The following is the original application that was submitted to the Eurpoean Research Council

This research will create a new method for dynamically assimilating data into agent-based models. This will create a step-change in our ability to reliably simulate urban systems and to forecast of the impacts of civil emergencies (and their management plans) on human populations.


Civil emergencies such as flooding, terrorist attacks, fire, etc., can have devastating impacts on people, infrastructure, and economies. Knowing how to best respond to an emergency can be extremely difficult because building a clear picture of the emerging situation is challenging with the limited data and modelling capabilities that are available. Agent-based modelling (ABM) is a field that excels in its ability to simulate human systems and has therefore become a popular tool for simulating disasters and for modelling strategies that are aimed at mitigating developing problems. However, the field suffers from a serious drawback: models are not able to incorporate up-to-date data (e.g. social media, mobile telephone use, public transport records, etc.). Instead they are initialised with historical data and therefore their forecasts diverge rapidly from reality.

To address this major shortcoming, this research will develop dynamic data assimilation methods for use in ABMs. These techniques have already revolutionised weather forecasts and could offer the same advantages for ABMs of social systems. There are serious methodological barriers that must be overcome, but this research has the potential to produce a step change in the ability of models to create accurate short-term forecasts of social systems. The project is largely methodological, and will evidence the efficacy of the new methods by developing a cutting-edge simulation of a city – entitled the Dynamic Urban Simulation Technique (DUST) – that can be dynamically optimised with streaming ‘big’ data. The model will ultimately be used in three areas of important policy impact: (1) as a tool for understanding and managing cities; (2) as a planning tool for exploring and preparing for potential emergency situations; and (3) as a real-time management tool, drawing on current data as they emerge to create the most reliable picture of the current situation.

What is the problem and why is it important?

Civil emergencies such as flooding, terrorist attacks, fire, train/air crashes, earthquakes, severe short-term air quality deterioration, etc., can have devastating impacts on people, infrastructure, and economies [8]. Knowing how to best respond to a developing emergency can be extremely difficult. This is because building a clear picture of the emerging situation can be challenging with the limited data available and also because, although models for internal evacuations (e.g. buildings, aeroplanes, etc.) are relatively well developed, models for evacuating urban regions are not. Those that could be used are often based on aggregate mathematical equations [see, e.g. 23] and struggle to account for behavioural heterogeneity in the population or the highly complex nature of the physical environment. A disaggregate simulation method that is able to incorporate the limited up-to-date data that emerge during a crisis and simulate the underlying system to a high degree of detail would be an extremely important development.

Agent-based modelling (ABM) is a field that excels in its ability to simulate human systems [6] and has occasionally been used for simulating civil emergencies [13454850]. Rather than attempting to derive aggregate mathematical equations to describe the behaviour of discrete individual entities (e.g. people), ABMs encapsulate system-wide characteristics by simulating the behaviour of individual ‘agents’ directly. This has been shown to be much more effective at modelling complex systems than traditional aggregate approaches [2]. However, the field suffers from a serious drawback: models are not able to incorporate up-to-date data that describe the state of the system (e.g. from social media, mobile telephone use, public transport records, etc.). Instead they are initialised solely with with historical data (censuses, surveys, etc) and thus diverge rapidly from reality. This limits their ability to create accurate short-term forecasts of the impacts of civil emergences or other unusual events. To address this major shortcoming, this research will develop dynamic data assimilation methods for ABM. These techniques have already revolutionised the accuracy of numerical weather predictions [24] and could offer the same advantages for models of urban systems. There are serious methodological barriers that must be overcome, but this research has the potential to produce a step change in the ability of models to create accurate short-term forecasts of social systems. The research will evidence the efficacy of the approach through the development of a cutting-edge simulation of urban dynamics that will be used to improve emergency plans and responses in a case study area. []{#Q1-1-4}

What are the main gaps and challenges?

Due to its aofrementioned ability to model complex social systems, ABM is becoming increasingly important for modelling human systems and is an ideal tool for simulating ‘normal’ urban activities as well as modelling civil emergencies [e.g. 13454850]. Although the proposed research will focus on modelling as a tool for emergency management, the development of dynamic data assimilation methods for ABM will have much wider applicability. It will have applications in forecasting phenomena such as traffic congestion and real-time crowd behaviour, and more generally as a means of utilising the instantaneous, streaming characteristics of many modern datasets to produce short-term, high-quality, local analyses and forecasts that can inform agile and responsive policy-making. This vision of policy making is one of the greatest potential advantages of the ‘smart cities’ movement, but has yet to be properly realised as most initiatives are purely reactive [7192656]. Therefore the methodological innovation proposed here has the potential to significantly advance the field and its applicability extends well beyond civil emergency management.

