Modular Autonomous Vehicle in Heterogeneous Traffic Flow: Modeling, Simulation, and Implication
<|reference_start|>Modular Autonomous Vehicle in Heterogeneous Traffic Flow: Modeling, Simulation, and Implication: Modular autonomous vehicles (MAVs) represent a groundbreaking concept that integrates modularity into the ongoing development of autonomous vehicles. This innovative design introduces unique features to traffic flow, allowing multiple modules to seamlessly join together and operate collectively. To understand the traffic flow characteristics involving these vehicles and their collective operations, this study established a modeling framework specifically designed to simulate their behavior within traffic flow. The mixed traffic flow, incorporating arbitrarily formed trains of various modular sizes, is modeled and studied. Simulations are conducted under varying levels of traffic demand and penetration rates to examine the traffic flow dynamics in the presence of these vehicles and their operations. The microscopic trajectories, MAV train compositions, and macroscopic fundamental diagrams of the mixed traffic flow are analyzed. The simulation findings indicate that integrating MAVs and their collective operations can substantially enhance capacity, with the extent of improvement depending on the penetration rate in mixed traffic flow. Notably, the capacity nearly doubles when the penetration rate exceeds 75%. Furthermore, their presence significantly influences and regulates the free-flow speed of the mixed traffic. Particularly, when variations in operational speed limits exist between the MAVs and the background traffic, the mixed traffic adjusts to the operating velocity of these vehicles. This study provides insights into potential future traffic flow systems incorporating emerging MAV technologies.<|reference_end|>
@article{ye2024modular,
    title={Modular Autonomous Vehicle in Heterogeneous Traffic Flow: Modeling,
  Simulation, and Implication},
    author={Lanhang Ye, Toshiyuki Yamamoto},
    journal={arXiv preprint arXiv:2409.17945},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17945},
    primaryClass={cs.MA cs.ET}
}