Truck trip forecasting based on commodity flows and I-O coefficients
Statewide truck trip forecasting based on commodity flows and Input-Output coefficients
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This work attempts to improve the modeling of statewide truck travel demand models by using commodity flow data from the Census, a private database (TRANSEARCH), and Input-Output (I-O) coefficients. The model developed adapted the four- stage Urban Transportation Planning modeling process to the state level and estimated trips for heavy trucks. Production and attraction rates in tons were derived at the county level using employment for 28 manufacture sectors. Annual tons at the county-level were converted to daily truck trips which were further disaggregated to the Traffic Analysis Zone level...
This work attempts to improve the modeling of statewide truck travel demand models by using commodity flow data from the Census, a private database (TRANSEARCH), and Input-Output (I-O) coefficients. The model developed adapted the four- stage Urban Transportation Planning modeling process to the state level and estimated trips for heavy trucks. Production and attraction rates in tons were derived at the county level using employment for 28 manufacture sectors. Annual tons at the county-level were converted to daily truck trips which were further disaggregated to the Traffic Analysis Zone level using zonal population as the disaggregation factor. An economic-based Input-Output software was used to derive the I-O direct matrix and the I-O direct coefficients at the state level for developing the trip attraction rates. The Commodity Flow Survey from the U.S. Census together with a private database were used to develop the trip production rates. The model developed performed well as a forecasting tool back-forecasted 15 years to the past. It may be used by planners and by the Wisconsin Department of Transportation for forecasting and operational applications.
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