Hierarchical Regression Model for Analyzing Truck Freight Demand

Abstract:

Trucks play a significant role in transporting freight through the ground transportation systems, and it is important to be able to estimate and predict the amount of freight in a highway network. In this study, truck weight and volume data, obtained from Weigh-in-Motion stations in California, are analyzed to examine temporal and seasonal patterns. Then coupled with socio- economic variables, a hierarchical regression model is established for estimating freight demand. The model reveals that truck freight demand can be estimated by the truck annual average daily traffic along with the population, number of firms and median income at that county level. What is more, it has a predicting capability of freight weight where no truck weight data is measured. Therefore it can be served for practitioners to predict the growth of freight demand in a freeway network, and is useful to achieve the optimal flows design of cargo at all segments, regardless of whether or not weight data is available.

• Periodical: Applied Mechanics and Materials (Volumes 178-181)
• Pages: 2752-2756
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.178-181.2752
• Online since: May 2012
• Authors: Hui Li Wang, Zhong Ke Shi
• Keywords: Freight Demand, Hierarchical Regression Model, Seasonal Variation, Temporal Variation

References

[1] Our Nation's Highways: 2010. U.S. Department of Transportation Federal Highway Administration, (2010).

[2] http://www.bts.gov/publications/commodity_flow_survey/final_tables_december_2009/html/table_01.html, Table 1, Shipment Characteristics by Mode of Transportation for the United States: (2009).

[3] Transportation Reboot: Restarting America's Most Essential Operating System: Unlocking Freight. American Association of State Highway and Transportation Officials, (2010).

[4] Sorratini, J. A., Smith, R. L., Development of a Statewide Truck Trip Forecasting Model Based on Commodity Flows and Input-Output Coefficients. Transportation Research Record 1707, (2000)

DOI: 10.3141/1707-06

[5] Fite, J. T., Don Taylor, G., Usher, J. S., English, J. R., Roberts, J. N., Forecasting freight demand using economic indices. International Journal of Physical Distribution & Logistics Management Vol 32, No.4 (2002), P:299-308.

DOI: 10.1108/09600030210430660

[6] Fernandez, L. J. E., de Cea Ch., J., Soto A. O., A multi-modal supply-demand equilibrium model for predicting freight flows. Transportation Research Part B: Methodological Vol 37, No.7, (2003), P: 615-640.

DOI: 10.1016/s0191-2615(02)00042-5

[7] Garrido, R. A. and Mahmassani, H. S., Forecasting freight transportation demand with the space-time multinomial probit model. Transportation Research Part B: Methodological Vol 34, No. 5, (2000), P: 403-418.

DOI: 10.1016/s0191-2615(99)00032-6

[8] Rabe-Hesketh, S. and Skrondal, A., Multilevel and longitudinal modeling using stata, College Station, TX: Stata Press. (2005).

DOI: 10.1177/0962280208101868