Agent-Based Modelling for Urban Analytics: State of the Art and Challenges

TL;DR

This paper reviews the current state and challenges of agent-based modelling in urban analytics, highlighting its potential and barriers for practical policy application in urban systems.

Contribution

It provides a comprehensive overview of recent advances, challenges, and future directions for ABM in urban analytics, especially in real-world policy contexts.

Findings

ABM is increasingly used to understand urban systems.

Challenges include data integration and model calibration under uncertainty.

Potential for real-time and large-scale urban modelling.

Abstract

Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual `agents', and the implications that their behaviour and interactions have for wider systemic behaviour. The method has been shown to hold considerable value in exploring and understanding human societies, but is still largely confined to use in academia. This is particularly evident in the field of Urban Analytics; one that is characterised by the use of new forms of data in combination with computational approaches to gain insight into urban processes. In Urban Analytics, ABM is gaining popularity as a valuable method for understanding the low-level interactions that ultimately drive cities, but as yet is rarely used by stakeholders (planners, governments, etc.) to address real policy problems. This paper presents the state-of-the-art in the application of ABM at the interface of MAS and Urban Analytics by a group of ABM researchers who are affiliated with the Urban Analytics programme of the Alan Turing Institute in London (UK). It addresses issues around modelling behaviour, the use of new forms of data, the calibration of models under high uncertainty, real-time modelling, the use of AI techniques, large-scale models, and the implications for modelling policy. The discussion also contextualises current research in wider debates around Data Science, Artificial Intelligence, and MAS more broadly.