Research on the Stability of Public Transit Passenger Travel Behavior Based on Smart Card Data

With the widespread use of the Smart Card Automatic Fare Collection System, most public transit passengers pay by smart cards. In this paper, we use four consecutive weeks' smart card data of Shenzhen to analyze passenger travel behavior. Traditionally, we divide transit passengers into adult, student, and senior citizen groups, according to the empirical predictions of social economics without considering user travel behavior, which is poor efficiency. After analyzing transit passenger daily travel behavior features, we propose a method to classify users into different types based on weekly boarding frequency. This can reflect a much more accurate and meaningful understanding of transit travel demand. Furthermore, we give a fuzzy comprehensive evaluation model to evaluate the stability of different transit passenger groups based on their weekly boarding frequency, travel time, trip route, and transaction amount. This research can help public transit providers find their most important customers and make effective policy to meet passenger needs.