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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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    1. Mi Diao & Yi Zhu & Joseph Ferreira Jr & Carlo Ratti, 2016. "Inferring individual daily activities from mobile phone traces: A Boston example," Environment and Planning B, , vol. 43(5), pages 920-940, September.
    2. Gregory C. Chow & Linlin Niu, 2015. "Housing Prices in Urban China as Determined by Demand and Supply," Pacific Economic Review, Wiley Blackwell, vol. 20(1), pages 1-16, February.
    3. Chu-Chia Lin & Sue-Jing Lin, 1999. "An Estimation of Elasticities of Consumption Demand and Investment Demand for Owner-Occupied Housing in Taiwan : A Two-Period Model," International Real Estate Review, Global Social Science Institute, vol. 2(1), pages 110-125.
    4. Miller, Caroline & Savage, Ian, 2017. "Does the demand response to transit fare increases vary by income?," Transport Policy, Elsevier, vol. 55(C), pages 79-86.
    5. Daniel Preoţiuc-Pietro & Svitlana Volkova & Vasileios Lampos & Yoram Bachrach & Nikolaos Aletras, 2015. "Studying User Income through Language, Behaviour and Affect in Social Media," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    6. Adriaan Kalwij & Wiemer Salverda, 2007. "The effects of changes in household demographics and employment on consumer demand patterns," Applied Economics, Taylor & Francis Journals, vol. 39(11), pages 1447-1460.
    7. Han, Gain & Sohn, Keemin, 2016. "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 121-135.
    8. Eric Schenker & John Wilson, 1967. "The Use of Public Mass Transportation in the Major Metropolitan Areas of the United States," Land Economics, University of Wisconsin Press, vol. 43(3), pages 361-367.
    9. Long Cheng & Xuewu Chen & Shuo Yang, 2016. "An exploration of the relationships between socioeconomics, land use and daily trip chain pattern among low-income residents," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(4), pages 358-369, June.
    10. Shaojun Luo & Flaviano Morone & Carlos Sarraute & Matías Travizano & Hernán A. Makse, 2017. "Inferring personal economic status from social network location," Nature Communications, Nature, vol. 8(1), pages 1-7, August.
    11. Cuauhtemoc Anda & Alexander Erath & Pieter Jacobus Fourie, 2017. "Transport modelling in the age of big data," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 19-42, August.
    12. Holmgren, Johan, 2007. "Meta-analysis of public transport demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(10), pages 1021-1035, December.
    13. Chen Zhong & Michael Batty & Ed Manley & Jiaqiu Wang & Zijia Wang & Feng Chen & Gerhard Schmitt, 2016. "Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    14. Zhao, Pengjun & Lü, Bin & Roo, Gert de, 2011. "Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era," Journal of Transport Geography, Elsevier, vol. 19(1), pages 59-69.
    15. Daniel Fernández-Kranz & Mark Hon, 2006. "A Cross-Section Analysis of the Income Elasticity of Housing Demand in Spain: Is There a Real Estate Bubble?," The Journal of Real Estate Finance and Economics, Springer, vol. 32(4), pages 449-470, June.
    16. Wang, Donggen & Chai, Yanwei, 2009. "The jobs–housing relationship and commuting in Beijing, China: the legacy of Danwei," Journal of Transport Geography, Elsevier, vol. 17(1), pages 30-38.
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    1. 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.
    2. Hamed Faroqi & Mahmoud Mesbah & Jiwon Kim, 2020. "Modelling socioeconomic attributes of public transit passengers," Journal of Geographical Systems, Springer, vol. 22(4), pages 519-543, October.

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