Title
A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles
Author
Mehrabani, B.B.
Erdmann, J.
Sgambi, L.
Snelder, M.
Publication year
2023
Abstract
One of the potential capabilities of Connected and Autonomous Vehicles (CAVs) is that they can have different route choice behavior and driving behavior compared with human Driven Vehicles (HDVs). This will lead to mixed traffic flow with multiple classes of route choice behavior. Therefore, it is crucial to solve the multiclass Traffic Assignment Problem (TAP) in the mixed traffic flow of CAVs and HDVs. Few studies have tried to solve this problem; however, most used analytical solutions, which are challenging to implement in real and large networks. Also, studies in implementing simulation-based methods have not considered all of CAVs' potential capabilities. On the other hand,several different (conflicting) assumptions are made about the CAV's route choice behavior in these studies. So, providing a tool that can solve the multiclass TAP of mixed traffic under different assumptions can help researchers to understand the impacts of CAVs better. To fill these gaps, this study provides an open source solution framework of the multiclass simulation-based traffic assignment problem for the mixed traffic flow of CAVs and HDVs. This model assumes that CAVs follow system optimal principles with rerouting capability, while HDVs follow user equilibrium principles. Moreover, this model can capture the impacts of CAVs on road capacity by considering distinct driving behavioral models. This proposed model is tested in two case studies. Researchers and decision-makers can implement this model in planning and operating strategies to leverage the advantages of CAVs.
Subject
Simulation-Based Traffic Assignment
Connected and Autonomous Vehicles (CAVs)
Mixed Traffic Flow
Human Driven Vehicles (HDVs)
Multiclass Traffic Assignment
Mobility & Logistics
Urbanisation
To reference this document use:
http://resolver.tudelft.nl/uuid:3063469f-6ed5-45ea-bd11-de08feb352aa
TNO identifier
981779
Source
TRB 102nd Annual Meeting of the Transportation Research Board (January 8–12, 2023 in Washington, D.C) (submitted July 2022), 1-18
Document type
conference paper