Equilibrium Traffic Dynamics with Mixed Autonomous and Human-Driven Vehicles and Novel Traffic Management Policies: The Effects of Value-of-Time Compensation and Random Road Capacity

IF 4.4 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Transportation Science Pub Date : 2023-07-04 DOI:10.1287/trsc.2021.0469
Hua Wang, Jing Wang, Shukai Chen, Q. Meng
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Abstract

Emerging autonomous vehicles (AVs) are expected to bring about a revolution in both the automotive industry and transportation systems. Introducing AVs into the existing mobility system with human-driven vehicles (HVs) yields mixed traffic with the following new features: in-vehicle compensation on value of time for AV users, distinct road capacities for pure AV and HV flows, and stochastic road capacity for the inseparable AV-HV traffic pattern. In this paper, we aim to investigate equilibrium traffic dynamics for the morning commuting problem where AVs and HVs coexist in a transportation corridor by considering these new features, and also explore several novel mixed AV-HV traffic management strategies. The AV-HV traffic pattern could be either separable (i.e., pure AV flow and pure HV flow depart from home in different periods) or inseparable, depending on the user profile condition. In addition to deriving departure time equilibriums for scenarios with separable traffic flows, significant effort is put into the scenario with an inseparable AV-HV traffic pattern, where stochastic road capacity is taken into account. Based on these equilibrium traffic analyses, we propose and explore some new traffic management strategies, including AV certificate of entitlement management scheme for scenarios with separable traffic flows and departure-period management (DPM) scheme and lane management policies for the scenario with an inseparable AV-HV traffic pattern. Eligibilities for applying these strategies are analytically derived and extensively discussed, and numerical experiments are conducted to demonstrate our theoretical findings and reveal the underlying impacts of road capacity randomness. Some lessons learned from the numerical experiments are (i) overlooking the impact of road capacity uncertainty will lead to an overestimation of system performance and even yield biased policymaking, (ii) the full dedicated-lane policy is the preferred option for the medium-level AV situation and partial dedicated-lane policies are more attractive choices for the early AV era or a market with a high AV share, and (iii) the DPM scheme could be a better substitute for partially dedicated-lane policies. Funding: This study was supported by the Ministry of Education of Singapore [Project T2EP40222-0002 under the MOE Tier 2 Grant] and the National Natural Science Foundation Council of China [Grant 72001133 and the Excellent Young Scientists Fund Program (Overseas)]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0469 .
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混合自动驾驶和人工驾驶车辆的均衡交通动力学和新的交通管理策略:时间补偿值和随机道路通行能力的影响
新兴的自动驾驶汽车(AVs)有望在汽车工业和运输系统中引发一场革命。将自动驾驶汽车引入现有的人类驾驶车辆(HVs)的交通系统,将产生混合交通,并具有以下新特征:自动驾驶汽车用户对时间价值的车内补偿,纯自动驾驶汽车和HV流量的不同道路容量,以及不可分割的自动驾驶-HV交通模式的随机道路容量。在考虑这些新特征的基础上,研究了交通走廊中av和hv共存的早晨通勤平衡交通动力学问题,并探讨了几种新的AV-HV混合交通管理策略。根据用户配置条件的不同,AV-HV流量模式可以是可分离的(即纯AV流量和纯HV流量在不同时期从家中出发),也可以是不可分离的。除了导出可分离交通流情景下的出发时间均衡外,还对考虑随机道路容量的不可分离的AV-HV交通模式情景下的出发时间均衡进行了大量的研究。在此基础上,我们提出并探索了一些新的交通管理策略,包括针对可分离交通流场景的AV授权证书管理方案和出发期管理(DPM)方案,以及针对不可分离的AV- hv交通模式场景的车道管理策略。本文对这些策略的适用性进行了分析推导和广泛讨论,并进行了数值实验来验证我们的理论发现,揭示道路容量随机性的潜在影响。从数值实验中得到的一些经验教训是:(1)忽视道路容量不确定性的影响将导致对系统性能的高估,甚至导致政策制定的偏差;(2)完全专用车道政策是中等水平自动驾驶情况下的首选方案,而部分专用车道政策对于早期自动驾驶时代或自动驾驶份额较高的市场更具吸引力。(iii) DPM计划可能是部分专用车道政策的更好替代品。本研究由新加坡教育部[教育部二级资助项目T2EP40222-0002]和中国国家自然科学基金委员会[资助项目72001133和优秀青年科学家基金(海外)计划]资助。补充材料:在线附录可在https://doi.org/10.1287/trsc.2021.0469上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Transportation Science
Transportation Science 工程技术-运筹学与管理科学
CiteScore
8.30
自引率
10.90%
发文量
111
审稿时长
12 months
期刊介绍: Transportation Science, published quarterly by INFORMS, is the flagship journal of the Transportation Science and Logistics Society of INFORMS. As the foremost scientific journal in the cross-disciplinary operational research field of transportation analysis, Transportation Science publishes high-quality original contributions and surveys on phenomena associated with all modes of transportation, present and prospective, including mainly all levels of planning, design, economic, operational, and social aspects. Transportation Science focuses primarily on fundamental theories, coupled with observational and experimental studies of transportation and logistics phenomena and processes, mathematical models, advanced methodologies and novel applications in transportation and logistics systems analysis, planning and design. The journal covers a broad range of topics that include vehicular and human traffic flow theories, models and their application to traffic operations and management, strategic, tactical, and operational planning of transportation and logistics systems; performance analysis methods and system design and optimization; theories and analysis methods for network and spatial activity interaction, equilibrium and dynamics; economics of transportation system supply and evaluation; methodologies for analysis of transportation user behavior and the demand for transportation and logistics services. Transportation Science is international in scope, with editors from nations around the globe. The editorial board reflects the diverse interdisciplinary interests of the transportation science and logistics community, with members that hold primary affiliations in engineering (civil, industrial, and aeronautical), physics, economics, applied mathematics, and business.
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