Abstract
This paper introduces a multi-commodity, single (generic) vehicle formulation of freight ODS model that combines a commodity-based model to estimate loaded truck trips and a complementary model of empty trips. This integration is important because explicit modeling of empty trips—that account for 30% to 40% of total truck trips—is required to avoid significant errors in the estimation of the directional traffic. The formulation is then applied to a case study. Two cases of the proposed model are studied. The first one uses total traffic in the estimation; while the second one is based on loaded and empty traffic. The results conclusively show that the models that consider an empty trip submodel significantly outperform the model that does not in their ability to replicate the observed traffic counts. The comparison between the results from the multi-commodity ODS and the single commodity ODS previously developed by the authors indicates that the multi-commodity formulation brings about substantial reductions in the error associated with the estimation of observed traffic counts. These reductions, in the order of 20% for empty traffic and 40% for loaded and total traffic, seem larger than the spurious improvement to be expected from the increased number of parameters, suggesting that the multi-commodity ODS formulation performs better. The results also show some minor improvements in the ability of the multi-commodity ODS formulation to estimate the OD matrices. In terms of the model's ability to correctly estimate the “true” value of the parameters of the models used, i.e., the parameter values estimated by calibrating the model directly from the OD data, it was found that the multicommodity ODS procedure is able to provide fairly good estimates Noortman and van Es's model parameters, though the parameters of the gravity models that came out to be quite different than the “true” values. The overall assessment of the formulation introduced here is that it represents a solid improvement with respect to comparable techniques.
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References
Abdelwahab WM, Sargious MA (1991) A simultaneous decision-making approach to model the demand for freight transportation. Can J Civ Eng 18(3):515–520
Al-Battaineh O, Kaysi IA (2005) Commodity-based truck matrix estimation using input–output data and genetic algorithms. Transp Res Rec 1923:37–45
Ben-Akiva M, Macke PP, Hsu PS (1985) Alternative methods to estimate route level trip tables and expand on-board surveys. Transp Res Rec 1037:1–11
Bureau of Transportation Statistics (1997) Standard Classification of Transported Goods (SCTG) Codes. http://www.bts.gov/programs/commodity_flow_survey/methods_and_limitations/commodity_classification_in_1997/classification.html
Cambridge Systematics Inc. (1997) A guidebook for forecasting freight transportation demand. National Cooperative Highway Research Program, Transportation Research Board, Washington DC
Friesz T, Harker P (1985) Freight network equilibrium: a review of the state of the art. In: Daughety A (ed) Analytical studies in transportation economics. New York, Cambridge University Press, pp 161–206
Friesz T, Holguín-Veras J (2005) Dynamic game—theoretic models of urban freight: formulation and solution approach. In: Reggiani A, Schintler L (eds) Methods and models in transport and telecommunications: cross Atlantic perspectives. Springer, Berlin, pp 143–161
Gedeon C, Florian M, Crainic T (1993) Determining origin–destination matrices and optimal multiproduct flows for freight transportation over multimodal networks. Transp Res Part B Methodol 27B(5):351–368
Holguín-Veras J (2000a) A framework for an integrative freight market simulation. IEEE 3rd Annual Intelligent Transportation Systems Conference ITSC-2000. IEEE, Dearborn Michigan, pp 476–481
Holguín-Veras J (2000b) On the attitudinal characteristics of motor carriers toward container availability systems. Int J Serv Technol Manag 1(2/3):140–155
Holguín-Veras J (2002) Revealed preference analysis of the commercial vehicle choice process. J Transp Eng 128(4):336–346
Holguín-Veras J, Patil G (2005) Observed trip chain behavior of commercial vehicles. Transp Res Rec 1906:74–80
Holguín-Veras J, Patil G (2008) An integrated commodity based/empty trip freight origin–destination synthesis model. Transp Res Rec (in press)
Holguín-Veras J, Thorson E (2000) An investigation of the relationships between the trip length distributions in commodity-based and trip-based freight demand modeling. Transp Res Rec No.1707, pp. 37–48
Holguín-Veras J, Thorson E (2003a) Modeling commercial vehicle empty trips with a first order trip chain model. Transp Res Part B Methodol 37(2):129–148
Holguín-Veras J, Thorson E (2003b) Practical implications of modeling commercial vehicle empty trips. Transp Res Rec 1833:87–94
Holguín-Veras J, Zorrilla JC, Thorson E (2005) Modeling commercial vehicle empty trips: theory and application. In: Mahmassani H (ed) XVI International Symposium of Traffic and Transportation Theory (ISTTT). Elsevier, pp 585–608
Holguín-Veras J, Silas M, Polimeni J, Cruz B (2007a) An investigation on the effectiveness of joint receiver–carrier policies to increase truck traffic in the off-peak hours. Part I: the behavior of receivers. Netw Spat Econ 7(3):277–295
Holguín-Veras J, Silas M, Polimeni J, Cruz B (2007b) An investigation on the effectiveness of joint receiver–carrier policies to increase truck traffic in the off-peak hours. Part II: the behavior of carriers. Netw Spat Econ (in press)
Kuwahara M, Sullivan E (1987) Estimating origin–destination surveys from roadside survey data. Transp Res Part B Methodol 21B(3):233–248
Lena TS, Ochieng V, Carter M, Holguín-Veras J, Kinney P (2002) Elemental carbon and PM2.5 levels in an urban community heavily impacted by truck traffic. Environ Health Perspect 110(10):1009–1015
List G, Turnquist M (1994) Estimating truck travel patterns in urban areas. Transp Res Rec 1430:1–9
McFadden D, Winston C, Boersch-Supan A (1986) Joint estimation of freight transportation decisions under non-random sampling. In: Daugherty A (ed) Analytical studies in transport economics. Cambridge University Press, Cambridge, pp 137–157
Nagurney A, Dong J (2002) Supernetworks: decision-making for the information age. Edward Elgar Publishers, Chentelham, England
Nagurney A, Siokos S (1997) Financial networks: statics and dynamics. Springer, Heidelberg
Noortman HJ, van Es J (1978) Traffic model. Manuscript for the Dutch Freight Transport Model
Nozick L, Turnquist M, List G (1996) Trade pattern estimation between the United States and Mexico. Trans Res Circ 459:74–87
Ogden KW (1992) Urban goods movement. Ashgate Publishing, London, England
Ortúzar JD, Willumsen LG (2001) Modelling transport. Wiley, New York
Tamin OZ, Willumsen LG (1988) Freight demand model estimation from traffic counts. PTRC Annual Meeting, University of Bath, England
Tavasszy LA, Stada JE, Hamerslag R (1994) The impact of decreasing border barriers in europe on freight transport flows by road. Proceedings of the 36th Annual Conference of the Transportation Research Forum, Florida, USA
Willumsen LG (1978) OD matrices from network data: a comparison of alternative methods for their estimation. PTRC Annual Meeting
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Holguín-Veras, J., Patil, G.R. A Multicommodity Integrated Freight Origin–destination Synthesis Model. Netw Spat Econ 8, 309–326 (2008). https://doi.org/10.1007/s11067-007-9053-4
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DOI: https://doi.org/10.1007/s11067-007-9053-4