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"The Demand for Transportation: Models and Applications"

Author

Listed:
  • Small, K.
  • Winston, C.

Abstract

This chapter describes how transportation demand is analyzed and what has been learned from doing so. We first present a selection of the most important transportation demand models, with an emphasis on disaggregate models because they have generally been the most successful in capturing essential features of travel behavior. We then show how the models have enriched our substantive knowledge of the demand for transportation, and discuss how they have been used to address important tranportation policy issues.

Suggested Citation

  • Small, K. & Winston, C., 1998. ""The Demand for Transportation: Models and Applications"," Papers 98-99-6, California Irvine - School of Social Sciences.
    • Handle: RePEc:fth:calirv:98-99-6
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    Citations

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    Cited by:

    1. Shenhao Wang & Qingyi Wang & Jinhua Zhao, 2018. "Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation," Papers 1812.04528, arXiv.org, revised Apr 2021.
    2. Parry, Ian W.H., 2008. "How should heavy-duty trucks be taxed?," Journal of Urban Economics, Elsevier, vol. 63(2), pages 651-668, March.
    3. Kenneth A. Small & Clifford Winston & Jia Yan, 2005. "Differentiated Road Pricing, Express Lanes and Carpools: Exploiting Heterogeneous Preferences in Policy Design," Working Papers 050616, University of California-Irvine, Department of Economics, revised Mar 2006.
    4. David Figlio & Jens Ludwig, 2012. "Sex, Drugs, and Catholic Schools: Private Schooling and Non-Market Adolescent Behaviors," German Economic Review, Verein für Socialpolitik, vol. 13(4), pages 385-415, November.
    5. Shenhao Wang & Qingyi Wang & Nate Bailey & Jinhua Zhao, 2018. "Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective," Papers 1810.10465, arXiv.org, revised Sep 2019.
    6. Cristina Borra & Luis Palma, 2004. "Analyzing the determinants of freight shipper's behavior: own account versus purchased transport," ERSA conference papers ersa04p163, European Regional Science Association.
    7. Ignacio Escañuela Romana & Mercedes Torres-Jiménez & Mariano Carbonero-Ruz, 2023. "Elasticities of Passenger Transport Demand on US Intercity Routes: Impact on Public Policies for Sustainability," Sustainability, MDPI, vol. 15(18), pages 1-27, September.
    8. Estache, Antonio & Romero, Manuel & Strong, John, 2000. "The long and winding path to private financing and regulation of toll roads," Policy Research Working Paper Series 2387, The World Bank.
    9. Wang, Shenhao & Wang, Qingyi & Zhao, Jinhua, 2020. "Multitask learning deep neural networks to combine revealed and stated preference data," Journal of choice modelling, Elsevier, vol. 37(C).
    10. Erik Bergkvist, 2001. "The value of time and forecasting of flowsin freight transportation," ERSA conference papers ersa01p271, European Regional Science Association.
    11. Shenhao Wang & Qingyi Wang & Jinhua Zhao, 2019. "Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data," Papers 1901.00227, arXiv.org, revised Aug 2019.
    12. Cristina Borra Marcos & Luis Palma Martos, 2004. "Analyzing the Determinants of Freight Shippers’ Behavior: Own-Account versus Purchased Transport in Andalusia," Economic Working Papers at Centro de Estudios Andaluces E2004/76, Centro de Estudios Andaluces.
    13. Brinkman, Anthony P., 2003. "The Ethical Challenges and Professional Responses of Travel Demand Forecasters," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt9c3330tt, Institute of Transportation Studies, UC Berkeley.
    14. Wang, Ling & Wang, Ke & Zhang, Jianjun & Zhang, Di & Wu, Xia & Zhang, Lijun, 2020. "Multiple objective-oriented land supply for sustainable transportation: A perspective from industrial dependence, dominance and restrictions of 127 cities in the Yangtze River Economic Belt of China," Land Use Policy, Elsevier, vol. 99(C).

    More about this item

    Keywords

    TRANSPORT ; ECONOMIC MODELS ; SPATIAL ANALYSIS;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

    Statistics

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