Shared Mobility Services (SMS), e.g., Demand-Responsive Transit (DRT) or ride-sharing, can improve mobility in low-density areas, often poorly served by conventional Public Transport (PT). Such improvement is mostly quantified via basic performance indicators, like wait or travel time. However, accessibility indicators, measuring the ease of reaching surrounding opportunities (e.g., jobs, schools, shops, ...), would be a more comprehensive indicator. To date, no method exists to quantify the accessibility of SMS based on empirical measurements. Indeed, accessibility is generally computed on graph representations of PT networks, but SMS are dynamic and do not follow a predefined network. We propose a spatial-temporal statistical method that takes as input observed trips of a SMS acting as a feeder for PT and summarized such trips in a graph. On such a graph, we compute classic accessibility indicators. We apply our method to a MATSim simulation study concerning DRT in Paris-Saclay.

翻译：共享出行服务（SMS），例如需求响应式公交（DRT）或拼车服务，能够提升传统公共交通（PT）服务不足的低密度区域的移动性。这种提升通常通过基本性能指标（如等待时间或行程时间）来量化。然而，衡量到达周边机会（如工作岗位、学校、商店等）便利程度的可达性指标，将是更具综合性的评价标准。迄今为止，尚无基于实证数据量化共享出行服务可达性的方法。事实上，可达性通常基于公共交通网络的图表示进行计算，但共享出行服务具有动态性，且不遵循预定义的线路网络。我们提出了一种时空统计方法，该方法以作为公共交通接驳工具的共享出行服务的观测行程为输入，并将这些行程汇总为图结构。在此图结构基础上，我们计算经典的可达性指标。我们将该方法应用于一项关于巴黎萨克雷地区需求响应式公交的MATSim仿真研究。