OCC-MP:公交和高客流车辆优先的最大压力框架

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
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引用次数: 0

摘要

最大压力(MP)是一种分散式自适应交通信号控制方法,已被证明能使私家车的吞吐量最大化。然而,基于 MP 的信号控制算法并不能区分公交车辆和私家车辆的行驶,也不能区分高载客量私家车辆和单载客量私家车辆的行驶。优先考虑公交车辆或其他高乘载车辆(HOV)的通行对于减少拥堵、提高公交运营的可靠性和效率至关重要。本研究提出了 OCC-MP:一种基于 MP 的新型算法,在计算移动权重时同时考虑了车辆队列和乘客占用率。通过对乘客占有率较高的交通流进行更高的权重计算,公交车和其他多车道车辆可获得隐含的优先权,同时考虑到这种优先权对单座车辆的负面影响。而且,与基于规则的公交信号优先(TSP)策略不同,OCC-MP 还能更自然地在信号灯控制的交叉路口考虑到相互冲突的公交线路,并为其通行提供便利,即使是在没有专用车道的混合交通中也是如此。对不同需求和公交配置下的网格网络进行模拟,证明了 OCC-MP 在提供 TSP 方面的有效性,同时减少了对低乘载率私家车的负面影响。此外,与集成到 MP 框架中的基于规则的 TSP 策略相比,OCC-MP 的需求稳定区域更大。研究还表明,OCC-MP 的性能对公交车辆乘客占用率信息的误差具有鲁棒性,在无法获得私家车乘客占用率的情况下也可应用。最后,当一部分车辆能够向信号控制器提供信息时,OCC-MP 可应用于部分联网车辆 (CV) 环境,在 CV 渗透率较低的情况下,其性能优于基线方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OCC-MP: A Max-Pressure framework to prioritize transit and high occupancy vehicles

Max-pressure (MP) is a decentralized adaptive traffic signal control approach that has been shown to maximize throughput for private vehicles. However, MP-based signal control algorithms do not differentiate the movement of transit vehicles from private vehicles or between high and single-occupancy private vehicles. Prioritizing the movement of transit or other high occupancy vehicles (HOVs) is vital to reduce congestion and improve the reliability and efficiency of transit operations. This study proposes OCC-MP: a novel MP-based algorithm that considers both vehicle queues and passenger occupancies in computing the weights of movements. By weighing movements with higher passenger occupancies more heavily, transit and other HOVs are implicitly provided with priority, while accounting for any negative impacts of that priority on single occupancy vehicles. And, unlike rule-based transit signal priority (TSP) strategies, OCC-MP more naturally also accommodates conflicting transit routes at a signalized intersection and facilitates their movement, even in mixed traffic without dedicated lanes. Simulations on a grid network under varying demands and transit configurations demonstrate the effectiveness of OCC-MP at providing TSP while simultaneously reducing the negative impact imparted onto lower occupancy private vehicles. Furthermore, OCC-MP is shown to have a larger stable region for demand compared to rule-based TSP strategies integrated into the MP framework. The performance of OCC-MP is also shown to be robust to errors in passenger occupancy information from transit vehicles and can be applied when passenger occupancies of private vehicles are not available. Finally, OCC-MP can be applied in a partially connected vehicle (CV) environment when a subset of vehicles is able to provide information to the signal controller, outperforming baseline methods at low CV penetration rates.

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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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