ABMs are commonly used either as in silico thought experiments or as detailed models of the real world. The latter are commonly termed predictive models and are becoming increasingly important as a means of understanding and forecasting change in social systems [16]. This is particularly pertinent in the era of ‘big data’ and ‘smart cities’, in which short-term urban management practices are increasingly being driven by the vast new streams of data that are being created by citizens and sensors (for example see [19]). Sources such as mobile phone call data records (CDRs) [14], public transport smart cards [3], vehicle traffic counters [7], social media contributions [33], etc. hold a wealth of information about the dynamics of cities. ‘Smart’ cities attempt to make use of these data to monitor and manage city flows [1026]. At present, however, the majority of planning systems currently in use are purely reactive; they are able to respond to present circumstances but do not attempt to forecast the impacts of short-term policy changes [792656]. ABM, on the other hand, holds the potential to encapsulate these diverse data streams and use the observations of citizens for simulations of near real-time emergency management.

The field of ABM has a substantial drawback that limits its potential as the planning tool of the future: namely it is unable to incorporate emerging observational data streams to reduce uncertainty. Typically, historical data are used to estimate suitable model parameters and models are subsequently iterated forward in time, independently of any new data that might arise. As the systems under study are complex, model predictions diverge rapidly from reality. Therefore a mechanism to reduce uncertainty in model predictions in response to new information about the world must be developed if this important field is to advance.

Fortunately, methods do exist to tackle this problem. Dynamic data assimilation (DDA) is a technique that has been widely used in fields such as meteorology, hydrology, oceanography, etc., and is one of the main reasons that weather forecasts have improved so substantially in recent decades [24]. In effect, DDA refers to a suite of mathematical approaches that make it possible to incorporate up-to-date observational data (from weather stations, satellite images, etc.) into models. This makes it possible to more accurately represent the current state of the system, and therefore reduce the uncertainty in future predictions. However, DDA methods are intrinsic to their underlying models – typically systems of partial differential equations – and cannot easily be disassociated from them for use in ABM. Figure 1 represents the overall vision for a dynamically optimised city model. This, in turn, will spawn a new generation of social forecasting models that will be integral to the planning and evaluation of contemporary (‘smart’) cities.

Diagram of data assimilation
An illustration of dynamic data assimilation: the state of a hypothetical agent-based city simulation (vertical axis) that can be optimised in response to new data (e.g. from social media) as they are created.

What are the aims and objectives of the project?

The overarching aim of the research is to develop data assimilation methods for use in ABM that will underpin the next generation of urban models. Specifically, the research will generate a new agent-based model of urban dynamics, optimised in real time using data assimilation methods, that can ultimately be used as a tool both for planning responses to emergency situations and for real-time emergency response management. The research objectives are to:

  1. Adapt data assimilation methods that have been successfully used in other fields for use in agent-based modelling (activity 1).

  2. Develop a suite of companion methods, including ensembles and model emulators, that will help to make agent-based modelling more amenable to dynamic data assimilation (activity 2).

  3. Review the available data sources that offer insight into real-time urban dynamics and develop data analytics to extract information that is useful for the simulation (activity 3).

  4. Develop a comprehensive agent-based model – the Dynamic Urban Simulation Tool (DUST) – that will be capable of simulating the most common activities of all individuals in an urban area, that can be optimised dynamically in response to real-time data streams (activity 3).

  5. Implement an emergency planning and response tool for to demonstrate the efficacy of the new dynamic agent-based model (activity 3).

  6. Develop a suite of machine learning methods that estimate future population flows to complement and validate the new agent-based simulation (activity 4).

What are the novel and ground-breaking aspects of the project?

The most ground-breaking and novel aspects of this project are:

  • The development of methods for dynamically assimilating data into ABMs.
  • A proof-of-principle model (DUST) capable of dynamically assimilating real-world streaming data that can be used as an emergency planning tool and for more general urban management.

What do I plan to do?

This project will establish a team who will develop cutting-edge urban simulations that are capable of, for the first time, assimilating real-time data to reduce error in forecasts. The team will be inherently interdisciplinary, drawing on expertise from mathematics, geography, computer science, and the environmental sciences. The work involves four complementary activities. Activities 1 and 2 are the most important and focus on the core methodological development by adapting data assimilation and companion methods for use in ABM. Activities 3 and 4 are empirical, and will draw on the methodological innovation in activities 1 and 2 to create new models of urban dynamics.

