LAUSR

Automatic fare collection (AFC) system archives massive and continuous trip information for each cardholder. Mining the smart card transaction data from AFC system brings new opportunities for travel behavior and demand modeling. This study focuses on detecting the home location and trip purposes for subway passengers (cardholders), based on the internal temporal–spatial relationship within multi-day smart card transaction data. A center-point based algorithm is proposed to infer the home location for each cardholder. In addition, a rule-based approach using the individual properties (home location and card type) of cardholders and the travel information (time and space) of each trip is established for trip purpose identification. The smart card data from Beijing subway in China is used to validate the effectiveness of the proposed approaches. Results show that 88.7% of passengers’ home locations and four types of trip purposes (six subtypes) can be detected effectively by mining the card transaction data in one week. The city-wide home location distribution of Beijing subway passengers, and travel behavior with different trip purposes are analyzed. This study provides us a novel and low-cost way for travel behavior and demand research.