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The value of time and forecasting of flowsin freight transportation

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  • Erik Bergkvist

Abstract

This paper gives an overview of the current state of research and present own results in some areas regarding cost benefit analysis (CBA) for road infrastructure investments. It deals with the robustness of the estimated average value of time (VOT) currently used in Swedish infrastructure appraisals and also analyses the estimator in the case of different heteroskedastic errors and functional forms. It finallly consider forecasts of road freight flows in cases where information are scarce and may lack precision. Critical questions are how heterogeneity and non-linearities influences forecasts in the freight market. In detail it consdier the following: The impact of VOT for road freight when the origins of transports are taken into account. VOT is found to be dependent on combinations of region, transported distance, industry bransch and if transports are owned or hired. However, available data does not allow for significant values in each category. Altogether the study indicates a VOT spanning from 0 to 732 SEK, which should be compared with the average value of 80 SEK used today. The results also indicate pair-wise differences in VOT between short and long as well as hired and internal transports. The ownership condition is found to have a significant impact on VOT, although current data not gives significant differences between each of the four ownership/distance categories. It is found that further studies should focus on the hypothesis that VOT for short internal freight flows are significantly higher than the average VOT. The traditional logit model is compared with the semi-parametric weighted average density (WAD) estimator. It is found that the performance for the WAD estimator in terms of bias and mean square error is similar to the logit ML estimator for spherical errors in a latent variable specification. Methods for prediction of road freight flows are also investigated. Three traditional gravity model specifications (OLS, NLS, and Poisson regression) are compared with a neural network specification. On a data set of Norwegian inter-regional freight flows it is found that the Poisson model performs best in terms of root mean square error (RMSE) but also that the size of predicted flows is dependent on the method chosen to evaluate available estimation methods. Finally, we integrate freight flow prediction and estimation of VOT in one analysis. Logit models and neural networks with linear and non-linear profit functions are compared. The study indicates that the average VOT may decrease when prediction improves as models are given more non-linear specifications.

Suggested Citation

  • Erik Bergkvist, 2001. "The value of time and forecasting of flowsin freight transportation," ERSA conference papers ersa01p271, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa01p271
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    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa01/papers/full/271.pdf
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    1. Chuan-Zhong Li, 1996. "Semiparametric Estimation of the Binary Choice Model for Contingent Valuation," Land Economics, University of Wisconsin Press, vol. 72(4), pages 462-473.
    2. Newey, Whitney K & Powell, James L & Walker, James R, 1990. "Semiparametric Estimation of Selection Models: Some Empirical Results," American Economic Review, American Economic Association, vol. 80(2), pages 324-328, May.
    3. Small, Kenneth A. & Gomez-Ibanez, Jose A., 1999. "Urban transportation," Handbook of Regional and Urban Economics, in: P. C. Cheshire & E. S. Mills (ed.), Handbook of Regional and Urban Economics, edition 1, volume 3, chapter 46, pages 1937-1999, Elsevier.
    4. Hardle, Wolfgang & Manski, Charles F., 1993. "Nonparametric and semiparametric approaches to discrete response analysis," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 1-2, July.
    5. Karlsson, Charlie & Johansson, Börje, 2006. "Regional Development and Knowledge," Working Paper Series in Economics and Institutions of Innovation 76, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    6. Fischer, Manfred M. & Gopal, Sucharita, 1994. "Artificial Neural Networks. A New Approach to Modelling Interregional Telecommunication Flows," MPRA Paper 77822, University Library of Munich, Germany.
    7. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    8. Yatchew, Adonis & Griliches, Zvi, 1985. "Specification Error in Probit Models," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 134-139, February.
    9. Powell, James L. & Stoker, Thomas M., 1996. "Optimal bandwidth choice for density-weighted averages," Journal of Econometrics, Elsevier, vol. 75(2), pages 291-316, December.
    10. Cameron, A Colin & Windmeijer, Frank A G, 1996. "R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 209-220, April.
    11. Johansson, Borje & Westin, Lars, 1994. "Affinities and Frictions of Trade Networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 28(3), pages 243-261, September.
    12. W. Michael Hanemann, 1984. "Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(3), pages 332-341.
    13. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
    14. Mattsson, Lars-Goran, 1984. "Equivalence between welfare and entropy approaches to residential location," Regional Science and Urban Economics, Elsevier, vol. 14(2), pages 147-173, May.
    15. Anas, Alex, 1983. "Discrete choice theory, information theory and the multinomial logit and gravity models," Transportation Research Part B: Methodological, Elsevier, vol. 17(1), pages 13-23, February.
    16. Lee, Lung-Fei, 1982. "Specification error in multinomial logit models : Analysis of the omitted variable bias," Journal of Econometrics, Elsevier, vol. 20(2), pages 197-209, November.
    17. Terry L. Friesz & Zhong-Gui Suo & Lars Westin, 1998. "Integration of Freight Network and Computable General Equilibrium Models," Advances in Spatial Science, in: Lars Lundqvist & Lars-Göran Mattsson & Tschangho John Kim (ed.), Network Infrastructure and the Urban Environment, chapter 12, pages 212-223, Springer.
    18. Small, K. & Winston, C., 1998. ""The Demand for Transportation: Models and Applications"," Papers 98-99-6, California Irvine - School of Social Sciences.
    19. Ichimura, H., 1991. "Semiparametric Least Squares (sls) and Weighted SLS Estimation of Single- Index Models," Papers 264, Minnesota - Center for Economic Research.
    20. Ruud, Paul A, 1983. "Sufficient Conditions for the Consistency of Maximum Likelihood Estimation Despite Misspecifications of Distribution in Multinomial Discrete Choice Models," Econometrica, Econometric Society, vol. 51(1), pages 225-228, January.
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    1. Boris Goenaga & Victor Cantillo, 2020. "Willingness to pay for freight travel time savings: contrasting random utility versus random valuation," Transportation, Springer, vol. 47(2), pages 705-736, April.
    2. Jawaher Binsuwadan & Gerard De Jong & Richard Batley & Phill Wheat, 2022. "The value of travel time savings in freight transport: a meta-analysis," Transportation, Springer, vol. 49(4), pages 1183-1209, August.
    3. Albert, Adrian & Schaefer, Andreas, 2013. "Demand for freight transportation in the U.S.: a high-level view," 54th Annual Transportation Research Forum, Annapolis, Maryland, March 21-23, 2013 206946, Transportation Research Forum.

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