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Latent Class Approach to Estimate the Willingness to Pay for Transit User Information

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DOI: 10.4236/jtts.2012.23021    5,164 Downloads   8,445 Views   Citations

ABSTRACT

The aim of analysis is to understand how unreliable information influences user behaviour and how much it discourages public transport use. For this purpose, a Stated Preference Survey was carried out in order to know the preferences of public transport users relating to information needs and uncertainty on the information provided by Advanced Traveller Information System (ATIS). The perceived uncertainty is defined as information inaccuracy. In our study, we considered the difference between forecasted or scheduled waiting time at the bus stop and/or metro station provided by ATIS, and that experienced by user, to catch the bus and/or metro. A questionnaire was submitted to an appropriate sample of Palermo’s population. A Latent Class Logit model was calibrated, taking into account attributes of cost, information inaccuracy, travel time, waiting time, and cut-offs in order to reveal preference heterogeneity in the perceived information. The calibrated model showed various sources of preference heterogeneity in the perceived information of public transport users as highlighted by the analysis reported. Finally, the willingness to pay was estimated, confirming a great sensitivity to the perceived information, provided by ATIS.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

P. Zito and G. Salvo, "Latent Class Approach to Estimate the Willingness to Pay for Transit User Information," Journal of Transportation Technologies, Vol. 2 No. 3, 2012, pp. 193-203. doi: 10.4236/jtts.2012.23021.

