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Trade-offs between headway, fare, and real-time bus information under different weather conditions

Author

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  • Md Matiur Rahman

    (University of Calgary)

  • Lina Kattan

    (University of Calgary)

  • S. C. Wirasinghe

    (University of Calgary)

Abstract

Given the increasing interest in real-time bus information, quantifying the value of such information from a user’s perspective is useful for transport modelers and service planners. Although a number of studies have investigated several other aspects of real-time bus information systems, there is a lack of studies that compare the disutility associated with the bus headway of a scheduled arrival information system and that of a real-time information system from a user’s perspective. In addition, no analyses in the literature examined the value of real-time information as affected by trip purpose and weather, which is important especially for the cities in which the weather remains below zero degrees during winter. The primary objectives of this research are to elucidate these issues. A stated preference survey describing the choice between scheduled and real-time information systems was conducted in Calgary, Canada. A total of 426 people participated in the survey, and each person was presented with three randomly selected choice situations. This data set was utilized to estimate the coefficients in different utility functions using a mixed logit model, which avoided several major limitations of a standard multinomial logit model. It was found that the disutility of the headway of a real-time information system was about half of the disutility of a scheduled information system. The analysis also showed that there was a nonlinear trend for the real-time information system, in which people found a higher disutility rate for a longer headway. Further, the value of real-time information availability was normally distributed in the population, with a mean of $0.50 and a standard deviation of $0.40. The results also revealed that the value of real-time information was significantly different when the weather was below and above 0 °C, those values were $0.59 and $0.41, respectively. Many of the findings obtained here are novel and have implications for both theory and practice. Particularly, they are important for transport modelers and service planners to design or adjust the headway for a desired level of service for a given (or a change in) bus arrival information type.

Suggested Citation

  • Md Matiur Rahman & Lina Kattan & S. C. Wirasinghe, 2018. "Trade-offs between headway, fare, and real-time bus information under different weather conditions," Public Transport, Springer, vol. 10(2), pages 217-240, August.
  • Handle: RePEc:spr:pubtra:v:10:y:2018:i:2:d:10.1007_s12469-018-0176-4
    DOI: 10.1007/s12469-018-0176-4
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    References listed on IDEAS

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    1. Rong-Chang Jou . & Ke-Hong Chen, 2015. "How Much Will I Pay for Freeway Real-Time Traffic Information?," Sustainability, MDPI, vol. 7(10), pages 1-12, September.
    2. 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, vol. 69(C), pages 409-422.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    4. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    5. 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.
    6. Johnson, Eric J & Meyer, Robert J, 1984. "Compensatory Choice Models of Noncompensatory Processes: The Effect of Varying Context," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 11(1), pages 528-541, June.
    7. Soriguera, Francesc, 2014. "On the value of highway travel time information systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 294-310.
    8. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    9. 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.
    10. Cats, Oded & Loutos, Gerasimos, 2016. "Evaluating the added-value of online bus arrival prediction schemes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 35-55.
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