Activity 1 – Dynamic Data Assimilation for Agent-Based Models

The quality of weather predictions has improved significantly in recent decades, to the extent that 7-day forecasts are now more accurate than 5-day forecasts were in the 1990s [4]. Part of this innovation can be attributed to improvements in data assimilation techniques [24]. The need for data assimilation was born out of data scarcity. Numerical weather prediction models typically have two orders of magnitude more degrees of freedom than they do observation data, so it is necessary to add additional information into models during initialisation (termed background or first guess information). To solve this problem, models began to be initialised with a combination of real observations (from satellites, weather stations, etc.) as well as predictions from other forecasts. This allows models to produce estimates that are consistent in space and time, using up-to-date observational data. This has the effect of both improving forecast accuracy and transporting information from geographical regions that are data rich to those that are data poor [24]. Both of these benefits are extremely relevant for a model of an urban system.

There are a variety of specific methods that perform dynamic data assimilation, including the Successive Corrections Method, Optimal Interpolation, 3D-Var, 4D-Var, and (Ensemble) Kalman Filtering. This activity will begin by testing all of the relevant methods, with respect to their potential application in urban models, and then iteratively adapting them for ABM. This, in itself, will be an extremely challenging endeavour. Numerical weather prediction models are typically based on aggregate differential equations, with functions linearised mathematically, so the aforementioned data assimilation methods make assumptions about the linearity of their underlying models. Agent-based models simulate the interactions between discrete entities whose behaviours are heterogeneous and models are therefore inherently non-linear. Although early work in this area by the PI points to Ensemble Kalman Filters (a DDA method that samples from a number of independent model runs to efficiently estimate uncertainty) as a means of overcoming problems with non-linearity [53], this is far from comprehensive and it is not clear how the method can be adapted for anything beyond the simplest of agent-based models. To adapt these techniques, that have not been designed to work in social systems, the activity will begin by designing methods for the simplest forms of agent based models, before moving on to models that encapsulate more complicated features (agent heterogeneity, feedback mechanisms, interactions, etc.).

This activity is the most challenging aspect of the proposal, but it also has the potential of the greatest reward. It proposes exploratory methodological work that has the potential to make a very significant contribution to the field. It will begin a move towards bringing agent-based models in to line with best practice from more established fields, and uncover new, long-term research challenges that need to be addressed. For this reason, Activity 1 will be the most heavily resourced, with support by a long-term, full-time PDRA with a background in applied mathematics and/or environmental modelling, a full-time PhD student, and the majority of the Principal Investigator’s time.

Activity 2 – Companion Methods

This Activity will draw on companion methods in fields such as meteorology to support the core agent-based modelling and data assimilation work. Like Activity 1, this work is novel, but, as the methods to be adapted are not embedded in their underlying mathematical models to the same extent, it does not need to be as heavily resourced. The two methods, in particular, that the activity will focus on initially are ensemble modelling and emulators.

Ensemble modelling is a technique that is used commonly in meteorology to produce more accurate forecasts and as a means of quantifying forecast uncertainty. It was born out of a recognition that the growth in uncertainties that arise from inaccuracies in initial model starting conditions needs to be encapsulated [30] and is one of innovations that has lead to the largest improvement in the predictive ability of forecasts [4]. Rather than running a single model, an ensemble of models are each initialised with small variations in their starting conditions. By comparing the results of different individual model instances it is possible to quantify the impact of the different starting conditions.

There are a number of methodological difficulties regarding ensemble modelling that Activity 2 will address. Firstly, because the behavioural theories on which ABMs are based contain a degree of uncertainty (e.g. we are rarely _certain that an individual will take a particular action), agent-based models are not deterministic so identical models will naturally diverge to some extent. Unless this divergence is understood and quantified, the computational difficulty in running ensembles of models will increase greatly, as numerous model runs are required for each instance in an ensemble. Furthermore, models might diverge to the point of being incomparable, whereby it would be impossible to create a single representative summary for an ensemble instance. This is a difficulty that ABM in general has yet to overcome, and one that this project will contribute to. Emulators (discussed below) will be useful here. The second difficulty is that the accuracy of the social behavioural theories that ultimately drive agent-based models are much less certain than the laws that drive physical systems. Hence uncertainty is introduced not just through the input data, but also through inaccuracies in the internal model dynamics [49]. Generally, although ensemble modelling is desired in the field of agent-based modelling [20], there is a lack of research into how it should be best used.

