Modeling the road network capacity in a mixed HV and CAV environment

Wenhan Zhou, Jiancheng Weng, Tongfei Li, Bo Fan and Yang Bian

Physica A: Statistical Mechanics and its Applications, 2024, vol. 636, issue C

Abstract: With the integration of the advanced Internet of Vehicles and autonomous driving technology, connected and autonomous vehicles (CAVs) possess a stronger information perception ability, real-time communication and cooperation ability, and a shorter reaction time. CAVs reveal great potential in increasing traffic efficiency and promoting sustainable development of urban traffic systems. Accordingly, the introduction of CAVs in the existing traffic system not only changes the driving behavior of vehicles but also reshapes the spatial distribution of traffic flow. To measure the impact of CAVs on urban traffic systems at a macro level, we first propose the concept of the road network capacity in a mixed human-driven vehicle (HV) and CAV environment and define it as the maximum total travel demand that can be accommodated by the road network. Two nonlinear programming models (NLP) are established to formulate and calculate the road network capacity (RNC) with mixed HV and CAV flows based on the assumption that CAVs’ route choice behavior follows the UE principle and the system optimal (SO) principle, respectively. Since the existence of the equilibrium conditions makes the established model challenging to solve, we reformulated the proposed model as a mixed-integer linear programming (MILP) after employing a piecewise linear approximation approach and solved it with the commercial solver. Finally, several numerical experiments based on Nguyen–Dupuis’s network demonstrate the validity of the proposed models and solution method. The change in the RNC with the variation of the CAV penetration rate and the reaction time of CAVs are also analyzed by conducting a set of sensitivity experiments.

Keywords: Connected and autonomous vehicles; Mixed traffic flow; Road network capacity; Route choice behavior (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:636:y:2024:i:c:s0378437124000347

DOI: 10.1016/j.physa.2024.129526

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