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Macroscopic Modeling of On-Street and Garage Parking: Impact on Traffic Performance

机译:沿着街道和车库停车的宏观造型:对交通业绩的影响

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The short-term interactions between on-street and garage parking policies and the associated parking pricing can be highly influential to the searching-for-parking traffic and the overall traffic performance in the network. In this paper, we develop a macroscopic on-street and garage parking decision model and integrate it into a traffic system with an on-street and garage parking search model over time. We formulate an on-street and garage parking-state-based matrix that describes the system dynamics of urban traffic based on different parking-related states and the number of vehicles that transition through each state in a time slice. This macroscopic modeling approach is based on aggregated data at the network level over time. This leads to data collection savings and a reduction in computational costs compared to most of the existing parking/traffic models. This easy to implement methodology can be solved with a simple numerical solver. All parking searchers face the decision to drive to a parking garage or to search for an on-street parking space in the network. This decision is affected by several parameters including the on-street and garage parking fees. Our model provides a preliminary idea for city councils regarding the short-term impacts of on-street and garage parking policies (e.g., converting on-street parking to garage parking spaces, availability of garage usage information to all drivers) and parking pricing policies on: searching-for-parking traffic (cruising), the congestion in the network (traffic performance), the total driven distance (environmental impact), as well as the revenue created for the city by the hourly on-street and garage parking fee rates. This model can be used to analyze how on-street and garage parking policies can affect traffic performance; and how traffic performance can affect the decision to use on-street or garage parking. The proposed methodology is illustrated with a case study of an area within the city of Zurich, Switzerland.
机译:路上和车库停车场之间的短期互动和相关的停车定价可能对搜索停车的交通和网络的整体交通绩效受到高度影响力。在本文中,我们开发了宏观街道和车库停车决策模型,并将其整合到交通系统中,随着时间的推移,带上街道和车库停车搜索模式。我们制定了一条街头和车库停车状态的矩阵,该矩阵描述了基于不同的停车场相关态的城市流量的系统动态,以及在时间切片中通过每个状态过渡的车辆数量。此宏观建模方法基于网络级别随时间的聚合数据。与大多数现有停车/流量模型相比,这导致数据收集节省和计算成本的降低。这种易于实现的方法可以用简单的数字求解器来解决。所有停车搜索者都面临着驾驶到停车库的决定,或搜索网络中的街边停车位。该决定受到几个参数的影响,包括路上和车库停车费。我们的模型为城市议会提供了关于路上和车库停车场的短期影响的初步理念(例如,将路上停车到车库停车位转换为车库停车位,车库使用情况信息给所有司机)和停车定价政策:停车停车流量(巡航),网络中的拥塞(交通绩效),总驱动距离(环境影响),以及由每小时街道和车库停车费用为城市创造的收入。该模型可用于分析路上和车库停车策略如何影响交通效果;以及流量绩效如何影响在街上或车库停车的决定。拟议的方法是用瑞士苏黎世市内的一个区域的案例研究。

著录项

  • 来源
    《Journal of Advanced Transportation》 |2019年第4期|5793027.1-5793027.20|共20页
  • 作者

    Jakob Manuel; Menendez Monica;

  • 作者单位

    Swiss Fed Inst Technol Inst Transport Planning & Syst IVT Zurich Switzerland;

    NYUAD Div Engn Civil & Urban Engn Abu Dhabi U Arab Emirates|NYU Tandon Sch Engn Civil & Urban Engn New York NY USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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