Truck trip generation models for seaports with container and trailer operation
Engineering, Civil; Transportation; Transportation Science & Technology
Freight movement throughout the United States continues to evolve as a significant challenge to the transportation industry. Seaport operations dominated by container and trailer movements will require operational and infrastructure changes to maintain the growth of international cargo operations. Transportation planning models can be used to determine the needs of port and street network modifications. Described is the research and initial development process of models for predicting the levels of cargo truck traffic moving inbound and outbound at the Port of Miami. The models were restricted to container and trailer truck configurations that transport virtually all of the Port of Miami's freight. Consequently, this associated truck traffic moves through the nearby street network within downtown Miami. The purpose of the trip generation models is to predict volumes of large inbound and outbound trucks for specified time frames. The concern is to know how many large cargo vehicles are traveling on the only road leading to the port. Primary factors affecting truck volume were found to be the amount and direction of cargo vessel freight and the particular weekday of operation. Time series models for predicting seasonal variations in freight movements were developed as part of the study. These models are useful for long-term forecasts of the input variables used in the trip generation models. Truck trip generation models will provide transportation planners and public agencies with valuable information when making transportation management decisions and infrastructure modifications. This information also is necessary for prioritizing funds for roadway upgrade projects.
Transportation Data, Statistics, and Information Technology: Planning and Administration
"Truck trip generation models for seaports with container and trailer operation" (2000). Faculty Bibliography 2000s. 2419.