Modelling Freight Trip Generation Based on Deliveries for Brazilian Municipalities

Abstract

Freight trip generation modelling is important for forecasting freight movements, and understanding freight movements is crucial to enabling sustainable freight transportation planning. The existing literature focuses on model development, and most of the previous models are estimated by ordinary least squares regression. However, few studies have carefully considered the OLS assumptions. The objective of this paper is to estimate freight trip generation models using deliveries to commercial establishments in Brazilian municipalities. A procedure is described to estimate models by ordinary least squares (OLS), and alternative techniques are considered to address the violations of the OLS assumptions. The analysis was conducted with data from 860 commercial establishments in nine Brazilian municipalities, and models were estimated for capital, non-capital, small, medium, and larger municipalities. The findings showed that alternative techniques to OLS regression can provide better-estimated parameters and more accurate results. Not evaluating the OLS assumptions could compromise the quality of the model and, consequently, planning using these models. Moreover, the results showed that the number of employees has a more significant influence in small cities and a lower influence in medium-sized municipalities. Finally, the findings demonstrated the importance of local models that include the municipalities’ characteristics and that can support freight transportation planning. These models can also include sustainable strategies for freight transport.

Travel pattern analysis and freight trip generation modeling of textile commodities. Case study: Thamrin City Shopping Center, Jakarta

“…Based on previous research, several factors affect FG and FTG are raw materials, production goods [6], land area [7], number of employees [7]- [14], distance to market [9], [10], and type of industry [10], [12], [13]. In addition, several methods are used for this modeling, such as linear regression analysis [8], [10], [11], [15], ross-classification analysis [12], and multiple classification analysis [9], [12]. Linear regression and multiple classification analyses are suggested for FG/FTG modeling due to their simplicity in modeling and presentation of results [12].…”

“…Linear regression and multiple classification analyses are suggested for FG/FTG modeling due to their simplicity in modeling and presentation of results [12]. In addition, linear regression analysis is the most frequently used method in estimating freight trip generation [8]. However, in conducting regression analysis or OLS regression modeling, it is necessary to evaluate the assumptions used because it will affect the quality and accuracy of the modeling.…”