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Activity detection and transfer identification for public transit fare card data

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  • Neema Nassir
  • Mark Hickman
  • Zhen-Liang Ma

Abstract

This paper contributes to the emerging literature analysing public transit fare card data for a better understanding of passengers’ mobility patterns and path choices. A new heuristic is proposed to estimate the stop-level origins and destinations by detecting the traveller activities in the observed transactions in a fare card dataset. The main focus in this research is estimating the actual passenger trajectories for multi-leg journeys. If the fare card dataset includes both boarding and alighting information of each transaction, the main challenge is the estimation of origins and destinations by distinguishing the transfer interchanges from the activity locations. Built on commonly used criteria for identifying transfers, this paper proposes a new method to improve the accuracy of short activity detection to estimate the passengers’ true origins and destinations. The set of criteria in this research is based on the proposed concept of “off-optimality” for a more accurate identification of short/hidden activities within the labelled transfers. The measure of off-optimality incorporates different variables of the transit service between the given journey ends (including alternative paths and routes, service headways, walk distances/times, transfer points, etc.) and reflects those into a simple quantity to improve the accuracy of estimation. In addition to off-optimality, the time gap between two transactions, the total travel time, and the circuity of the path trajectories are other variables that are used in distinguishing the true transfers from activities. The proposed set of criteria is calibrated using a large endogenous set of fare card data from Brisbane, Australia, and is validated using a set of transit journeys and reported activities from a household travel survey. The results are presented for the fare card data from Brisbane collected in March 2013. The validation and case study results affirm the effectiveness of the proposed criteria in short activity detection. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Neema Nassir & Mark Hickman & Zhen-Liang Ma, 2015. "Activity detection and transfer identification for public transit fare card data," Transportation, Springer, vol. 42(4), pages 683-705, July.
    • Handle: RePEc:kap:transp:v:42:y:2015:i:4:p:683-705
    DOI: 10.1007/s11116-015-9601-6
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    References listed on IDEAS

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    1. Lee, Sanggu & Hickman, Mark & Tong, Daoqin, 2013. "Development of a temporal and spatial linkage between transit demand and land-use patterns," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(2), pages 33-46.
    2. Takahiko Kusakabe & Takamasa Iryo & Yasuo Asakura, 2010. "Estimation method for railway passengers’ train choice behavior with smart card transaction data," Transportation, Springer, vol. 37(5), pages 731-749, September.
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    3. Zhou, Jiangping & Sipe, Neil & Ma, Zhenliang & Mateo-Babiano, Derlie & Darchen, Sébastien, 2019. "Monitoring transit-served areas with smartcard data: A Brisbane case study," Journal of Transport Geography, Elsevier, vol. 76(C), pages 265-275.
    4. Liu, Jiangtao & Zhou, Xuesong, 2019. "Observability quantification of public transportation systems with heterogeneous data sources: An information-space projection approach based on discretized space-time network flow models," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 302-323.
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    6. Wang, Ding & Tayarani, Mohammad & Yueshuai He, Brian & Gao, Jingqin & Chow, Joseph Y.J. & Oliver Gao, H. & Ozbay, Kaan, 2021. "Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 151-170.
    7. Egu, Oscar & Bonnel, Patrick, 2020. "How comparable are origin-destination matrices estimated from automatic fare collection, origin-destination surveys and household travel survey? An empirical investigation in Lyon," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 267-282.
    8. 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.
    9. Nassir, Neema & Hickman, Mark & Malekzadeh, Ali & Irannezhad, Elnaz, 2016. "A utility-based travel impedance measure for public transit network accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 26-39.
    10. Jiangyue Wu & Jiangping Zhou & Hanxi Ma, 2022. "Revisiting the valuable locales in our cities? Visualizing social interaction potential around metro station areas in Wuhan, China," Environment and Planning A, , vol. 54(3), pages 433-436, May.
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    12. Yi Zhu, 2020. "Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore," Transportation, Springer, vol. 47(6), pages 2703-2730, December.
    13. Salih, Samal Hama & Lee, Jinwoo (Brian), 2022. "Measuring transit accessibility: A dispersion factor to recognise the spatial distribution of accessible opportunities," Journal of Transport Geography, Elsevier, vol. 98(C).
    14. Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
    15. Ahmad Tavassoli & Mahmoud Mesbah & Mark Hickman, 2020. "Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network," Transportation, Springer, vol. 47(5), pages 2133-2156, October.
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    18. Zhou, Jiangping & Murphy, Enda, 2019. "Day-to-day variation in excess commuting: An exploratory study of Brisbane, Australia," Journal of Transport Geography, Elsevier, vol. 74(C), pages 223-232.
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    21. Zhao, Shuangming & Zhao, Pengxiang & Cui, Yunfan, 2017. "A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 143-157.

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