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Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders

Author

Listed:
  • Watkins, Kari Edison
  • Ferris, Brian
  • Borning, Alan
  • Rutherford, G. Scott
  • Layton, David

Abstract

In order to attract more choice riders, transit service must not only have a high level of service in terms of frequency and travel time but also must be reliable. Although transit agencies continuously work to improve on-time performance, such efforts often come at a substantial cost. One inexpensive way to combat the perception of unreliability from the user perspective is real-time transit information. The OneBusAway transit traveler information system provides real-time next bus countdown information for riders of King County Metro via website, telephone, text-messaging, and smart phone applications. Although previous studies have looked at traveler response to real-time information, few have addressed real-time information via devices other than public display signs. For this study, researchers observed riders arriving at Seattle-area bus stops to measure their wait time while asking a series of questions, including how long they perceived that they had waited. The study found that for riders without real-time information, perceived wait time is greater than measured wait time. However, riders using real-time information do not perceive their wait time to be longer than their measured wait time. This is substantiated by the typical wait times that riders report. Real-time information users say that their average wait time is 7.5Â min versus 9.9Â min for those using traditional arrival information, a difference of about 30%. A model to predict the perceived wait time of bus riders was developed, with significant variables that include the measured wait time, an indicator variable for real-time information, an indicator variable for PM peak period, the bus frequency in buses per hour, and a self-reported typical aggravation level. The addition of real-time information decreases the perceived wait time by 0.7Â min (about 13%). A critical finding of the study is that mobile real-time information reduces not only the perceived wait time, but also the actual wait time experienced by customers. Real-time information users in the study wait almost 2Â min less than those arriving using traditional schedule information. Mobile real-time information has the ability to improve the experience of transit riders by making the information available to them before they reach the stop.

Suggested Citation

  • Watkins, Kari Edison & Ferris, Brian & Borning, Alan & Rutherford, G. Scott & Layton, David, 2011. "Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 839-848, October.
  • Handle: RePEc:eee:transa:v:45:y:2011:i:8:p:839-848
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    References listed on IDEAS

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    1. Poudenx, Pascal, 2008. "The effect of transportation policies on energy consumption and greenhouse gas emission from urban passenger transportation," Transportation Research Part A: Policy and Practice, Elsevier, pages 901-909.
    2. Lyons, Glenn & Urry, John, 2005. "Travel time use in the information age," Transportation Research Part A: Policy and Practice, Elsevier, pages 257-276.
    3. 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, pages 489-501.
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    Citations

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    Cited by:

    1. Yang Chen & Arturo Ardila-Gomez & Gladys Frame, 2016. "Achieving Energy Savings by Intelligent Transportation Systems Investments in the Context of Smart Cities," World Bank Other Operational Studies 24740, The World Bank.
    2. Marina Lagune-Reutler & Andrew Guthrie & Yingling Fan & David Levinson, 2015. "Transit Riders’ Perception of Waiting Time and Stops’ Surrounding Environments," Working Papers 000142, University of Minnesota: Nexus Research Group.
    3. Kuo, Pei-Fen & Lord, Dominique, 2013. "Accounting for site-selection bias in before–after studies for continuous distributions: Characteristics and application using speed data," Transportation Research Part A: Policy and Practice, Elsevier, pages 256-269.
    4. Fan, Yingling & Guthrie, Andrew & Levinson, David, 2016. "Waiting time perceptions at transit stops and stations: Effects of basic amenities, gender, and security," Transportation Research Part A: Policy and Practice, Elsevier, pages 251-264.
    5. Cats, Oded & Loutos, Gerasimos, 2016. "Evaluating the added-value of online bus arrival prediction schemes," Transportation Research Part A: Policy and Practice, Elsevier, pages 35-55.
    6. repec:eee:transa:v:101:y:2017:i:c:p:111-132 is not listed on IDEAS
    7. Morfoulaki, Maria & Myrovali, Glikeria & Kotoula, Kornilia, 2015. "Increasing the attractiveness of public transport by investing in soft ICT based measures: Going from words to actions under an austerity backdrop – Thessaloniki's case, Greece," Research in Transportation Economics, Elsevier, vol. 51(C), pages 40-48.
    8. Kari Watkins & Alan Borning & G. Rutherford & Brian Ferris & Brian Gill, 2013. "Attitudes of bus operators towards real-time transit information tools," Transportation, Springer, pages 961-980.
    9. Frei, Charlotte & Mahmassani, Hani S. & Frei, Andreas, 2015. "Making time count: Traveler activity engagement on urban transit," Transportation Research Part A: Policy and Practice, Elsevier, pages 58-70.
    10. Cats, Oded & Loutos, Gerasimos, 2013. "Real-time bus arrival information system: an empirical evaluation," Working papers in Transport Economics 2013:25, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    11. Brakewood, Candace & Barbeau, Sean & Watkins, Kari, 2014. "An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, Florida," Transportation Research Part A: Policy and Practice, Elsevier, pages 409-422.

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