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Navigating the transit network: Understanding riders’ information seeking behavior using trip planning data

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  • Li, Lisa
  • Shalaby, Amer

Abstract

Relevant and timely provision of transit information advises travelers of the route options available to them, allows riders to plan the timing of their trips, and helps mitigate the adverse impacts of unexpected disruptions. This in turn can improve the experience and retention of current riders and help attract new ones. While previous studies have relied primarily on data collected from surveys to understand people’s use of transit information services, this paper uses backend data from Transit, a multimodal trip planner smartphone application (app), to analyze usage patterns in Calgary over the span of six months. A clustering analysis was initially performed to gain an understanding of trip search characteristics. The results show that most searches were made for short distanced trips. Additionally, panel data models were estimated to investigate the relationship between search frequency and transit service characteristics, temporal factors, built environment, weather and sociodemographic attributes. The model results reveal that people seek out transit information the most during times of uncertainty, as poor reliability and service disruptions were shown to increase itinerary searches markedly. Furthermore, there was found to be a significant increase in searches after the network was restructured and three bus rapid transit (BRT) lines were introduced. These findings can help agencies determine the best way to deliver information to people and gain insights into travel behavior.

Suggested Citation

  • Li, Lisa & Shalaby, Amer, 2024. "Navigating the transit network: Understanding riders’ information seeking behavior using trip planning data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:transa:v:185:y:2024:i:c:s0965856424001447
    DOI: 10.1016/j.tra.2024.104096
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    References listed on IDEAS

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    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
    2. Lorelei Schmitt & Graham Currie & Alexa Delbosc, 2015. "Lost in transit? Unfamiliar public transport travel explored using a journey planner web survey," Transportation, Springer, vol. 42(1), pages 101-122, January.
    3. Hilbe,Joseph M., 2014. "Modeling Count Data," Cambridge Books, Cambridge University Press, number 9781107028333, September.
    4. Mohamed Wahba & Amer Shalaby, 2014. "Learning-based framework for transit assignment modeling under information provision," Transportation, Springer, vol. 41(2), pages 397-417, March.
    5. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    6. Schmitt, Lorelei & Currie, Graham & Delbosc, Alexa, 2013. "Measuring the impact of unfamiliar transit travel using a university access survey," Transport Policy, Elsevier, vol. 30(C), pages 301-307.
    7. Mulley, Corinne & Clifton, Geoffrey Tilden & Balbontin, Camila & Ma, Liang, 2017. "Information for travelling: Awareness and usage of the various sources of information available to public transport users in NSW," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 111-132.
    8. Wooldridge, Jeffrey M., 1999. "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, Elsevier, vol. 90(1), pages 77-97, May.
    9. Tammy Harris & Joseph M. Hilbe & James W. Hardin, 2014. "Modeling count data with generalized distributions," Stata Journal, StataCorp LP, vol. 14(3), pages 562-579, September.
    10. Farag, Sendy & Lyons, Glenn, 2012. "To use or not to use? An empirical study of pre-trip public transport information for business and leisure trips and comparison with car travel," Transport Policy, Elsevier, vol. 20(C), pages 82-92.
    11. Wen Hua & Ghim Ping Ong, 2018. "Effect of information contagion during train service disruption for an integrated rail-bus transit system," Public Transport, Springer, vol. 10(3), pages 571-594, December.
    12. Peftitsi, Soumela & Jenelius, Erik & Cats, Oded, 2022. "Modeling the effect of real-time crowding information (RTCI) on passenger distribution in trains," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 354-368.
    13. Godwin Yeboah & Caitlin D. Cottrill & John D. Nelson & David Corsar & Milan Markovic & Peter Edwards, 2019. "Understanding factors influencing public transport passengers’ pre-travel information-seeking behaviour," Public Transport, Springer, vol. 11(1), pages 135-158, June.
    14. Dziekan, Katrin & Kottenhoff, Karl, 2007. "Dynamic at-stop real-time information displays for public transport: effects on customers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(6), pages 489-501, July.
    Full references (including those not matched with items on IDEAS)

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