the UK’s national institute for data science, and this new collaboration offers exciting opportunites to for LIDA researchers to engage with scientific leaders from a range of fields.</p>
Urban Analytics Blueprint - Newcastle2019-03-14T00:00:00+00:00/2019/03/Newcastle_Urban_Analytics<figure style="width:30%;float:right;">
<img src="https://urban-analytics.github.io/dust/figures/LOGO_TURING.png" alt="The ALan Turing Institute logo" />
</figure>
<p>There is an interesting upcoming event at the University of Newcastle called <a href="https://www.turing.ac.uk/events/blueprint-urban-analytics-research">A Blueprint for Urban Analytics Research</a> which has been organised with the <a href="https://www.turing.ac.uk/">Alan Turing Institute</a> as part of their <a href="https://www.turing.ac.uk/research/research-programmes/urban-analytics">Urban Analytics programme</a>.</p>
<p><em>Urban analytics research helps cities run more efficiently, improves quality of life for inhabitants, and increases our understanding of how humans interact with and navigate their built environment. At the same time, urban analytics research is a fast-moving target: data availability, state-of-the-art methods, and research and policy priorities are all quickly evolving.</em></p>
<p><em>This workshop, sponsored by The Alan Turing Institute, brings together experts from a range of backgrounds to help chart an urban analytics research agenda, with the aim of pushing the boundaries of (urban) sciences for the public good. We invite participants from a variety of career stages—in particular early career—and institutional backgrounds to contribute to this conversation and help identify an agenda for impactful and cutting-edge urban analytics research and to help ensure this research tackles real-world challenges and issues.</em></p>
<figure>
<a href="https://www.turing.ac.uk/events/blueprint-urban-analytics-research">
<img src="https://www.turing.ac.uk/sites/default/files/styles/hero/public/2019-02/denys-nevozhai-100695-unsplash.jpg" alt="Birds-eye view of cars and roads" /></a>
</figure>
ABMUS 20182018-07-15T00:00:00+00:00/2018/07/ABMUS<p><a href="http://celweb.vuse.vanderbilt.edu/aamas18/">
<img src="http://celweb.vuse.vanderbilt.edu/static/images/aamasBanner.png" alt="AAMAS logo" /></a></p>
<p>Last week <a href="https://www.geog.leeds.ac.uk/people/t.crols">Tomas Crols</a> and <a href="https://www.geog.leeds.ac.uk/people/n.malleson/">Nick Malleson</a> attended the international conference on Autonomous Agents and Multi-Agent Systems (<a href="http://celweb.vuse.vanderbilt.edu/aamas18/">AAMAS</a>) and presented at the workshop on Agent-Based Modelling and Urban Systems (<a href="http://modelling-urban-systems.com/abmus2018">ABMUS</a>).</p>
<p>Their talks were entitled:</p>
<ul>
<li>
<p>Understanding Input Data Requirements and Quantifying Uncertainty for Successfully Modelling ‘Smart’ Cities (<a href="/dust/p/2018-07-15-abmus-da.html">slides</a>, <a href="/dust/p/2018-07-15-abmus-da-abstract.pdf">long abstract</a>)</p>
</li>
<li>
<p>Simulating Urban Flows of Daily Routines of Commuters (<a href="http://surf.leeds.ac.uk/p/2018-07-15-commuters-abstract.pdf">long abstract</a>).</p>
</li>
</ul>
<p>The slides for Nick’s talk can also be viewed below:</p>
<iframe src="https://urban-analytics.github.io/dust/p/2018-07-15-abmus-da.html" style="width:640px; height:420px"></iframe>