On-demand mobility services – autonomous as well as human-driven – promise to transform transportation and urban living. Yet, designing such services involves an array of challenges: from demand data acquisition and processing, to building a versatile simulation framework, to finally evaluating a variety of service configurations and making recommendations. This article illustrates the process of designing and analyzing various types of on-demand mobility services using the City of Chicago, Illinois, as a test case. We start by surveying and categorizing a large number of articles on simulation-based design of on-demand mobility services. This is followed by an extensive experimental study, which analyzes how different service designs perform under real transportation demand patterns in terms of key performance indicators such as demand acceptance rate, excess ride time, deviation from the desired pickup time, vehicle occupancy, and various fleet efficiency metrics. This type of analysis helps mobility service providers and public transit authorities gain insights into the interplay of the different passenger- and fleet-related key performance indicators. Finally, we introduce the fundamental ridesharing diagram, which quantifies the relationship between passenger convenience and fleet efficiency, and helps identify socially optimal service designs.

随需应变的移动服务-自主的和人为驱动的-都有望改变交通和城市生活。然而，设计此类服务涉及一系列挑战：从需求数据获取和处理到构建通用的仿真框架，最后评估各种服务配置并提出建议。本文以伊利诺伊州芝加哥市为例，说明了设计和分析各种类型的按需移动服务的过程。我们首先调查和分类大量有关基于模拟的按需移动服务设计的文章。随后进行了广泛的实验研究，它根据关键性能指标（例如需求接受率，超长乘车时间，偏离所需接送时间，车辆占用率和各种车队效率指标）来分析不同服务设计在实际运输需求模式下的性能。这种类型的分析可帮助出行服务提供商和公共交通管理部门深入了解与乘客和车队相关的关键绩效指标之间的相互影响。最后，我们介绍基本乘车图，该图量化了乘客便利性与车队效率之间的关系，并帮助确定了社会上最佳的服务设计。