On-Demand Public Transit: A Markovian Continuous Approximation Model
On-Demand Public Transit: A Markovian Continuous Approximation Model
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DOI：
10.1287/trsc.2021.1063
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发表时间：
2021-07
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影响因子：
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通讯作者：
Daniel F. Silva;A. Vinel;Bekircan Kirkici
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作者：
Daniel F. Silva;A. Vinel;Bekircan Kirkici
随着移动的技术的最新进展，世界各地的公共交通机构已开始积极试验新的交通模式，其中许多可以被描述为按需公共交通。这类系统的设计和有效运作可能特别具有挑战性，因为它们往往需要仔细平衡需求量与资源可用性。我们提出了一个家庭的按需公共交通模型，结合联合收割机连续近似方法与马尔可夫过程。我们的目标是开发一个易于处理的方法来评估和预测系统的性能，特别是专注于获得性能指标的概率分布。然后，这些信息可以用于资本规划，例如车队规模，合同和驾驶员调度等。我们提出了一个程式化的单车辆模型的第一英里操作的解析解。然后，我们描述了几个扩展的基础模型，包括两种方法的多车辆的情况下。我们使用计算实验来说明性能指标的输入的影响，并比较不同的交通方式。最后，我们包括一个案例研究，使用收集的数据，从一个真实世界的试点按需公共交通项目在美国的一个主要大都市地区，展示所提出的模型可以用来预测系统的性能和支持决策。
With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.