Exploring the role of spatial cognition in predicting urban traffic flow through agent-based modelling. (March 2018)

• Record Type: Journal Article
• Authors: Manley, Ed; Cheng, Tao
• Is Part Of: Transportation research. Volume 109(2018)
• Page Start: 14
• Page End: 23
• Publication Date: 2018-03
• Subjects: Transportation modelling -- Route choice -- Traffic flow -- Spatial cognition -- Complexity; Transportation -- Research -- Periodicals; 388.011
• DOI: 10.1016/j.tra.2018.01.020
• Languages: English
• ISSNs: 0965-8564

Abstract

Highlights: Models of route choice from transportation and cognitive science widely differ. Transportation patterns product of bounded decision-making and cognitive models. Exploratory agent-based models change in traffic flow induced by model selection. Poorly validated behavioural models risk reducing policy making capabilities. Abstract: Urban systems are highly complex and non-linear in nature, defined by the behaviours and interactions of many individuals. Building on a wealth of new data and advanced simulation methods, conventional research into urban systems seeks to embrace this complexity, measuring and modelling cities with increasingly greater detail and reliability. The practice of transportation modelling, despite recent developments, lags behind these advances. This paper addresses the implications resulting from variations in model design, with a focus on the behaviour and cognition of drivers, demonstrating how different models of choice and experience significantly influence the distribution of traffic. It is demonstrated how conventional models of urban traffic have not fully incorporated many of the important findings from the cognitive science domain, instead often describing actions in terms of individual optimisation. We introduce exploratory agent-based modelling that incorporates representations of behaviour from a more cognitively rich perspective. Specifically, through these simulations, we identify how spatial cognition in respect to route selection and the inclusion of heterogeneity in spatial knowledge significantly impact the spatial extent and volume of traffic flow within a real-world setting. These initial results indicate that individual-level models of spatial cognition can potentially play an important role in predicting urban traffic flow, and that greater heed should be paid to these approaches going forward. The findings from this work hold important lessons in the development of models of transport systems and hold potential implications for policy.