Affiliation:
1. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China
2. Dept of Civil and Environmental Engineering, University of South Florida, Tampa, United States
Abstract
Overloading of road freight vehicles accelerates road damage, creates unfair competition
in the transport market, and increases safety risk. There is a dearth of research on the mining of data of
highway Freight Weight (FW), and this paper therefore aims to discover factors affecting road freight overloading
based on highway FW data, with a view of developing strategies to mitigate such occurrences. A comprehensive
sampling survey of road freight transportation was conducted in Anhui Province (China). Vehicle Characteristics
(VC), Operation Mode (OM), and transportation information from a total of 3248 trucks were collected. In order
to take advantage of the strengths associated with both statistical modelling techniques and non-parametric methods,
a Classification And Regression Tree (CART) technique was integrated with Binary Logistic Regression (BLR) to reveal
the factors affecting road freight overloading. The classification efficacy test shows that the BLR–CART method
outperformed the BLR method in term of accuracy. It is also revealed that the factors affecting overloading of
freight vehicles are the Type of Transportation (ToT), Rated Load (RL), OM, FW during the investigation period,
interaction between RL and FW, and interaction among RL, FW, and Average Haul Distance (AHD). Road transport
authorities should pay greater attention to these factors in order to improve efficiency and effectiveness of
overloading inspection.
Publisher
Vilnius Gediminas Technical University
Subject
Mechanical Engineering,Automotive Engineering
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