Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China

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 ov...

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Main Authors: Yikai Chen, Kai Wang, Yu Zhang, Renjia Luo, Shujun Yu, Qin Shi, Wenting Hu
Format: Article
Language:English
Published: Vilnius Gediminas Technical University 2020-05-01
Series:Transport
Subjects:
Online Access:https://journals.vgtu.lt/index.php/Transport/article/view/12635
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spelling doaj-d6fa730528ae491a9f1bf4f8c9fb62472021-07-02T15:33:44ZengVilnius Gediminas Technical UniversityTransport1648-41421648-34802020-05-0135323624610.3846/transport.2020.1263512635Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in ChinaYikai Chen0Kai Wang1Yu Zhang2Renjia Luo3Shujun Yu4Qin Shi5Wenting Hu6School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, ChinaDept of Civil and Environmental Engineering, University of South Florida, Tampa, United StatesSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, ChinaOverloading 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.https://journals.vgtu.lt/index.php/Transport/article/view/12635highway transportationoverloaded truckingsampling surveyclassification and regression tree (cart)binary logistic regression (blr)overloading inspection
collection DOAJ
language English
format Article
sources DOAJ
author Yikai Chen
Kai Wang
Yu Zhang
Renjia Luo
Shujun Yu
Qin Shi
Wenting Hu
spellingShingle Yikai Chen
Kai Wang
Yu Zhang
Renjia Luo
Shujun Yu
Qin Shi
Wenting Hu
Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China
Transport
highway transportation
overloaded trucking
sampling survey
classification and regression tree (cart)
binary logistic regression (blr)
overloading inspection
author_facet Yikai Chen
Kai Wang
Yu Zhang
Renjia Luo
Shujun Yu
Qin Shi
Wenting Hu
author_sort Yikai Chen
title Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China
title_short Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China
title_full Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China
title_fullStr Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China
title_full_unstemmed Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China
title_sort investigating factors affecting road freight overloading through the integrated use of blr and cart: a case study in china
publisher Vilnius Gediminas Technical University
series Transport
issn 1648-4142
1648-3480
publishDate 2020-05-01
description 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.
topic highway transportation
overloaded trucking
sampling survey
classification and regression tree (cart)
binary logistic regression (blr)
overloading inspection
url https://journals.vgtu.lt/index.php/Transport/article/view/12635
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