Integrated-Hybrid Framework for Connected and Autonomous Vehicles Microscopic Traffic Flow Modelling
In this study, a novel traffic flow modeling framework is proposed considering the impact of driving system and vehicle mechanical behavior as two different units on the traffic flow. To precisely model the behavior of Connected and Autonomous (CA) vehicles, three submodels are proposed as a novel microscopic traffic flow framework, named Integrated-Hybrid (IH) model. Focusing on the realization of the car following behavior of CA vehicles, the driving system (vehicle control system) and the vehicle mechanical system are modeled separately and linked by throttle and brake actuators model. +e IH model constitutes the key part of the Full Velocity Difference (FVD) model considering the mechanical capability of vehicles and dynamic collision avoidance strategies to ensure the safety of following distance between two consecutive vehicles. Linear stability conditions are derived for each model and developing methodology for each submodel is discussed. Our simulations revealed that the IH model successfully generates velocity and acceleration profiles during car following maneuvers and throttle angle/brake information in connected vehicles environment can effectively improve traffic flow stability. +e vehicles’ departure and arrival process while passing through a signal-lane with a traffic light considering the anticipation driving behavior and throttle angle/brake information of direct leading vehicle was explored. Our numerical results demonstrated that the IH model can capture the velocity fluctuations, delay times, and kinematic waves efficiently in traffic flow.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Department, Program, or Center
Civil Engineering Technology Environmental Management and Safety (CET)
Ammar Jafaripournimchahi, Yingfeng Cai, Hai Wang, Lu Sun, Jiancheng Weng, "Integrated-Hybrid Framework for Connected and Autonomous Vehicles Microscopic Traffic Flow Modelling", Journal of Advanced Transportation, vol. 2022, Article ID 2253697, 16 pages, 2022. https://doi.org/10.1155/2022/2253697
RIT – Main Campus