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Estimating the residence zone of frequent public transport users to make travel pattern and time use analysis

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  • Amaya, Margarita
  • Cruzat, Ramón
  • Munizaga, Marcela A.

Abstract

Public transport systems with electronic fare collection devices continuously store data related to trips taken by users, which contain valuable information for planning and policy analysis. However, if the card is not personalized, there is no socioeconomic information available, which imposes a limitation on the types of analysis that can be performed. This work presents a simple method to estimate the residence zone of card users, which will allow socioeconomic variables to be estimated, thereby enriching the analytical possibilities. The method, which is based on the observation of morning transactions of frequent users, is applied to a sample of over 2 million cards. The method is evaluated using a sample from the Santiago ODS where users declared their card id and also declared their home address. A sample of 888,970 cards that are observed at least three days in a week and show spatial regularity for the morning transaction is used for zone of residence estimation and analysis of travel patterns and time use. The results show that users who live in the city center or in the wealthier East zone experience lower travel time, spend more time at home and less time at work.

Suggested Citation

  • Amaya, Margarita & Cruzat, Ramón & Munizaga, Marcela A., 2018. "Estimating the residence zone of frequent public transport users to make travel pattern and time use analysis," Journal of Transport Geography, Elsevier, vol. 66(C), pages 330-339.
    • Handle: RePEc:eee:jotrge:v:66:y:2018:i:c:p:330-339
    DOI: 10.1016/j.jtrangeo.2017.10.017
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    References listed on IDEAS

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    2. Hossain, Sanjana & Habib, Khandker Nurul, 2022. "Inferring origin and destination zones of transit trips through fusion of smart card transactions, travel surveys, and land-use data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 267-284.
    3. Guofa Li & Yuan Liao & Qiangqiang Guo & Caixiong Shen & Weijian Lai, 2021. "Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016," IJERPH, MDPI, vol. 18(3), pages 1-24, January.
    4. Munizaga, Marcela A. & Gschwender, Antonio & Gallegos, Nestor, 2020. "Fare evasion correction for smartcard-based origin-destination matrices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 307-322.
    5. Gramsch, Benjamin & Guevara, C. Angelo & Munizaga, Marcela & Schwartz, Daniel & Tirachini, Alejandro, 2022. "The effect of dynamic lockdowns on public transport demand in times of COVID-19: Evidence from smartcard data," Transport Policy, Elsevier, vol. 126(C), pages 136-150.
    6. Yuhui Zhao & Xinyan Zhu & Wei Guo & Bing She & Han Yue & Ming Li, 2019. "Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 11(21), pages 1-17, November.

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