Public Transit Passenger Profiling by Using Large-Scale Smart Card Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
The term “user persona” recently has become more popular and can reflect the characteristics and needs of each user. To analyze the individual characteristics of each passenger in order to better implement targeted transportation policies, a method to mine travel profiles from each passenger based on their smart card transaction records is proposed, mainly mining six labels of passengers: activity, loyalty, whether they are stable commuters, where they live, where they work, and how they prefer to travel. A case study was conducted in the Huitian area of Beijing, the largest community in Asia, which demonstrated the high practicality and accuracy of the passenger profiling method. The results show that 33.06% of passengers were given the stable commuter label and were more likely to have high activity and loyalty labels, whereas 66.94% of other passengers were more likely to have low activity and loyalty labels; 88.9% of passengers were given the residence label and 67.71% of stable commuters received the workplace label, with an accuracy rate of over 90% according to a travel survey. In addition, the application of passenger labels during the design of demand responsive transit (DRT) was discussed to illustrate how to use the labels to improve the efficiency of DRT. The proposed passenger profiling method is applicable to the data mining of passenger travel labels in a simple and accurate way, and can help public transport service providers and researchers to study individual passenger characteristics and provide a theoretical basis for transit network planning and personalization measures.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (indicator calculation code and labels identification code).
Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 2022JBMC056) and the National Natural Science Foundation of China (Grant No. 71901018). All data used for this study were provided by the Beijing Transport Institute.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 26, 2022
Accepted: Nov 14, 2022
Published online: Jan 24, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 24, 2023
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