Coupling an agent-based model and an ensemble Kalman filter for real-time crowd modelling.

Agent-based modelling has emerged as a powerful tool for modelling systems that are driven by discrete, heterogeneous individuals and has proven particularly popular in the realm of pedestrian simulation. However, real-time agent-based simulations face the challenge that they will diverge from the real system over time. This paper addresses this challenge by integrating the ensemble Kalman filter (EnKF) with an agent-based crowd model to enhance its accuracy in real time.

An open dataset on individual perceptions of transport policies.

Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety. This paper reports on a new survey dataset that was created as part of the Urban Transport Modelling for Sustainable Well-Being in Hanoi (UTM-Hanoi) project.

Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread.

The global COVID-19 pandemic brought considerable public and policy attention to the field of infectious disease modelling. A major hurdle that modellers must overcome, particularly when models are used to develop policy, is quantifying the uncertainty in a model's predictions. By including the most recent available data in a model, the quality of its predictions can be improved and uncertainties reduced.

Using social media data to understand the impact of the COVID-19 pandemic on urban green space use.

Having access to and visiting urban green space (UGS) improves liveability and provides considerable benefits to residents. However, traditional methods of investigating UGS visitation, such as questionnaires and social surveys, are usually time- and resource-intensive, and frequently provide less transferable, site-specific outcomes. This study uses social media data (Twitter) to examine spatio-temporal changes in UGS use in London associated with COVID-19 related lockdowns.

Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters.

This paper explores the use of a particle filter-a data assimilation method-to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA).  The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model.