The second technique that the activity will adapt is that of model emulators. These are simplified representations of a more complex and computationally expensive model that are easier to compute [29]. Emulators are used in a range of fields [51851], but are in their infancy in agent-based modelling [47]. Agent-based models are typically extremely computationally expensive, so developing a means of reliably emulating models is essential. One of the most difficult aspects of the work will be designing emulators that are able to account for the non-linearity that agent-based models exhibit. The Activity will begin by reviewing existing implementations that use regression [21], before exploring more advanced methods that have already been used successfully to create emulators for models of non-linear systems [44], although not for agent-based models.

Activity 3 – Modelling Urban Dynamics: the Dynamic Urban Simulation Tool (DUST)

Activity 3 will enact the methodological work in the preceding activities by implementing the Dynamic Urban Simulation Tool (DUST). This agent-based modelling tool will create hypothetical proxy agents to represent all individuals in an urban area and simulate the most ‘common’ behaviours that ultimately drive urban dynamics (e.g. commuting, shopping, education, etc.) as determined from prior empirical research [1727]. The ultimate outcome will be a dynamically-optimised city model that can be used to for three areas of important policy impact: (1) as a tool for understanding and managing cities in its own right; (2) as a planning tool for exploring potential emergency scenarios and preparing for them; and (3) as a real-time management tool that runs during an emergency, drawing on current data as they emerge to create the most reliable picture of the current situation and subsequently produce highly accurate short-term forecasts. DUST will not attempt to replace existing tools that focus on efficient information sharing and coordination of the emergency services [84654]. Rather, the model will allows policy makers to better understand how the wider population will react in the event of disruption to infrastructure, and how policies can be designed to limit this disruption. Although this activity is challenging, it is achievable with the resources requested. The PI has considerable expertise in the technologies required to build such a model, such as ABM and synthetic population generation [22313234353637383940414243], and the challenges are largely technical rather than methodological. Although large agent-based models have been attempted [15], a model that simultaneously captures individual behavioural complexity, a realistic environment, and dynamic optimisation from real data will be a new and timely addition to the field.

Activity 4 – Deep Learning Models of Urban Dynamics

Validating agent-based models (i.e. quantifying the extent to which they are able to accurately represent the system under study) is one of the key ongoing challenges for the discipline [1112]. Although data assimilation will reduce uncertainty in the model outcomes by “using all the available information” [52], it will be important to apply additional validation methods. “Docking” [155] is commonly used, whereby a second model is used ascertain whether similar results can be replicated. This project proposes a novel approach: drawing on the ongoing innovations in the field of machine learning to adapt a recurrent neural network (RNN) to model urban flows at an aggregate level. Unlike traditional neural networks, that are stateless, RNNs maintain information about a history of past inputs [28]. They use this historical state vector to predict future states. For example, RNNs have been used to generate new text in the style of the author that they were initialised with, e.g. Shakespeare [25]. Therefore data about population flows or densities (from cameras that count the number of passers-by, aggregate mobile phone counts, etc.) will be used to initialise an RNN that can subsequently be used to make short-term predictions about future urban dynamics. This approach is novel and interesting in its own right – neural networks have not been applied to the problem of predicting short-term population flows – and will provide a valuable means of validating the research results. []{#Q1-1-13}

What are the main risks with the project?

This is a high risk, high reward project. It proposes novel, ambitious, and innovative methodological developments that, as with all methodological work, carry the risk of not satisfying the original aims. However, the potential rewards far outweigh the risks; it is the use of incomplete and unproven methods that makes this project state of the art. If successful, ability of ABMs to incorporate diverse data and make more accurate predictions will be of direct relevance to the community of urban planners, policy makers, and agent-based modellers. The empirical outcomes of the work have the potential to revolutionise urban planning in the context of smart cities by providing a means of accurately simulating urban dynamics at higher levels of accuracy than has been possible before.

The risks will be mitigated through iterative, achievable work packages and by resourcing the activities with the highest risk the most heavily. The risks are also mediated by the quality of the research team. The PI has a wealth of experience in building highly advanced models, particularly agent-based models [223941], and is well versed in problems of model execution cost [42], the development of innovative methods for model optimisation [43], and with early explorations of dynamic data assimilation methods for agent-based modelling [53]. The post-doctoral researchers will be employed on long-term contracts which will encourage the strongest applicants. Furthermore, through attendance at conferences and workshops, the project will draw on the growing community of agent-based modellers to engage with and support the work. Most importantly, if Activity 1 does proceed as planned, the development of ensemble models and emulators (activity 2) and the parts of the DUST model that do not assimilate data (activity 3) are all of significant value their own right.


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