The following publications and other outputs report on the current progress of the DUST project or on related activities

Contents

  1. Peer Reviewed Articles
  2. Conference proceedings
  3. Preprints
  4. Data
  5. Other documents and resources

Peer Reviewed Articles

Kieu, M., H. Nguyen, J. A. Ward, and N. Malleson (2024). Towards Real-Time Predictions Using Emulators of Agent-Based Models. Journal of Simulation 18 (1): 29–46. DOI: 10.1080/17477778.2022.2080008

Molly Asher, Nik Lomax, Karyn Morrissey, Fiona Spooner, Nick Malleson (2023). Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread. Nature Scientific Reports 13:8637. DOI: 10.1038/s41598-023-35580-z

N. Malleson, Birkin, M., Birks, D., Ge, J., Heppenstall, A., Manley, E., McCulloch, J., Ternes, P. (2022) Agent-based modelling for Urban Analytics: State of the art and challenges. AI Communications 35, 393–406. DOI: 10.3233/AIC-220114. [PDF (open access)]

Tang, D. and N. Malleson (2022). Data assimilation with agent-based models using Markov chain sampling. Open Research Europe 2(70). DOI: 10.12688/openreseurope.14800.1 (open access)

Kieu, M., H. Nguyen, J.A. Ward and N. Malleson (2022). Towards real-time predictions using emulators of agent-based models. Journal of Simulation 1–18. DOI: 10.1080/17477778.2022.2080008 [PDF].

McCulloch, J., J. Ge, J.A. Ward, A. Heppenstall, J.G. Polhill, N. Malleson (2022) Calibrating agent-based models using uncertainty quantification methods. Journal of Artificial Societies and Social Simulation 25, 1. DOI: 10.18564/jasss.4791

Ternes, P., J.A. Ward, A. Heppenstall, V. Kumar, L.-M. Kieu, N. Malleson (2021) Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters. Open Research Europe 1, 131. DOI:10.12688/openreseurope.14144.1

An, Li, Volker Grimm, Abigail Sullivan, B.L. Turner II, Nicolas Malleson, Alison Heppenstall, Christian Vincenot, Derek Robinson, Xinyue Ye, Jianguo Liu, Emilie Lindkvist, Wenwu Tang (2021) Challenges, tasks, and opportunities in modeling agent-based complex systems, Ecological Modelling 457(1):109685. DOI: 10.1016/j.ecolmodel.2021.109685

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. Simulation Modelling Practice and Theory 113 (102386) DOI: 10.1016/j.simpat.2021.102386

Cui, N., N. Malleson, V. Houlden, and A. Comber (2021) Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review. ISPRS International Journal of Geo-Information 10(7): 425. DOI: 10.3390/ijgi10070425 (open access)

A. Whipp, N. Malleson, J. Ward, A. Heppenstall (2021) Estimates of the Ambient Population: Assessing the Utility of Conventional and Novel Data Sources. International Journal of Geo-Information 10(3), 131. DOI: 10.3390/ijgi10030131 (open access)

Malleson, N., K. Minors, Le-Minh Kieu , J. A. Ward , A. West and A. Heppenstall (2020) Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter. Journal of Artificial Societies and Social Simulation (JASSS) 23 (3). http://jasss.soc.surrey.ac.uk/23/3/3.html DOI: 10.18564/jasss.4266 (open access)

Heppenstall, A., A. Crooks, N. Malleson, E. Manley, J. Ge, and M. Batty (2020). Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities. Geographical Analysis 53: 76-91. DOI: 10.1111/gean.12267 (open access)

Kieu, Le-Minh, N. Malleson, and A. Heppenstall (2019). Dealing with Uncertainty in Agent-Based Models for Short-Term Predictions’. Royal Society Open Science 7(1): 191074. DOI: 10.1098/rsos.191074 (open access)

Kieu, Le-Minh, D. Ngoduy, N Malleson, and E. Chung (2019). A Stochastic Schedule-Following Simulation Model of Bus Routes. Transportmetrica B: Transport Dynamics 7 (1): 1588–1610. DOI: 10.1080/21680566.2019.1670118.

