Group Biometry, Biosignals, Security, and Smart Mobility, Departamento de Matemática Aplicada a las Tecnologías de la Información y las Comunicaciones, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
Sensors (Basel). 2021 Dec 21;22(1):17. doi: 10.3390/s22010017.
Transport agencies require accurate and updated information about public transport systems for the optimal decision-making processes regarding design and operation. In addition to assessing topology and service components, users' behaviors must be considered. To this end, a data-driven performance evaluation based on passengers' actual routes is key. Automatic fare collection platforms provide meaningful smart card data (SCD), but these are incomplete when gathered by entry-only systems. To obtain origin-destination (OD) matrices, we must manage complete journeys. In this paper, we use an adapted trip chaining method to reconstruct incomplete multi-modal journeys by finding spatial similarities between the outbound and inbound routes of the same user. From this dataset, we develop a performance evaluation framework that provides novel metrics and visualization utilities. First, we generate a space-time characterization of the overall operation of transport networks. Second, we supply enhanced OD matrices showing mobility patterns between zones and average traversed distances, travel times, and operation speeds, which model the real efficacy of the public transport system. We applied this framework to the Comunidad de Madrid (Spain), using 4 months' worth of real SCD, showing its potential to generate meaningful information about the performance of multi-modal public transport systems.
交通机构需要准确和最新的公共交通系统信息，以便在设计和运营方面做出最佳决策。除了评估拓扑结构和服务组件外，还必须考虑用户的行为。为此，基于乘客实际路线的数据分析驱动的性能评估是关键。自动收费平台提供有意义的智能卡数据 (SCD)，但仅通过入口系统收集时这些数据并不完整。为了获得起点-终点 (OD) 矩阵，我们必须管理完整的旅程。在本文中，我们使用经过调整的行程链接方法，通过找到同一用户出站和进站路线之间的空间相似性来重建不完整的多模式行程。从这个数据集，我们开发了一个性能评估框架，提供了新颖的指标和可视化工具。首先，我们生成了对整个交通网络运行的时空特征描述。其次，我们提供了增强的 OD 矩阵，显示了区域之间的移动模式以及平均穿越距离、旅行时间和运行速度，这些模型反映了公共交通系统的实际效果。我们将该框架应用于西班牙马德里社区，使用了 4 个月的真实 SCD，展示了其生成有关多模式公共交通系统性能的有意义信息的潜力。
