Multimodal traffic assignment considering heterogeneous demand and modular operation of shared autonomous vehicles

This study proposes a solution to address the lack of consideration for personalized needs in complex multimodal transportation systems by formulating and solving a heterogeneous demand traffic assignment problem (HD-TAP). The HD-TAP takes into account the varying preferences of travelers when selecting travel modes and the common occurrence of multiple people traveling together. The use of modular shared autonomous vehicles (SAVs) is also considered in the model, which allows for flexibility in combining the number of modules based on the number of group riders. The HD-TAP is formulated as a multimodal, multiclass, multiple equilibrium principles, combined mode split traffic assignment model, incorporating a cross-nested logit model for private vehicle travelers' route choice behavior and a multinomial logit user equilibrium model for non-private vehicle travelers' mode and route choice behavior. To solve the HD-TAP, a gradient projection-based algorithm is developed. Numerical examples demonstrate that the proposed algorithm can efficiently solve large-scale multimodal network problems. Through numerical experiments in real-world networks, the study investigates the impacts of preferred travel modes, the number of group riders, and the modular operation of SAVs on system performance. The findings indicate that providing an excessive number of modular SAVs with a capacity of five passengers or fewer may result in a loss of public transit users. It is important to control the supply of such vehicles to ensure the preservation of public transit usage.