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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Vilnius Gediminas Technical University
2020-05-01
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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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