Empirical Method for Predicting Internal-External Truck Trips at a Major Port
Publication: Journal of Transportation Engineering
Volume 137, Issue 7
Abstract
This paper presents a case study to explore the truck-trip generation model for hauling containers at a major international seaport. An internal-external truck-trip forecast model is examined. It incorporates influential factors of regional freight activity attributes, economic growth attributes, and natural disaster attributes based on monthly data from 2000–2008. A best-fit truck-trip forecasting model is determined by comparing the prediction accuracy of a multiple regression model, time-series models, and a neural network model. The findings indicate that the back propagation neural network model generates better forecasting performance than the regression and time-series approaches. Additionally, this paper identifies the difference between truck trips and commodity-flow tonnages converted by truck payload factors, which would be significantly affected by truck-trip chains and truck drivers’ route choice behaviors. The analysis also reveals that a port truck-trip forecast model based on commodity flows would be very sensitive to the events of oil price fluctuations and new operation or infrastructure upgrade of competitive ports nearby, once the conversion difference goes up to 30%.
Get full access to this article
View all available purchase options and get full access to this article.
References
Al-Deek, H. M. (2001). “Comparison of two approaches for modeling freight movement at seaports.” J. Comput. Civ. Eng., 15(4), 284–291.
Al-Deek, H. M., Johnson, G., Mohamed, A., and El-Maghraby, A. (2000). “Truck trip generation models for seaports with container and trailer operation.” Transportation Research Record 1719, Transportation Research Board, Washington, DC, 1–9.
Ashtakala, B., and Murthy, A. S. N. (1988). “Optimized gravity models for commodity transportation.” J. Transp. Eng., 114(4), 393–408.
Beagan, D., Fischer, M., and Kuppam, A. (2007). “Quick response freight manual II.” FHWA-HOP-08-010, Federal Highway Administration, Washington, DC.
Bishop (1995). Neural networks for pattern recognition, Clarendon, Oxford, UK.
Bowerman, B. L., and O’Connell, R. T. (1993). Forecasting and time series: An applied approach, Duxbury, Pacific Grove, CA.
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time series analysis: Forecasting and control, Wiley, Hoboken, NJ.
Brockwell, P. J., and Davis, R. A. (1987). Time series: Theory and methods, Springer-Verlag, New York.
Cambridge Systematics. (2007). MAG internal truck travel survey and truck model development study, Maricopa Association of Governments, Phoeniz, AZ.
Chatfield, C. (2003). The analysis of time series: An introduction, CRC, Boca Raton, FL.
Colorado Department of Transportation (2008). “2035 transportation plan: Freight technical report.” Denver.
Federal Highway Administration (FHWA) (2001). “Traffic monitoring guide.” Office of Highway Policy Information, Washington, DC.
Fischer, M., Ang-Olson, J., and La, A. (2000). “External urban truck trips based on commodity flows: A model.” Transportation Research Record 1707, Transportation Research Board, Washington, DC, 73–80.
Gardner, E. S., Jr. (1985). “Exponential smoothing: The state of the art.” J. Forecast., 4, 1–28.
Harris, G. A., Farrington, P. A., Anderson, M. D., Schoening, N., and Swain, J. (2009). “Developing freight analysis zones at a state level: A cluster analysis approach.” Proc., Transportation Research Board 88th Annual Meeting, Washington, DC.
Holguin-Veras, J. (2002). “Revealed preference analysis of commercial vehicle choice process.” J. Transp. Eng., 128(4), 336–346.
Holguin-Veras, J., López-Genao, Y., and Salam, A. (2002). “Truck-trip generation at container terminals: Results from a nationwide survey.” Transportation Research Record 1790, Transportation Research Board, Washington, DC, 89–96.
Holguin-Veras, J., and Thorson, E. (2000). “Trip length distributions in commodity-based and trip-based freight demand modeling: investigation of relationships.” Transportation Research Record 1707, Transportation Research Board, Washington, DC, 37–48.
Holguin-Veras, J., and Thorson, E. (2003). “Practical implications of modeling commercial vehicle empty trips.” Transportation Research Record 1833, Transportation Research Board, Washington, DC, 87–94.
Jack Faucett Associates (1999). “Research and development of destination, mode, and routing choice models for freight.” Final Rep. Prepared for U.S. DOT Small Business Innovation Research Office, DTS-22
Jessup, E., Casavant, K. L., and Lawson, C. T. (2004). “Truck trip data collection methods.” FHWA-OR-RD-04-10, Federal Highway Administration, Washington, DC
John, Fossey (2009). Containerisation international yearbook 2009, Lloyd’s MIU, London.
Kansas Department of Transportation (2007). Kansas statewide freight plan, Topeka, KS.
National Cooperative Highway Research Program (NCHRP) (2001). “Truck trip generation data: A synthesis of highway practice.” NCHRP Synthesis 298, Transportation Research Board, Washington, DC.
New Jersey Department of Transportation (2007). New Jersey comprehensive statewide freight plan, Trenton, NJ.
Novak, D. C., Hodgdon, C., Guo, F., and Aultman-Hall, L. (2008). Nationwide freight generation models: A spatial regression approach, Springer, Netherlands.
Sarvareddy, P., Al-Deek, H., Klodzinski, J., and Anagnostopoulos, G. (2005). “Evaluation of two modeling methods for generating heavy-truck trips at an intermodal facility by using vessel freight data.” Transportation Research Record 1906, Transportation Research Board, Washington, DC, 113–120.
Shin, Hyeon-Shic, and Kawamura, Kazuya (2006). “Development of disaggregate-level truck trip generation model: Case study of furniture chain stores.” Proc., Transportation Research Board 85th Annual Meeting, Washington, DC.
Spear, B., Giaimo, G., Curlee, T. R., and Neels, K. (2006). “Freight modeling: State of the practice.” Freight demand modeling: Tools for public-sector decision making. Transportation Research Board, Washington, DC
U.S. Bureau of Transportation Statistics (BTS) (2005). Trucks carry the most freight by weight and value, U.S. DOT, Washington, DC.
Wisinee, W., Kazushi, S., and Shoji, M. (2006). “Commodity distribution model incorporating spatial interactions for urban freight movement.” Proc., Transportation Research Board 85th Annual Meeting, Washington, DC.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 137 • Issue 7 • July 2011
Pages: 496 - 508
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Apr 21, 2010
Accepted: Sep 27, 2010
Published online: Oct 9, 2010
Published in print: Jul 1, 2011
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.