On Path-based Marginal Cost of Heterogeneous Traffic Flow for General Networks

TL;DR

This paper introduces a novel method for approximately evaluating path marginal costs in complex heterogeneous traffic networks, improving accuracy and computational efficiency for system-optimal traffic assignment.

Contribution

It proposes a decomposition-based approach that models intra- and inter-class interactions using conversion factors, addressing challenges in heterogeneous traffic flow analysis.

Findings

Method outperforms existing approaches in bi-class SO-DTA problems.

Lower total travel costs achieved with the proposed method.

Effective in capturing multi-class flow competition at system optimum.

Abstract

Path marginal cost (PMC) is a crucial component in solving path-based system-optimal dynamic traffic assignment (SO-DTA), dynamic origin-destination demand estimation (DODE), and network resilience analysis. However, accurately evaluating PMC in heterogeneous traffic conditions poses significant challenges. Previous studies often focus on homogeneous traffic flow of single vehicle class and do not well address the interactive effect of heterogeneous traffic flows and the resultant computational issues. This study proposes a novel but simple method for approximately evaluating PMC in complex heterogeneous traffic condition. The method decomposes PMC into intra-class and inter-class terms and uses conversion factor derived from heterogeneous link dynamics to explicitly model the intricate relationships between vehicle classes. Additionally, the method considers the non-differentiable issue that arises when mixed traffic flow approaches system optimum conditions. The proposed method is tested on a small corridor network with synthetic demand and a large-scale network with calibrated demand from real-world data. Results demonstrated that our method exhibits superior performance in solving bi-class SO-DTA problems, yielding lower total travel cost and capturing the multi-class flow competition at the system optimum state.