Using Commodity Flow Data for Predicting Truck Freight Flow on State Truck Routes.

摘要

The increase in truck traffic on highways has brought many problems and challenges to transportation planning and traffic operation, including traffic congestion, transportation system deficiency (insufficient truck parking, etc.), safety, infrastructure deterioration, environmental impacts (air quality and noise), economic development, and so forth. Along with the increase in truck traffic, the need for developing a statewide truck freight demand model has grown so that a state can estimate truck traffic at any point on its highways. The most significant hurdle to including freight transportation in the transportation modeling process is that most of the demand forecasting methodologies currently available were developed for passenger trips, not freight trips. This type of modeling methodology usually makes an assumption that freight trips follow the same behavioral mechanism as passenger trips. In order to overcome the weakness of using a typical four-step demand forecasting modeling process, the concept of commodity flow models (CFMs) can be used to develop a truck freight flow model. It is widely accepted that focusing on the freights enables CFMs to capture more accurately the fundamental economic mechanisms that drive freight movements. The type of commodity being carried is one of the most important characteristics of truck movements, and it is sometimes a challenge to obtain such information from the carriers. Thus, lately, the integration of the freight flow modeling and land use modeling has emerged as an alternate tool to estimate freight movements than the previously developed models. In this study, county-level multiple regression models relating land use to commodity flow were developed using a geographical information system and statistics. Then, a statistical/mathematical statewide commodity flow distribution model was developed by using a physical friction factor (physical distance), a statistical friction factor (Euclidean distance), and economic factors (differences of population and difference of employment among the counties). The commodity flow distributed among truck traffic analysis zones (TTAZs) by the statewide commodity flow distribution model were converted to truck trips and the resulting truck trips were assigned to Utah's truck routes using the all-or-nothing assignment procedure of TransCAD and a genetic algorithm. Truck freight data from the US Census Bureau's Commodity Flow Surveys, which have become available to the public for free via the Internet, enabled the development of a commodity flow based statewide truck freight demand model. It was found that the integration of the freight flow and land use data could be a practical method for modeling tuck traffic demand on state-wide truck routes although the current level of data availability on commodity flow and land use data still constrains the full capability of this type of modeling.