Time-dependent effects in freight trip generation models

Abstract

Freight trip generation models are a key input to transportation system planning studies, transportation infrastructure design and policy making processes since estimations from these models are commonly used to forecast the traffic impact of freight transportation. Therefore, accurate models for freight trip generation are vital to avoid negative impacts on the costs of transportation system improvement initiatives and prevent the implementation of ineffective policies with the consequent misuse of public funds. A common assumption in the case of passenger trip generation applications is that models are stable over the time and can be used for different periods of time. This is not necessarily the case for freight trip generation models since the number of trips either attracted or produced by a commercial establishment is an expression of a multiplicity of economic factors subject to variation over the time.

The results suggest that time-dependent effects are highly significant in freight trip generation models and ignoring such effects when using these models to forecast traffic demand could lead to serious under/overestimation errors that could cause planning processes, regulations and policies based on these models to be completely ineffective. The comparative analysis conducted reveal that models including time-dependent effects and revenue have a good fit to the data and produce better estimations than those reported in previous studies. The study also demonstrates that models including time-dependent effects and revenue are highly responsive to market disruptions such as those caused by a financial crisis. These types of models are also well-suited to be used with both explanatory and forecasting purposes.

This research conducts an extensive modeling process to determine and analyze time-dependent effects in freight trip generation models and their implications for planning and policy making purposes. The set of models estimated for freight trip generation includes fixed and continuous time-dependent factors with different functional forms. Models that include revenue are also analyzed along with their effects and interaction with time-dependent variables. A comparative analysis with previous models from the literature related to freight trip generation is also presented.