Abstract
The smart card-based automated fare collection (AFC) system has become the main method for collecting urban bus and rail transit fares in many cities worldwide. Such smart card technologies provide new opportunities for transportation data collection since the transaction data obtained through AFC system contains a significant amount of archived information which can be gathered and leveraged to help estimate public transit origin–destination matrices. Boarding location detection is an important step particularly when there is no automatic vehicle location (AVL) system or GPS information in the database in some cases. With the analysis of raw data without AVL information in this paper, an algorithm for trip direction detection is built and the directions for any bus in operation can be confirmed. The transaction interval between each adjacent record will also be analyzed to detect the boarding clusters for all trips in sequence. Boarding stops will then be distributed with the help of route information and operation schedules. Finally, the feasibility and practicality of the methodology are tested using the bus transit smart card data collected in Guangzhou, China.
Highlights
As smart card (SC) fare system has been widely implemented in the world today, smart card data (SCD) plays an important role in the regional transportation system management
The smart card-based automated fare collection (AFC) system has become the main method for collecting urban bus and rail transit fares in many cities worldwide
Such smart card technologies provide new opportunities for transportation data collection since the transaction data obtained through AFC system contains a significant amount of archived information which can be gathered and leveraged to help estimate public transit origin–destination matrices
Summary
As smart card (SC) fare system has been widely implemented in the world today, smart card data (SCD) plays an important role in the regional transportation system management. With the consideration of inconvenience of traditional travel survey methods, SCD-based research has become more and more popular recently. Devillaine et al [2] presented a methodology to categorize trips by different purposes after the detection and estimation of the locations of destination, trip time, activity duration and card type by utilizing smart card databases. Long et al [4] conducted an analysis based on the travel time characteristics of four major passenger groups (‘early birds,’ ‘night owls,’ ‘tireless itinerants’ and ‘recurring itinerants’) with the help of household survey data. Kieu et al [5] presented a study utilizing three levels of density-based spatial clustering of application with noise (DBSCAN) algorithm for mining transit users’ travel regularity
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