ACC, queue storage, and worrisome news for cities

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY Transportation Research Part C-Emerging Technologies Pub Date : 2024-08-15 DOI:10.1016/j.trc.2024.104809
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Abstract

Rush-period traffic conditions in two idealized settings are forecast into the future, when most drivers will presumably rely on adaptive cruise control (ACC) while operating their cars. Field experiments emulating the full range of congested conditions confirm that, for a given traffic speed, the spacings for ACC-vehicles tend to be larger than those in present-day congestion, where vehicles are fully human-controlled. These larger spacings mean smaller densities, which mean, in turn, that queues will be less compacted than at present. The queues will therefore expand over greater distances in the future, as more ACC-controlled vehicles enter the scene. These wider-spread, uncompacted queues spell trouble for cities, where queue storage during a rush is often a problem already.

Simulations calibrated to the field-measured data were used to explore this unintended consequence of ACC for various foreseeable futures. Assumptions favorable to ACC were adopted throughout, to produce what are likely lower-bound estimates of future queue-storage problems. These lower bounds served as simple means to address forecast uncertainties. This is because our best-case outcomes for all futures examined are still far worse than the glowing predictions from elsewhere of how ACC may someday eliminate congestion. The first idealized setting was inspired by Downtown Los Angeles, where moderately high congestion already persists during each rush, but where physically long street links help with queue storage. We predict that, owing to ACC alone, rush-period vehicle hours traveled (VHT) on this first network will grow from present-day levels by as much as 12%. In the second setting, inspired by Midtown Manhattan where congestion is already severe and link lengths are short, VHT is predicted to grow by as much as 87%. Higher bottleneck capacities often promised of ACC are shown to be of little value when spillover queues constrain bottleneck flows from reaching those capacities. Adjusting onboard ACC controllers to produce smaller jam spacings was tested through simulation. The tests show how looming problems might be averted by this intervention, and futures thus improved.

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ACC、队列存储和城市的令人担忧的消息
我们预测了未来两个理想化环境下的高峰期交通状况,届时大多数驾驶员在驾驶汽车时可能会依赖自适应巡航控制系统(ACC)。模拟各种拥堵状况的现场实验证实,在给定车速下,自适应巡航控制车辆的间距往往比目前完全由人工控制的拥堵状况下的间距要大。这些较大的间距意味着较小的密度,这反过来又意味着队列的紧凑程度将低于目前的水平。因此,随着越来越多的自动控制车辆进入现场,未来的车龙将会扩展到更远的距离。这些分布更广、不紧凑的车龙会给城市带来麻烦,因为高峰期的车龙存储往往已经是一个问题。我们使用了根据现场测量数据进行校准的模拟,以探讨自动控制汽车在各种可预见的未来所带来的意外后果。整个模拟过程都采用了对自动售检票系统有利的假设,以得出对未来排队等候问题的下限估计。这些下限是解决预测不确定性的简单方法。这是因为我们所研究的所有未来的最佳结果仍然比其他地方对 ACC 有朝一日如何消除拥堵的辉煌预测要糟糕得多。第一个理想化场景的灵感来源于洛杉矶市中心,那里在每个高峰期都会出现中度拥堵,但实际的长距离街道连接有助于队列存储。我们预测,仅由于采用了自动拥堵控制系统,这第一个网络的高峰期车辆行驶小时数(VHT)将比目前的水平增长多达 12%。在第二种情况下,受曼哈顿中城的启发,交通拥堵已经非常严重,而且连接长度较短,预计 VHT 将增长高达 87%。当溢出队列限制了瓶颈流量达到这些容量时,ACC 通常承诺的更高瓶颈容量就显示出价值不大。通过模拟测试,对车载自动控制控制器进行了调整,以产生更小的拥堵间隔。测试结果表明,通过这种干预措施可以避免迫在眉睫的问题,从而改善期货交易。
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
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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