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Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data

Author

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  • Yadi Zhu

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Feng Chen

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
    Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention, Beijing Jiaotong University, Beijing 100044, China
    School of Highway, Chang’an University, Xi’an 710064, China)

  • Ming Li

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Zijia Wang

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
    Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Socioeconomic attributes are essential characteristics of people, and many studies on economic attribute inference focus on data that contain user profile information. For data without user profiles, like smart card data, there is no validated method for inferring individual economic attributes. This study aims to bridge this gap by formulating a mobility to attribute framework to infer passengers’ economic attributes based on the relationship between individual mobility and personal attributes. This framework integrates shop consumer prices, house prices, and smart card data using three steps: individual mobility extraction, location feature identification, and economic attribute inference. Each passenger’s individual mobility is extracted by smart card data. Economic features of stations are described using house price and shop consumer price data. Then, each passenger’s comprehensive consumption indicator set is formulated by integrating these data. Finally, individual economic levels are classified. From the case study of Beijing, commuting distance and trip frequency using the metro have a negative correlation with passengers’ income and the results confirm that metro passengers are mainly in the low- and middle-income groups. This study improves on passenger information extracted from data without user profile information and provides a method to integrate multisource big data mining for more information.

Suggested Citation

  • Yadi Zhu & Feng Chen & Ming Li & Zijia Wang, 2018. "Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4178-:d:182516
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    References listed on IDEAS

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    2. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.

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