This paper uses data from a trucking origin/destination study conducted with global positioning system (GPS) technology to develop a truck trip generation model for medium sized urban communities—in this study taken to be communities between 200,000 and 1,000,000 people. The difficulty with developing truck trip generation equations centers on the limitation of data. For passenger transportation, data are collected from household surveys. For truck transportation, if available, data are typically collected from a small collection of shippers/businesses within the urban area and extrapolated to cover the entire study area. Because of the data limitations, truck transportation is typically indirectly modeled or as an after-thought. Increasing truck volumes, coupled with cost saving strategies such as just-in-time delivery systems, require that transportation policymakers analyze infrastructure needs and make investment decisions that explicitly include truck volumes as a component. This paper contains a case study using a medium sized urban area and a GPS collected set of truck origins and destinations to develop a truck specific trip generation equation using standard employment data. The paper presents the models developed and validates the models to the case study community. The paper concludes that the trip generation equations developed can be incorporated into medium sized community travel models to provide a framework for truck planning that can be used to improve resource allocation decisions.
KeywordsTruck Trip Generation ModelsTravel ModelsEmployment Data
Alho, A. (2011). The Adequacy of Freight Modeling Methods to Study Policy/Regulations’ Implementation Impacts in a City Logistics Context. M.S. Thesis, Lisbon: Technical University of Lisbon.
Anderson, M. D., Dondapati, M. C., & Harris, G. A. (2013). Effectively Using the QRFM to Model Truck Trips in Medium-Sized Urban Communities. Journal of Transportation Technologies, 3, 185-189. https://doi.org/10.4236/jtts.2013.33018
Bastida, C., & Holguin-Veras, J. (2009). Freight Generation Models: Comparative Analysis of Regression Models and Multiple Classification Analysis. Transportation Research Record: Journal of the Transportation Research Board, 2097, 51-61. https://doi.org/10.3141/2097-07
Bradley, M., Bowman, J. L., & Griesenbeck, B. (2010). SACSIM: An Applied Activity-Based Model System with Fine-Level Spatial and Temporal Resolution. Journal of Choice Modeling, 3, 5-31. https://doi.org/10.1016/S1755-5345(13)70027-7
Cambridge Systematics, Inc. (1996). Quick Response Freight Manual. Federal Highway Administration. DTFH61-93-C-00075, DTFH61-93-C-00216.
Cambridge Systematics, Inc. (2007). Quick Response Freight Manual II. Federal Highway Administration. Publication No. FHWA-HOP-08-010.
Chow, J. Y. J., Yang, C. H., & Regan, A. C. (2010). State-of-the-Art of Freight Forecast Modeling: Lessons Learned and the Road Ahead. The Journal of Transportation, 37, 1011-1030. https://doi.org/10.1007/s11116-010-9281-1
Cohen, H., Horowitz, A., & Pendyala, R. (2008). NCHRP Report 606: Forecasting Statewide Freight Toolkit (pp. 5-15). Washington DC: Transportation Research Board of the National Academies, Report by Cambridge Systematics, Inc., & Global Insight (Formerly Reebie Associates). https://doi.org/10.1016/0191-2615(82)90037-6
Daly, A. (1982). Estimating Choice Models Containing Attraction Variables. Transportation Research Part B: Methodological, 16, 5-15. https://doi.org/10.17226/14133
Doustmohammadi, E., & Sisiopiku, V. (2016). Comparison of Freight Demand Forecasting Models. International Journal of Engineering Science Invention, 5, 45-51.
Doustmohammadi, E., Sisiopiku, V., & Sullivan, A. (2016b). Modeling Freight Truck Trips in Birmingham Using Tour-Based Approach. Journal of Transportation Technology, 6, 436-448. https://doi.org/10.4236/jtts.2016.65035
Doustmohammadi, E., Sisiopiku, V., Erson, M., Doustmohammadi, M., & Sullivan, A. (2016a). Comparison of Freight Demand Forecasting Models. International Journal of Traffic and Transportation Engineering, 5, 19-26.
