Development of a Statewide Truck Trip Forecasting Model Based on Commodity Flows and Input-Output Coefficients

Abstract

This research attempts to improve the modeling of statewide truck travel demand models by using commodity flow data from the U.S. Census Bureau, a private freight database (TRANSEARCH), and input-output (I-O) coefficients. The standard urban transportation planning modeling process was applied at the state level to estimate heavy truck trips. Economic-based I-O 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 for 28 manufacturing sectors. The Commodity Flow Survey from the U.S. Census Bureau together with a private database developed for Wisconsin were used to develop the trip production rates. Transportation planning software (TRANPLAN) was used to distribute and assign truck trips generated at the zonal level. The selected-link function in TRANPLAN was used to adjust the initial productions and attractions in order to generate link volumes that match the actual ground counts for 40 selected links. The model only required two iterations of the selected link analysis in order to produce an acceptable match with the ground counts, compared with three iterations for two prior similar models. The rapid convergence provides clear evidence that the disaggregate trip generation models give better initial estimates of trip productions and attractions than was possible with the prior studies. A “back forecast” of 15 years to the year 1977 was found to be reasonable both in terms of the percent root mean square error by volume group and the performance measures for five screen lines.

Nationwide Freight Generation Models: A Spatial Regression Approach

“…There are also sources that provide modeling guidance for site specific and regional level freight planning (USACE 2005;Cambridge Systematics 1997). Much of the freight generation literature focuses on state level or metropolitan level demand (Aultman-Hall et al 2000;Sorratini and Smith 2000;Eatough et al 2000;Fisher et al 2000;Huang and Smith 1999). Because much of the freight transportation in the United States spans multiple states and/or regions, there is a need to focus modeling efforts on national freight generation issues.…”

Freight Generation and Freight Trip Generation Models

“…Studies of port FTG have been conducted by Guha & Walton (1993) (2008) and Orsini, Gavaud, & Bourhis (2009). In addition to rates and regression models, times series models (Garrido, 2000), inputÀoutput (Sorratini, 2000), neural networks (AlDeek, 2001) and other related models have been used for FTG. InputÀoutput and spatial computable general equilibrium (SCGE) models are recurrently used to estimate FP and FA at the zonal level (see also Chapter 2 of this volume).…”

Transportation Energy Futures Series: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future

“…• Commodity-based and IO models that estimate current and forecasted freight traffic generation and distribution by linking economic activity to associated commodity flows, and converting those flows to truck and rail trips (Kim et al, 2004) (Sorratini 2000;Lawrence and Kleinman 1997).…”

“…The paper concludes that the CFS freight demand models could be enhanced with capabilities to disaggregate origin-destination tables to county or zip code levels or to assess regional economic impacts associated with service interruptions. Sorratini (2000). "Estimating Statewide Truck Trips Using Commodity Flows and Input-Output Coefficients."…”