A Queueing Model and Analysis for Autonomous Vehicles on Highways
建立多车道高速公路的排队模型，比较专用车道与混合车道两种自动驾驶汽车政策，发现专用车道仅在高度拥堵且自动驾驶汽车占多数时优于基准，而混合车道始终不差于基准。
We investigate the effects of autonomous vehicles (AVs) on highway congestion. AVs have the potential to significantly reduce highway congestion because they can maintain smaller intervehicle gaps and travel together in larger platoons than human-driven vehicles (HVs). Various policies have been proposed to regulate AV travel on highways, yet no in-depth comparison of these policies exists. To address this shortcoming, we develop a queueing model for a multilane highway and analyze two policies: the designated-lane policy (“D policy”), under which one lane is designated to AVs, and the integrated policy (“I policy”), under which AVs travel together with HVs in all lanes. We connect the service rate to intervehicle gaps (governed by a Markovian arrival process) and congestion, and measure the performance using mean travel time and throughput. Our analysis shows that although the I policy performs at least as well as a benchmark case with no AVs, the D policy outperforms the benchmark only when the highway is heavily congested and AVs constitute the majority of vehicles; in such a case, this policy may outperform the I policy only in terms of throughput. These findings caution against recent industry and government proposals that the D policy should be employed at the beginning of the mass appearance of AVs. Finally, we calibrate our model to data and show that for highly congested highways, a moderate number of AVs can make a substantial improvement (e.g., 22% AVs can improve throughput by 30%), and when all vehicles are AVs, throughput can be increased by over 400%. This paper was accepted by Jayashankar Swaminathan, operations management.