Study on modeling truck trip generation: Disaggregate approach based on the survey of industries

The goal of this research is to develop a robust methodology for estimating truck trip generation (TTG) at the disaggregate level of two retail chains. This study assumes that TTG is better modeled at the level of individual facilities, because TTG is an outcome of a series of business decisions based on supply chain management (SCM) strategies, and those decisions can be captured only at the disaggregate level.; In total, the data from 426 stores representing 9 national retail chains---5 furniture chains and 4 shoe chains---were collected by survey questionnaire, phone call and store visit. All stores were geocoded to the address level and socioeconomic information within 6-mile market area has been included in the final dataset.; Multiple regression analyses to identify the relationships between the amount of merchandise delivered to each store and the store and socioeconomic characteristics were built. The study found the store characteristics and locations were the most important independent variables for Furniture Chain A and the number of employees for Shoe Chain A.; Two best models using the logit regression analysis were applied to the test dataset, Furniture Chain E, to assess the transferability and compare the predictive power of two final models. The study found that, as far as Furniture Chains A and E are concerned, commonly used independent variables in past studies, such as the store floor space or the number of employees, are weak predictors of TTG. Although the study covered only two chains, this was consistent for both cases. Instead, the Inclusion of location and store type dummy variables almost always improved model's predictive power, often dramatically. In addition, the comparison between ITE trip generation rate and the best models were made. ITE trip rates were much higher than those from this study. This is probably due to different definition of trucks, sample size, and business classification systems, suggesting clear and consistent system of industry classification scheme should be developed in order to calibrate and apply disaggregate TTG models that are similar to the models presented in this dissertation, for the different sectors of industry.