Boarding stop inference with uncertain relationship between bus vehicles and mobile smart card readers

Abstract

Boarding stop inference for bus passengers is essential for the improvement of bus transit services. Previous studies mainly focus on matching the bus trajectories with the bus stop locations, while the relationship between smart card readers—which collect the smart card data—and bus vehicles is usually given. However, uncertainties arise in practical applications regarding the matching of vehicles and card readers. To tackle this challenge, in this study, a data-driven approach is proposed to dig into the spatiotemporal features of passengers' smart card data and bus vehicle operations. A weighted bipartite graph algorithm is developed to match the smart card readers with the bus vehicles automatically. To verify the feasibility and effectiveness of the proposed approach, a case study is conducted on the Bus Anhong Line in Shanghai, China. The inferred results of boarding stops are compared with the data from passenger counting sensors installed in the bus vehicles. The matching accuracy rate achieves 0.9539, which validates the effectiveness of the proposed matching model. In addition, the inferred data are used to present the spatiotemporal patterns of boarding passengers and identify high-demand bus stops.