Effect of the measurement period and spatial dependence on the accuracy of urban freight trip generation models

Despite recent advancements in the Freight Trip Generation (FTG) modelling literature, there is a lack of understanding on the effect of the choice of a regression model, measurement period (daily/weekly FTG), and spatial dependence on model fit and freight-related policies. This study addresses these research gaps by developing non-spatial and spatial autoregressive multiple linear regression and count models for daily and weekly Freight Trip Production (FTP) and Freight Trip Attraction (FTA). We model Freight Shipments (FS) as FTP and Freight Deliveries (FD) as FTA. The results show that the best model for daily and weekly FTP is the spatial Zero-Inflated Negative Binomial (ZINB) model. The best daily and weekly FTA model is the non spatial Negative Binomial (NB) model. The findings indicate the presence of spatial dependence in the best FTP model, while it is absent in the best FTA model. The elasticity analysis shows that daily models may lead to bias and inaccurate prediction of policy impacts. The study recommends using count models that capture more FTG characteristics with a week as the measurement period and consider spatial dependence, if present.

Automated summary

This study addresses the research gaps in understanding the effect of regression models, measurement period, and spatial dependence on Freight Trip Generation (FTG) modeling and freight-related policies. The results show that the spatial Zero-Inflated Negative Binomial (ZINB) model is the best for daily and weekly Freight Trip Production (FTP), while the non-spatial Negative Binomial (NB) model is the best for daily and weekly Freight Trip Attraction (FTA). The study also highlights the importance of considering spatial dependence and using count models with a week as the measurement period.