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A Cross Sectional Study of Freight Transport Demand and Rail-Truck Competition in Canada

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  • Tae Hoon Oum

Abstract

This article derives a rail-truck freight demand model that is consistent with the economic theory of modal choice in the price-speed-reliability space. The translog model is estimated from the cross sectional data of Canadian interregional freight flows for the eight selected commodities. Major empirical findings are: (i) The quality attributes of service significantly influence modal choice only for the relatively high-value commodities. (ii) Both the price and quality elasticities of demand and the elasticity of rail-truck substitution vary substantially from route to route as well as from commodity to commodity. This implies that CES models including Cobb-Douglas form should not be used for freight demand studies. (iii) For the relatively high-value commodities, short-haul traffic is largely dominated by the truck mode, and significant rail-truck competition exists only in the medium and long-haul markets. (iv) For the relatively low-value commodities, effective rail-truck competition exists only in the short-haul markets. Hence, the medium and long-haul markets are largely rail-dominated.

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  • Tae Hoon Oum, 1979. "A Cross Sectional Study of Freight Transport Demand and Rail-Truck Competition in Canada," Bell Journal of Economics, The RAND Corporation, vol. 10(2), pages 463-482, Autumn.
    • Handle: RePEc:rje:bellje:v:10:y:1979:i:autumn:p:463-482
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    Cited by:

    1. Larson, Richard C., 2017. "Cross-sectional surveys: Inferring total eventual time in current state using only elapsed time-to-date," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 1-13.
    2. Wilson, William W., 1984. "Estimation Of Modal Demand Elasticities In Grain Transportation," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 9(2), pages 1-15, December.
    3. Casavant, Ken & Jessup, Eric, 2005. "What Makes them Viable? Determining the Attributes that Offer Potential Viability to Inter-Modal Truck-Rail Facilities in Washington State," 46th Annual Transportation Research Forum, Washington, D.C., March 6-8, 2005 208221, Transportation Research Forum.
    4. Mishra, Sabyasachee & Iseki, Hiroyuki & Moeckel, Rolf, 2014. "Multi entity perspective freight demand modeling technique: Varying objectives and outcomes," Transport Policy, Elsevier, vol. 35(C), pages 176-185.
    5. Ferguson, Mark & Maoh, Hanna & Ryan, Justin & Kanaroglou, Pavlos & Rashidi, Taha Hossein, 2012. "Transferability and enhancement of a microsimulation model for estimating urban commercial vehicle movements," Journal of Transport Geography, Elsevier, vol. 24(C), pages 358-369.
    6. Wilson, William W., 1982. "Estimation of Demand Elasticities for Transportation Modes in Grain Transportation," Agricultural Economics Reports 23266, North Dakota State University, Department of Agribusiness and Applied Economics.
    7. Ndembe, Elvis & Bitzan, John, 2018. "Grain freight elevator consolidation, transportation demand, and the growth of shuttle facilities," Research in Transportation Economics, Elsevier, vol. 71(C), pages 54-60.
    8. Gorman, Michael F. & Conway, Daniel G., 2005. "Logistics Costs Based Estimation of Freight Transportation Demand," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 44(1).
    9. Jordan, Jeffrey L. & Thompson, Stanley R., 1984. "Forecasting Rail Freight Traffic from a Statewide Economic Model," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 16(2), pages 83-90, December.
    10. Kim, Nam Seok & Van Wee, Bert, 2011. "The relative importance of factors that influence the break-even distance of intermodal freight transport systems," Journal of Transport Geography, Elsevier, vol. 19(4), pages 859-875.
    11. Babcock, Michael W. & German, Wade, 1982. "Forecast of Agricultural Rail Demand to 1985," Transportation Research Forum Proceedings 1980s 311554, Transportation Research Forum.
    12. Jourquin, Bart & Beuthe, Michel, 2019. "Cost, transit time and speed elasticity calculations for the European continental freight transport," Transport Policy, Elsevier, vol. 83(C), pages 1-12.
    13. Ron Yang, 2022. "(Don’t) Take Me Home: Home Preference and the Effect of Self-Driving Trucks on Interstate Trade," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    14. Tae H. Oum & Waters, W.G. & Jong Say Yong, 1990. "A survey of recent estimates of price elasticities of demand for transport," Policy Research Working Paper Series 359, The World Bank.
    15. German, H. Wade & Babcock, Michael W., 1991. "How Can Railroads Recapture Lost Market Share?," Transportation Research Forum Proceedings 1990s 319055, Transportation Research Forum.
    16. Babcock, Michael W. & Gayle, Philip G., 2014. "Specifying and Estimating a Regional Agricultural Railroad Demand Model," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 53(1).
    17. Bühler, Georg & Hoffmann, Tim & Wölfing, Nikolas & Schmidt, Markus, 2009. "Wettbewerb und Umweltregulierung im Verkehr: Eine Analyse zur unterschiedlichen Einbindung der Verkehrsarten in den Emissionshandel," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 110505.
    18. Train, Kenneth & Wilson, Wesley W., 2007. "Spatially Generated Transportation Demands," Research in Transportation Economics, Elsevier, vol. 20(1), pages 97-118, January.
    19. Manuel Manzanedo & Carlos Alonso de Armiño & Nuño Basurto & Roberto Alcalde & Belen Alonso, 2024. "Divergences between EU Members on the Sustainability of Road Freight Transport," Sustainability, MDPI, vol. 16(15), pages 1-17, July.
    20. Michel Beuthe & Bart Jourquin & Natalie Urbain, 2014. "Estimating Freight Transport Price Elasticity in Multi-mode Studies: A Review and Additional Results from a Multimodal Network Model," Transport Reviews, Taylor & Francis Journals, vol. 34(5), pages 626-644, September.

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