A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles
(2023) Transportmetrica A: Transport Science ā Vol. 32, nĀ° 1, p. 0 (2023)
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- Authors
Bamdad Mehrabani, Behzad 
- Abstract
- Connected and Autonomous Vehicles (CAVs) may exhibit different driving and route choice behaviors compared to Human-Driven Vehicles (HDVs), which can result in a mixed traffic flow with multiple classes of route choice behavior. To effectively address this issue, it is necessary to solve the Multiclass Traffic Assignment Problem (TAP) for mixed traffic of CAVs and HDVs. However, most existing studies have relied on analytical solutions that are difficult to apply in real-world and large networks, especially in dynamic scenarios. Furthermore, simulation-based methods have not fully considered all of CAVs' potential capabilities and have made several different assumptions about their route choice behavior. This study presents an open-source solution framework for the multiclass simulation-based traffic assignment problem in mixed traffic of CAVs and HDVs. The proposed model assumes that CAVs follow system optimal principle with rerouting capabilities, while HDVs adhere to user equilibrium principle. It also considers the impact of CAVs on road capacity through distinct driving behavioral models at both micro and meso scales. The proposed model is demonstrated through three case studies, which show that as the penetration rate of CAVs increases, the total travel time for all vehicles decreases. This study bridges the gaps in previous research and provides a valuable tool that can consider several assumptions for better understanding the impact of CAVs on mixed traffic flow.
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Citations
Bamdad Mehrabani, B., Erdmann, J., Sgambi, L., Seyedabrishami, S., & Snelder, M. (2023). A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles. Transportmetrica A: Transport Science, 32(1), 0. https://doi.org/10.1080/23249935.2023.2257805 (Original work published 2023)