Identifying primary public transit corridors using multi-source big transit data

作者：Tong Zhang, Yicong Li, Hui Yang, Chenrong Cui, Jing Li, Qinghua Qiao · 发表于：International Journal of Geographical Information Systems · 年份：2018 · DOI：10.1080/13658816.2018.1554812 · 被引用次数：24 · 研究领域：Human Mobility and Location-Based Analysis、Traffic Prediction and Management Techniques、Transportation Planning and Optimization

Effective public transit planning needs to address realistic travel demands, which can be illustrated by corridors across major residential areas and activity centers. It is vital to identify public transit corridors that contain the most significant transit travel demand patterns. We propose a two-stage approach to discover primary public transit corridors at high spatio-temporal resolutions using massive real-world smart card and bus trajectory data, which manifest rich transit demand patterns over space and time. The first stage was to reconstruct chained trips for individual passengers using multi-source massive public transit data. In the second stage, a shared-flow clustering algorithm was developed to identify public transit corridors based on reconstructed individual transit trips. The proposed approach was evaluated using transit data collected in Shenzhen, China. Experimental results demonstrated that the proposed approach is a practical tool for extracting time-varying corridors for many potential applications, such as transit planning and management.