Route Choice-based Socio-Technical Macroscopic Traffic Model

TL;DR

This paper introduces a socio-technical macroscopic traffic model that integrates human route choice behavior using behavioral economics and game theory, capturing the cyber-physical-social interactions in smart transportation.

Contribution

It presents a novel macroscopic traffic model incorporating human route choice via CPT and MFG, bridging behavioral economics with traffic flow modeling.

Findings

Model captures traffic dynamics influenced by human route choices.

Simulation results demonstrate the model's ability to reflect real traffic patterns.

Framework enables analysis of cyber-physical-social coupling in transportation systems.

Abstract

Human route choice is undeniably one of the key contributing factors towards traffic dynamics. However, most existing macroscopic traffic models are typically concerned with driving behavior and do not incorporate human route choice behavior models in their formulation. In this paper, we propose a socio-technical macroscopic traffic model that characterizes the traffic states using human route choice attributes. Essentially, such model provides a framework for capturing the Cyber-Physical-Social coupling in smart transportation systems. To derive this model, we first use Cumulative Prospect Theory (CPT) to model the human passengers' route choice under bounded rationality. These choices are assumed to be influenced by traffic alerts and other incomplete traffic information. Next, we assume that the vehicles are operating under a non-cooperative cruise control scenario. Accordingly, human route choice segregates the traffic into multiple classes where each class corresponds to a specific route between an origin-destination pair. Thereafter, we derive a Mean Field Game (MFG) limit of this non-cooperative game to obtain a macroscopic model which embeds the human route choice attribute. Finally, we analyze the mathematical characteristics of the proposed model and present simulation studies to illustrate the model behavior.