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（如需求响应式公交或拼车）能够改善传统公共交通覆盖不足的低密度区域的出行条件。此类提升通常通过基本性能指标（如等待时间或行程时间）进行量化。然而，衡量到达周边机会（如工作岗位、学校、商店等）便利性的可达性指标将更具综合性。目前尚不存在基于实测数据量化共享出行服务可达性的方法。这是因为可达性通常基于公交网络图模型计算，但共享出行服务具有动态性且不遵循预设网络。本文提出一种时空统计方法，以共享出行服务作为公交接驳的实测出行轨迹为输入，将此类出行行为归纳为图结构，并基于该图计算经典可达性指标。我们将该方法应用于巴黎-萨克雷地区需求响应式公交的MATSim仿真研究。