Etz, A. (2018). Introduction to the Concept of Likelihood and Its Applications. Advances in Methods and Practices in Psychological Science, 1, 60-69. http://journals.sagepub.com/doi/pdf/10.1177/2515245917744314
Figliozzi, M. A. (2007). Analysis of the Efficiency of Urban Commercial Tour: Data Collection, Methodology, and Policy Implications. The Journal of Transportation Research Board, 41, 1014-1032. https://doi.org/10.1016/j.trb.2007.04.006
Fischer, M. J., Outwater, M. L., Cheng, L. L., Ahanotu, D. N., & Calix, R. (2005). An Innovative Framework for Modeling Freight Transportation in Los Angeles County. Los Angeles, CA: Los Angeles County Metropolitan Transportation Authority by Cambridge Systematics, Inc. https://doi.org/10.1177/0361198105190600113
Gliebe, J., Cohen, O., & Hunt, J. D. (2007). Dynamic Choice Model for Urban Commercial Activity Patterns of Vehicles and People. Transportation Research Record: Journal of the Transportation Research Board, 2003, 17-26. https://doi.org/10.3141/2003-03
Greaves, S. P., & Figliozzi, M. A. (2008). Collecting Commercial Vehicle Tour Data with Passive Global Positioning System Technology: Issues and Potential Applications. Transportation Research Record, 2049, 158-166.
Holguín-Veras, J., & Patil, G. R. (2008). Integrated Origin-Destination Synthesis Model for Freight with Commodity-Based and Empty Trip Models. Transportation Research Record: Journal of the Transportation Research Board, 2008, 60-66. https://doi.org/10.3141/2008-08
Holguín-Veras, J., & Thorson, E. (2000). Trip Length Distributions in Commodity-Based and Trip-Based Freight Demand Modeling. Transportation Research Record: Journal of the Transportation Research Board, 1707, 37-48. https://doi.org/10.3141/1707-05
Holguin-Veras, J., Jaller, M., Destro, L., Ban, X., Lawson, C., & Levinson, H. (2011). Freight Generation, Freight Trip Generation, and Perils of Using Constant Trip Rates. Transportation Research Record: Journal of the Transportation Research Board, 2224, 68-81. https://doi.org/10.3141/2224-09
Holguin-Veras, J., Sanchez-Diaz, I., Lawson, C., Jaller, M., Campbell, S., Levinson, H., & Shin, H.-S. (2013). Transferability of Freight Trip Generation Models. Transportation Research Record: Journal of the Transportation Research Board, 2379, 1-8. https://doi.org/10.3141/2379-01
Hunt, J. D., & Stefan, K. J. (2007). Tour-Based Microsimulation of Urban Commercial Movements in Alberta, Canada. Transportation Research Part B, 41, 981-1013. https://doi.org/10.1016/j.trb.2007.04.009
Jansuwan, S., Ryu, S., Chen, A., & Heaslip, K. (2014). A Two-Stage Approach for Estimating a Statewide Truck Trip Table (pp. 5-12). Report, Logan, UT: The Utah Transportation Center, Utah State University.
Jong, G., & Ben-Akiva, M. (2007). A Micro-Simulation Model of Shipment Size and Transport Chain Choice. Transportation Research Part B, 41, 950-965. https://doi.org/10.1016/j.trb.2007.05.002
Jong, G., Gunn, H., & Walker, W. (2004). National and International Freight Models: An Overview and Ideas for Further Development. Transport Reviews, 24, 103-124. https://doi.org/10.1080/0144164032000080494
Kuppam, A., Lemp, J., Beagan, D., Livshits, V., Nippani, S., & Vallabhaneni, L. (2014). Development of a Tour-Based Truck Travel Demand Model Using Truck GPS Data. In Proceedings, 93rd Transportation Research Board Annual Meeting (No. 14-4293). Washington DC: National Research Council.
North Carolina Department of Transportation (2009). North Carolina Truck Network Model Development Research.
Roorda, M., Hain, M., Amirjamshidi, G., Cavalcante, R., Abdulhai, B., & Woudsma, C. (2010). Exclusive Truck Facilities in Toronto, Ontario, Canada: Analysis of Truck and Automobile Demand. Transportation Research Record: Journal of the Transportation Research Board, 2168, 114-128. https://doi.org/10.3141/2168-14
Ruan, M., Lin, J., & Kawamura, K. (2011). Modeling Commercial Vehicle Daily Tour Chaining. Transportation Research Part E: Logistics and Transportation Review, 48, 1169-1184.
Samimi, A., Mohammadian, A., & Kawamura, K. (2010). Behavioral Paradigms for Modeling Freight Travel Decision-Making. In 12th International Conference on Travel Behavior Research.
Tavasszy, L., & Jong, G. (n.d.). Modelling Freight Transport (pp. 166-191). Amsterdam: Elsevier.
Wheeler, N., & Figliozzi, M. (2011). Multi-Criteria Freeway Performance Measures for Trucking in Congested Corridors. Transportation Research Record: Journal of the Transportation Research Board, 2224, 82-93. https://doi.org/10.3141/2224-10
Yang, C., Regan, A. C., & Son, Y. T. (2010). Another View of Freight Forecasting Modeling Trends. KSCE Journal of Civil Engineering, 14, 237-242. https://doi.org/10.1007/s12205-010-0237-y