References

[1] C. G. Chorus, “Traveler Response to Information,” The Neth-erlands TRAIL Research School, Delft, 2007.
[2] M. A. Ab-del-Aty, “Using Ordered Probit Modeling to Study the Effect of ATIS on Transit Ridership,” Transportation Research Part C, Vol. 9, No. 4, 2001, pp. 265-277. doi:10.1016/S0968-090X(00)00037-1
[3] M. A. Abdel-Aty, et al., “Investigating Effect of Advanced Traveler Information on Commuter Tendency to Use Transit,” Transportation Research Record, Vol. 1550, 1996, pp. 65-72. doi:10.3141/1550-09
[4] A. J. Khattak, et al., “Willingness to Pay for Travel Information,” Transportation Research Part C, Vol. 11, No. 2, 2003, pp. 137-159. doi:10.1016/S0968-090X(03)00005-6
[5] E. J. E. Molin, et al., “Traveler Expectations and Willingness-to-Pay for Web-Enabled Public Transport Information Services,” Transportation Research Part C, Vol. 14, No. 1, 2006, pp. 57-67. doi:10.1016/j.trc.2006.05.003
[6] A. Polydoropoulou, , et al., “Willingness to Pay for Advanced Traveler Information Systems: Smartraveller Case Study,” Transportation Research Record, Vol. 1588, 1997, pp. 1-9.
[7] J. L. Adler, et al., “In-Laboratory Experiments to Investigate Driver Behavior under Advanced Traveler Information Systems,” Transportation Research Part C, Vol. 2, No. 2, 1994, pp. 149-164. doi:10.1016/0968-090X(94)90006-X
[8] R. H. M. Emmerink, et al., “Effects of Information in Road Transport Networks with Recurrent Congestion,” Transportation, Vol. 22, No. 1, 1995, pp. 21-53. doi:10.1007/BF01151617
[9] R. H. M. Emmerink, et al., “Variable Message Signs and Radio Traffic Information: An Integrated Empirical Analysis of Drivers’ Route Choice Be-haviour,” Transportation Research Part A, Vol. 30, No. 2, 1996, pp. 135-153. doi:10.1016/0965-8564(95)00016-X
[10] E. Hato, et al., “In-corporating an Information Acquisition Process into a Route Choice Model with Multiple Information Sources,” Transpor-tation Research Part C, Vol. 7, No. 2, 1999, pp. 109-129. doi:10.1016/S0968-090X(99)00015-7
[11] R. Jou, et al., “Route Switching Behavior on Freeways with the Provision of Different Types of Real-Time Traffic Information,” Transpor-tation Research Part A, Vol. 39, No. 5, 2005, pp. 445-461. doi:10.1016/j.tra.2005.02.004
[12] S. Kenyon, et al., “The Value of Integrated Multimodal Traveler Information and Its Potential Contribution to Modal Change,” Transportation Re-search Part F, Vol. 6, No. 1, 2003, pp. 1-21. doi:10.1016/S1369-8478(02)00035-9
[13] E. C. van Berkum, et al., “The Impact of Traffic Information,” Ph.D. Thesis, Delft University of Technology. Delft, 1993.
[14] E. Avineri, et al., “Sensitivity to Uncertainty: Need for a Paradigm Shift,” Transportation Research Records, Vol. 1854, 2003, pp. 90-98. doi:10.3141/1854-10
[15] T. A. Arentze, et al., “Information Gain. Novelty Seeking and Travel: A Model of Dynamic Ac-tivity-Travel Behavior under Conditions of Uncertainty,” Transportation Research Part A, Vol. 39, No. 2-3, 2005, pp. 125-145. doi:10.1016/j.tra.2004.08.002
[16] C. G. Chorus, et al., “Trav-ellers’ Need for Information in Traffic and Transit: Results from a Web Survey,” Journal of Intelligent Transportation Systems. Vol. 11, No. 2, 2007, pp. 57-67. doi:10.1080/15472450701293841
[17] K.V. Katsikopoulos, et al., “The Framing of Drivers’ Route Choices When Travel Time Information Is Provided under Varying Degrees of Cognitive Load,” Human Factors, Vol. 42, No. 3, 2000, pp. 470-481. doi:10.1518/001872000779698088
[18] K. V. Katsikopoulos, et al., “Risk Attitude Reversals in Drivers’ Route Choice When Range of Travel Information Is Provided,” Human Factors, Vol. 44, No. 3, 2002, pp. 466-473. doi:10.1518/0018720024497718
[19] K. Dziekan and K. Kot-tenhoff, “Dynamic at Stop RealTime Information Displays for Public Transport: Effects on Customers,” Transportation Re-search A, Vol. 41, 2007, pp. 489-501. doi:10.1016/j.tra.2006.11.006
[20] J. Polak and P. Jones, “The Acquisition of Pre-Trip Information: A Stated Preference Ap-proach,” Transportation, Vol. 20, 1993, pp. 179-198. doi:10.1007/BF01307058
[21] P. Nijkamp, et al., “Public Transport Information Systems: An English Case Study,” In: Telematics and Transport Behaviour, Springer, Heidelberg, 1996, pp. 137-157.
[22] R. G. Mishalani, et al., “The Value of Real-Time Bus Arrival Information under Various Supply and Demand Characteristics,” ITS America 10th Annual Meeting, Boston, 1-4 May 2000, pp. 303-324.
[23] M. D. Hickman and N. H. M. Wilson, “Passenger Travel Time and Path Choice Implications of Real-Time Transit Information,” Transportation Research C, Vol. 3, 1995, pp. 211-226. doi:10.1016/0968-090X(95)00007-6
[24] J. W. Grotenhuis, et al., “The Desired Quality of Integrated Multimodal Travel In-formation in Public Transport: Customer Needs for Time and Effort Savings,” Transport Policy, Vol. 14, 2007, pp. 27-38. doi:10.1016/j.tranpol.2006.07.001
[25] J. Lappin, “Under-standing and Predicting Traveller Response to Information. A Literature Review,” 2001.
[26] S. I. J. Chien, et al., “Devel-opment of a Probabilistic Model to Optimize Disseminated Real-Time Bus Arrival Information for Pre-Trip Passengers,” Journal of Advanced Transportation, Vol. 41, No. 2, 2007, pp. 195-215. doi:10.1002/atr.5670410205
[27] M. C. Tan, et al., “An Algo-rithm for Finding Reasonable Paths in Transit Networks,” Journal of Advanced Transportation, Vol. 41, No. 3, 2007, pp. 285-305. doi:10.1002/atr.5670410305
[28] M. Lehtonen and R. Kulmala, “Benefits of Pilot Implementation of Public Transport Signal Priorities and Real-Time Passenger Information,” Transporta-tion Research Record, Vol. 1799, 2002, pp. 18-25. doi:10.3141/1799-03
[29] J. Y. K. Luk and C. Yang, “Impact of ITS Measures on Public Transport: A Case Study,” Journal of Advanced Transportation, Vol. 35, No. 3, 2002, pp. 305-320. doi:10.1002/atr.5670350308
[30] P. Zito, et al., “The Effect of Advanced Traveller Information Systems on Public Transport Demand and Its Uncertainty,” Transportmetrica, Vol. 7, No. 1, 2011, pp. 3143. doi:10.1080/18128600903244727
[31] K. E. Watkins, et al., “Where Is My Bus? Impact of Mobile Real-Time Information on the Perceived and Actual Wait Time of Transit Riders,” Transportation Research Part A, Vol. 45, 2011, pp. 839-848. doi:10.1016/j.tra.2011.06.010
[32] J. Swait, “A Non Compen-satory Choice Model Incorporating Attribute Cutoffs,” Trans-portation Research Part B, Vol. 35, 2001, pp. 903-928. doi:10.1016/S0191-2615(00)00030-8
[33] G. Kocur, et al., “Guide to Forecasting Travel Demand with Direct Utility As-sessment,” 1982.
[34] J. de Ortúzar, et al., “Modelling Trans-port,” 3rd edition, John Wiley & Sons, New York, 2001.
[35] W. H. Greene and D. A. Hensher, “A Latent Class Model for Discrete Choice Analysis: Contrasts with Mixed Lo-git,” Transportation Research Part B, Vol. 37, 2003, pp. 681698. doi:10.1016/S0191-2615(02)00046-2
[36] K. Train, “Discrete Choice Methods with Simulation,” Cambridge Uni-versity Press, Cambridge, 2003. doi:10.1017/CBO9780511753930
[37] D. A. Hensher, et al., “Applied Choice Analysis a Primer,” Cambridge University Press, Cambridge, 2005.

  
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