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Truck freight demand elasticity with respect to tolls in New York State

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  • Wang, Xiaokun (Cara)
  • Zhang, Dapeng

Abstract

Road pricing is an important travel demand management strategy and its effects on transportation system has been widely investigated. Toll elasticity has been derived in the existing literature to characterize its effect on travel demand all around the world. However, very few studies have comprehensively analyzed the effect of tolls on freight transportation, which plays an increasingly important role in social and economic activities. To enrich the understanding of freight travel demand, this study conducted a stated preference survey on freight carriers who routinely use toll facilities. A regression model about freight carriers’ stated reduction in vehicle miles traveled (VMT) on toll roads is then developed. The elasticity value is derived and compared with values in existing literature. Based on the calibrated model, the VMT change in response to hypothetical toll price increases is simulated for New York State. The simulation results reveal important insights that will help policy makers design ideal freight demand management strategies.

Suggested Citation

  • Wang, Xiaokun (Cara) & Zhang, Dapeng, 2017. "Truck freight demand elasticity with respect to tolls in New York State," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 51-60.
    • Handle: RePEc:eee:transa:v:101:y:2017:i:c:p:51-60
    DOI: 10.1016/j.tra.2017.04.035
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    1. Börjesson, Maria & Eliasson, Jonas & Hugosson, Muriel & Brundell-Freij, Karin, 2012. "The Stockholm congestion charges – five years on. Effects, acceptability and lessons learnt," Working papers in Transport Economics 2012:3, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    2. Theo Arentze & Tao Feng & Harry Timmermans & Jops Robroeks, 2012. "Context-dependent influence of road attributes and pricing policies on route choice behavior of truck drivers: results of a conjoint choice experiment," Transportation, Springer, vol. 39(6), pages 1173-1188, November.
    3. Lee, Lung-Fei, 1992. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Econometric Theory, Cambridge University Press, vol. 8(4), pages 518-552, December.
    4. Anna Matas & José-Luis Raymond, 2002. "The demand elasticity on tolled motorways," Working Papers wp0203, Department of Applied Economics at Universitat Autonoma of Barcelona.
    5. Odeck, James & Kjerkreit, Anne, 2010. "Evidence on users' attitudes towards road user charges--A cross-sectional survey of six Norwegian toll schemes," Transport Policy, Elsevier, vol. 17(6), pages 349-358, November.
    6. Holgui­n-Veras, José & Wang, Qian & Xu, Ning & Ozbay, Kaan & Cetin, Mecit & Polimeni, John, 2006. "The impacts of time of day pricing on the behavior of freight carriers in a congested urban area: Implications to road pricing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(9), pages 744-766, November.
    7. Ni, Linglin & Wang, Xiaokun (Cara) & Zhang, Dapeng, 2016. "Impacts of information technology and urbanization on less-than-truckload freight flows in China: An analysis considering spatial effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 12-25.
    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. Börjesson, Maria & Eliasson, Jonas & Hugosson, Muriel B. & Brundell-Freij, Karin, 2012. "The Stockholm congestion charges—5 years on. Effects, acceptability and lessons learnt," Transport Policy, Elsevier, vol. 20(C), pages 1-12.
    10. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    11. Zhang, Dapeng & Wang, Xiaokun (Cara), 2014. "Transit ridership estimation with network Kriging: a case study of Second Avenue Subway, NYC," Journal of Transport Geography, Elsevier, vol. 41(C), pages 107-115.
    12. Paleti, Rajesh & Bhat, Chandra R., 2013. "The composite marginal likelihood (CML) estimation of panel ordered-response models," Journal of choice modelling, Elsevier, vol. 7(C), pages 24-43.
    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.
    14. Zhang, Dapeng & Magalhães, David José Ahouagi Vaz & Wang, Xiaokun (Cara), 2014. "Prioritizing bicycle paths in Belo Horizonte City, Brazil: Analysis based on user preferences and willingness considering individual heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 67(C), pages 268-278.
    15. Friedlaender, Ann F & Spady, Richard H, 1980. "A Derived Demand Function for Freight Transportation," The Review of Economics and Statistics, MIT Press, vol. 62(3), pages 432-441, August.
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