Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled
In this study, gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models were constructed to systematically ascertain the influencing factors and electric vehicle (EV) use action laws from the perspective of travelers. The use intensity of EVs was represented by electric vehicle...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/12/4/247 |
| _version_ | 1797499753826615296 |
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| author | Xinghua Hu Yanshi Cao Tao Peng Runze Gao Gao Dai |
| author_facet | Xinghua Hu Yanshi Cao Tao Peng Runze Gao Gao Dai |
| author_sort | Xinghua Hu |
| collection | DOAJ |
| description | In this study, gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models were constructed to systematically ascertain the influencing factors and electric vehicle (EV) use action laws from the perspective of travelers. The use intensity of EVs was represented by electric vehicle miles traveled (eVMT); variables such as the charging time, travel preference, and annual income were used to describe the travel characteristics. Seven variables, including distance to the nearest business district, road density, public transport service level, and land use mix were extracted from different dimensions to describe the built environment, explore the influence of the travel behavior mode and built environment on EV use. From the eVMT survey data, points of interest (POI) data, urban road network data, and other heterogeneous data from Chongqing, an empirical analysis of EV usage intensity was conducted. The results indicated that the deviation of the GBDT model (9.62%) was 11.72% lower than that of the OLS model (21.34%). The charging time was the most significant factor influencing the service intensity of EVs (18.37%). The charging pile density (15.24%), EV preference (11.52%), and distance to the nearest business district (10.28%) also exerted a significant influence. |
| first_indexed | 2024-03-10T03:51:58Z |
| format | Article |
| id | doaj.art-28157778eaea469296c5f17764d91fcc |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-10T03:51:58Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-28157778eaea469296c5f17764d91fcc2023-11-23T11:03:44ZengMDPI AGWorld Electric Vehicle Journal2032-66532021-11-0112424710.3390/wevj12040247Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles TraveledXinghua Hu0Yanshi Cao1Tao Peng2Runze Gao3Gao Dai4School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, ChinaKey Laboratory of Operation Safety Technology on Transport Vehicles, Ministry of Transport of the People’s Republic of China, Beijing 100088, ChinaChongqing YouLiang Science & Technology Co., Ltd., Chongqing 408319, ChinaIn this study, gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models were constructed to systematically ascertain the influencing factors and electric vehicle (EV) use action laws from the perspective of travelers. The use intensity of EVs was represented by electric vehicle miles traveled (eVMT); variables such as the charging time, travel preference, and annual income were used to describe the travel characteristics. Seven variables, including distance to the nearest business district, road density, public transport service level, and land use mix were extracted from different dimensions to describe the built environment, explore the influence of the travel behavior mode and built environment on EV use. From the eVMT survey data, points of interest (POI) data, urban road network data, and other heterogeneous data from Chongqing, an empirical analysis of EV usage intensity was conducted. The results indicated that the deviation of the GBDT model (9.62%) was 11.72% lower than that of the OLS model (21.34%). The charging time was the most significant factor influencing the service intensity of EVs (18.37%). The charging pile density (15.24%), EV preference (11.52%), and distance to the nearest business district (10.28%) also exerted a significant influence.https://www.mdpi.com/2032-6653/12/4/247electric vehiclebuilt environmenteVMTgradient boosting decision treenonlinear influence |
| spellingShingle | Xinghua Hu Yanshi Cao Tao Peng Runze Gao Gao Dai Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled World Electric Vehicle Journal electric vehicle built environment eVMT gradient boosting decision tree nonlinear influence |
| title | Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled |
| title_full | Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled |
| title_fullStr | Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled |
| title_full_unstemmed | Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled |
| title_short | Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled |
| title_sort | nonlinear influence model of built environment of residential area on electric vehicle miles traveled |
| topic | electric vehicle built environment eVMT gradient boosting decision tree nonlinear influence |
| url | https://www.mdpi.com/2032-6653/12/4/247 |
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