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

Peer Reviewed Articles

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)

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].

Preprints

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].

Conference proceedings

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

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]

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