PDF(Pubmed)
Abstract：
Several cities around the world rely on urban rail transit systems composed of interconnected lines, serving massive numbers of passengers on a daily basis. Accessing the location of passengers is essential to ensure the efficient and safe operation and planning of these systems. However, passenger route choices between origin and destination pairs are variable, depending on the subjective perception of travel and waiting times, required transfers, convenience factors, and on-site vehicle arrivals. This work proposes a robust methodology to estimate passenger route choices based only on automated fare collection data, i.e. without privacy-invasive sensors and monitoring devices. Unlike previous approaches, our method does not require precise train timetable information or prior route choice models, and is robust to unforeseen operational events like malfunctions and delays. Train arrival times are inferred from passenger volume spikes at the exit gates, and the likelihood of eligible routes per passenger estimated based on the alignment between vehicle location and the passenger timings of entrance and exit. Applying this approach to automated fare collection data in Lisbon, we find that while in most cases passengers preferred the route with the least transfers, there were a significant number of cases where the shorter distance was preferred. Our findings are valuable for decision support among rail operators in various aspects such as passenger traffic bottleneck resolution, train allocation and scheduling, and placement of services.
摘要：
世界上几个城市都依赖由互联线路组成的城市轨道交通系统，每天为大量乘客提供服务。访问乘客的位置对于确保这些系统的高效和安全运行和规划至关重要。然而,起点和目的地对之间的乘客路线选择是可变的，根据对旅行和等待时间的主观感知，所需的转移，便利因素，和现场车辆到达。这项工作提出了一种稳健的方法来估计仅基于自动票价收集数据的乘客路线选择，即没有隐私侵入式传感器和监控设备。与以前的方法不同，我们的方法不需要精确的列车时刻表信息或先前的路线选择模型，并且对故障和延误等不可预见的操作事件非常强大。列车到达时间是根据出口门的客运量峰值推断的，以及根据车辆位置与进出乘客时间之间的对齐方式估计的每位乘客的合格路线的可能性。将这种方法应用于里斯本的自动票价收集数据，我们发现，虽然在大多数情况下，乘客更喜欢换乘最少的路线，有相当数量的病例首选较短距离。我们的发现对于铁路运营商在客运瓶颈解决等各个方面的决策支持非常有价值。列车分配和调度，和服务的安置。