Crols, T., and N. Malleson (2019) Quantifying the Ambient Population Using Hourly Population Footfall Data and an Agent-Based Model of Daily Mobility. GeoInformatica (online first). DOI: 10.1007/s10707-019-00346-1. [Open access].

Conference proceedings

For a full list of conference presentations, see the presentations page.

M. Asher, Y. Oswald, and N. Malleson (2023). Predicting Pedestrian Counts using Machine Learning. Proceedings of the 26th AGILE Conference on Geographic Information Science, “Spatial data for design”. AGILE-GISS, 4, 18, 2023 DOI: 10.5194/agile-giss-4-18-2023.

R. Clay, Le-Minh Kieu, J. A. Ward, A. Heppenstall, N. Malleson (2020) Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter. In Demazeau Y., Holvoet T., Corchado J., Costantini S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science, vol 12092. Springer. DOI: 10.1007/978-3-030-49778-1_6 Paper (pdf)

D. Birks, A. Heppenstall and N. Malleson (2020). Towards the Development of Societal Twins. 24th European Conference on Artificial Intelligence - ECAI 2020. Abstract (pdf).

Heppenstall, A. and N. Malleson (2020). Building cities from slime mould, agents and quantum field theory. In Proceedings of AAMAS 2020. Abstract (pdf). Presentation. DOI: 10.5555/3398761.3398765.

Malleson, N., Jonathan A. Ward, A. Heppenstall, M. Adcock, D. Tang, J. Coello, and T. Crols. (2018). Understanding Input Data Requirements and Quantifying Uncertainty for Successfully Modelling ‘Smart’ Cities. In 3rd International Workshop on Agent-Based Modelling of Urban Systems (ABMUS), of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018). 10-15 July, Stockholm, Sweden. [Full abstract (pdf)]. [Slides (html)].

Malleson, N., A. Tapper, J. Ward, A. Evans (2017). Forecasting Short-Term Urban Dynamics: Data Assimilation for Agent-Based Modelling. In proceedings of the Social Simulation Conference (SSC) - the 13th Annual Conference of the European Social Simulation Association (ESSA). 25-29 September 2017, Dublin, Ireland. [Slides] [Abstract PDF]

Preprints

Y. Oswald, N. Malleson, K. Suchak (2023). An agent-based model of the 2020 international policy diffusion in response to the COVID-19 pandemic with particle filter. arXiv:2302.11277 [cs.MA]

Tang, D. and N. Malleson (2022). Data assimilation with agent-based models using Markov chain sampling. arXiv:2205.01616 [cs.MA].

A. Whipp, N. Malleson, Jonathan Ward, Alison Heppenstall (2021). Towards a Comprehensive Measure of the Ambient Population: Building Estimates Using Geographically Weighted Regression Preprint: 10.31235/osf.io/pquvy.

Tang, D. (2020). Finding the Maximum-a-Posteriori Behaviour of Agents in an Agent-Based Model. ArXiv:2005.02096 [Cs].

Tang, D. (2020) Decentralised, Privacy-Preserving Bayesian Inference for Mobile Phone Contact Tracing’, 2020. arXiv: 2005.05086 [cs.CY].

Tang, D. (2019). Data Assimilation in Agent-Based Models Using Creation and Annihilation Operators. ArXiv:1910.09442 [Cs].

Malleson, N., 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. arXiv:1909.09397 [cs.MA].

Kieu, Le-Minh, N. Malleson, and A. Heppenstall (2019) Dealing with Uncertainty in Agent-Based Models for Short-Term Predictions’. arXiv:1908.08288 [cs.MA].

Data

Manually Collected Footfall Counts in Leeds. Manually-collected counts of the number of people passing certain points over time in Leeds, UK.

Leeds City Council Footfall Camera Aggregated Data. Amalgamated dataset containing counts of passers-by from a number of cameras in central Leeds, UK.

Other documents and resources

Exploration of Gaussian processes for emulation of stochastic models. A series of notbooks exploring the challanges with using Gaussian Processes to estimate stochastic models, like agent-based modelling.

The original DUST Project Funding Proposal (outline).

A Review of Agent-Based Pedestrian Simulation Software.

A Review of pedestrian footfall data.