Towards a social psychology-based microscopic model of driver behavior and decision-making: Modifying Lewin's field theory

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Abstract

Central to effective roadway design is the ability to understand how drivers behave as they traverse a segment of roadway. While simple and complex microscopic models have been used over the years to analyse driver behaviour, most models: 1.) incorporate separate car-following and lane-changing algorithms, and thus do not capture the interdependencies between lane-changing and car-following vehicle; 2.) do not capture differences in the drivers' cognitive and physical characteristics; and 3.) are constructed from observed vehicle movements and make no attempt to model the discrete differences between how each roadway element alters each driver's behaviour. This paper employs field theory to construct a conceptual framework for a new microscopic model. In field theory, an agent (e.g. the driver) views a field (i.e. the area surrounding the vehicle) filled with stimuli and perceives forces associated with each stimuli once these stimuli are internalized. Based on this theory, the resulting model would be designed to directly incorporate drivers' perceptions to roadway stimuli along with vehicle movements for drivers of different cognitive and physical abilities. It is postulated that such a model would more effectively reflect reality, and if this model were accurately calibrated, could potentially model the effects of external stimuli such as innovative geometric configurations, lane closures, and technology applications such as variable message boards. A modified field theory could potentially capture and model "hot topics" in traffic engineering, such as the distracted drivers, road rage, the incorporation of ITS elements, and driver behaviour through a work zone. © 2014 Published by Elsevier B.V.

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Berthaume, A. L., Romoser, M. R. E., Collura, J., & Ni, D. (2014). Towards a social psychology-based microscopic model of driver behavior and decision-making: Modifying Lewin’s field theory. In Procedia Computer Science (Vol. 32, pp. 816–821). Elsevier B.V. https://doi.org/10.1016/j.procs.2014.05.496

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