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An analysis of parking behaviour using discrete choice models calibrated on SP datasets

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  • Stephane Hess

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  • John W. Polak

    ()

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    Abstract

    Parking policy is an important component of contemporary travel demand management policies. The effectiveness of many parking policy measures depends on influencing parking type choice, so that understanding the factors affecting these choices is of considerable practical importance. Yet, academic interest in this issue has been, at best, intermittent. This paper reports the results of an analysis of parking choice behaviour, based on a stated preference (SP) dataset, collected in various city centre locations in the UK. The analysis advances the state of the art in the analysis of parking choice behaviour by using a mixed multinomial logit (MMNL) model, capable of accommodating random heterogeneity in travellers’ tastes and potential correlation structure induced by repeated observations being made of the same individuals. The results of the analysis indicate that taste heterogeneity is a major factor in parking type choice. Accommodating this heterogeneity leads to significantly different conclusions regarding the influence of substantive factors such as access, search and egress time and on the treatment of potential fines for illegal parking. It also has important effects on the implied willingness to pay for timesavings and on the distribution of this willingness in the population. Our analysis also reveals important differences in parking behaviour across different journey purposes, and the models reveal an important locational effect, in such that the results of the analysis vary substantively across the three locations used in the SP surveys. Finally, the paper also discusses a number of technical issues related to the specification of taste heterogeneity that are of wider significance in the application of the MMNL model.

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    Bibliographic Info

    Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa04p60.

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    Date of creation: Aug 2004
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    Handle: RePEc:wiw:wiwrsa:ersa04p60

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
    2. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    3. David Revelt & Kenneth Train, 2001. "Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier," Econometrics 0012001, EconWPA.
    4. Hensher, David A. & King, Jenny, 2001. "Parking demand and responsiveness to supply, pricing and location in the Sydney central business district," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(3), pages 177-196, March.
    5. Kenneth E. Train, 1998. "Recreation Demand Models with Taste Differences over People," Land Economics, University of Wisconsin Press, vol. 74(2), pages 230-239.
    6. Bhat, Chandra R., 2000. "A multi-level cross-classified model for discrete response variables," Transportation Research Part B: Methodological, Elsevier, vol. 34(7), pages 567-582, September.
    7. Algers, Staffan & Bergström, Pål & Dahlberg, Matz & Lindqvist Dillén, Johanna, 1998. "Mixed Logit Estimation of the Value of Travel Time," Working Paper Series 1998:15, Uppsala University, Department of Economics.
    8. Train, Kenneth, 2000. "Halton Sequences for Mixed Logit," Department of Economics, Working Paper Series qt6zs694tp, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    9. Brownstone, David & Bunch, David S & Train, Kenneth, 1999. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," University of California Transportation Center, Working Papers qt45f996hh, University of California Transportation Center.
    10. H C W L Williams, 1977. "On the formation of travel demand models and economic evaluation measures of user benefit," Environment and Planning A, Pion Ltd, London, vol. 9(3), pages 285-344, March.
    11. 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.
    12. Stephane Hess & John Polak, 2003. "An alternative method to the scrambled Halton sequence for removing correlation between standard Halton sequences in high dimensions," ERSA conference papers ersa03p406, European Regional Science Association.
    13. 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.
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