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Impacts of the urban parking system on cruising traffic and policy development: the case of Zurich downtown area, Switzerland

机译:城市停车系统对巡航和政策发展的影响:瑞士苏黎世市中心区的案例

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摘要

Cruising-for-parking is a common phenomenon in many urban areas worldwide. Properly understanding and mitigating cruising can reduce travel times, alleviate traffic congestion, and improve the local environment. Most of the existing studies estimating cruising traffic are based on empirical data and/or detailed simulation models. Both approaches have large data requirements, and the detailed simulation models tend to have high computational costs. In this paper, we present a case study of an area within the city of Zurich, Switzerland, using a recently proposed macroscopic model to analyze the current conditions of cruising-for-parking. The results are validated with empirical data. The macroscopic model, inspired by a bottleneck model, reproduces the dynamics of both, the parking and the traffic system, as well as their interactions. As such, it calculates the delays encountered by drivers while waiting for parking, and the impact of such delays on the overall traffic stream, which involves not only the searching traffic but also the through traffic. It is shown that the macroscopic parking model could, additionally, incorporate the data generated by agent-based models, cooperatively producing valid and trustworthy results of cruising estimations, while requiring comparatively few data inputs and relatively low computational costs. The study shows that in a small area of Zurich (0.28km(2)) with a demand of 2687 trips in a typical working day, cruising-for-parking generates 83h of additional travel time and 1038km of additional travel distance. Surprisingly, the worst conditions are observed at noon, corresponding to a maximum number of 30 searchers with an average search time of 13min. Additionally, four types of parking policies are discussed, and their potential impacts on traffic performance are either quantitatively or qualitatively illustrated. The four policies include: the adjustment of the parking supply, the adjustment of parking time controls, the adoption of dynamic parking charges, and the provision of parking forecasts.
机译:巡航停车是全球许多城市地区的常见现象。正确理解和减轻巡航可以减少旅行时间,减轻交通拥堵,改善当地环境。估计巡航流量的大多数研究基于经验数据和/或详细的仿真模型。两种方法都具有大的数据要求,详细的仿真模型往往具有高的计算成本。在本文中,我们在瑞士苏黎世市内的一个地区的案例研究,使用最近提出的宏观模型来分析巡航巡航条件。结果用经验数据验证。由瓶颈模型的启发的宏观模型再现了两者,停车和交通系统的动态,以及它们的交互。因此,它计算驱动程序在等待停车时遇到的延迟,以及这种延迟对整个流量流的影响,这不仅涉及搜索流量,还涉及到通过流量。结果表明,宏观停车模型可以另外包含由基于代理的模型生成的数据,协作地产生巡航估计的有效和可信的结果,同时需要相对较少的数据输入和相对较低的计算成本。该研究表明,在苏黎世的一小块面积(0.28km(2))中,在典型的工作日的需求下,巡航停车额外的行程时间和1038km的额外行程距离产生83小时。令人惊讶的是,中午观察到最糟糕的条件,对应于13分钟的平均搜索时间的最大30名搜索者。此外,讨论了四种类型的停车策略,并且它们对交通性能的潜在影响是定量或定性地说明的。四项政策包括:停车供应的调整,停车时间控制的调整,采用动态停车费,以及提供停车预测。

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