Worst-case traffic assignment model for mixed traffic flow of human-driven vehicles and connected and autonomous vehicles by factoring in the uncertain link capacity. (July 2022)

• Record Type: Journal Article
• Authors: Wang, Jian; Wang, Wei; Ren, Gang; Yang, Min
• Is Part Of: Transportation research. Volume 140(2022)
• Publication Date: 2022-07
• Subjects: Connected and autonomous vehicles -- Mixed traffic assignment model -- Bilevel programming problem -- Sensitivity analysis; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103703
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Analyzed the maximum and minimum link capacity for given traffic conditions under a mixed traffic flow environment. Developed a multiclass traffic assignment problem by factoring in uncertain link capacity. Developed a worst-case mixed traffic assignment problem to compute the equilibrium flow minimizing the system performance. Proposed a globally convergent solution algorithm for the worst-case mixed traffic assignment problem. Abstract: Connected and autonomous vehicles (CAVs) can form platoons to reduce the time headway and improve the link capacity. However, in a mixed traffic flow environment where both human-driven vehicles (HDVs) and CAVs exist, the platoon intensity is significantly impacted by the stochastic order of the HDVs and CAVs (i.e., the fleet sequence). Therefore, the link capacity involves a large uncertainty even under the same HDV and CAV flow. This uncertain link capacity can cause a large variation in network flow. In the literature, traffic assignment models for mixed traffic flows of HDVs and CAVs are developed based on expected link capacity models, in which the computed link capacity is deterministic for given HDV and CAV flows. These models ignore the impacts of uncertain link capacity on the network performance and network flow distribution, which can dramatically reduce the effectiveness of the corresponding planning strategies. To address this problem, this study proposes a worst-case mixed traffic assignment model. It aims to compute the worst network performance and corresponding equilibrium flow that may occur due to uncertain link capacity. The worst-case mixed traffic assignment is formulated as a bilevel programming problem, where the low-level problem is a variational inequality problem presented to compute the equilibrium results based on a fixed link capacity while the upper-level problem is to find the optimal input for all link capacities within their ranges to minimize the network performance. The partition-based norm relaxed method of the feasible direction solution algorithm is proposed to solve the bilevel worst-case mixed traffic assignment problem. A numerical application shows that the uncertain link capacity has drastic effects on the network flows and network performance, and the proposed algorithm can effectively and efficiently solve the bilevel worst-case mixed traffic assignment problem to compute the worst-case network equilibrium flows and network performance. These results can help traffic managers design robust planning strategies to ensure a minimum level of network performance under the impacts of uncertain link capacity.