Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction
In order to play the important role of electric vehicles to promote the realization of the 3060 double carbon target, electric vehicles have seen explosive growth. However, due to the tight symmetry between the number and distribution of electric vehicles and their corresponding charging facilities,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/11/2052 |
| _version_ | 1797508351528009728 |
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| author | Hui Gao Lutong Yang Anyue Zhang Mingxin Sheng |
| author_facet | Hui Gao Lutong Yang Anyue Zhang Mingxin Sheng |
| author_sort | Hui Gao |
| collection | DOAJ |
| description | In order to play the important role of electric vehicles to promote the realization of the 3060 double carbon target, electric vehicles have seen explosive growth. However, due to the tight symmetry between the number and distribution of electric vehicles and their corresponding charging facilities, the layout of charging facilities has higher requirements. This paper collects travel data in the form of a traffic travel questionnaire for electric vehicle users. Based on the vehicle parking demand model of the queuing theory and Monte Carlo simulation, the paper gives the number of stopping vehicles and the time of vehicles stopping in different places such as residential areas, workplaces, supermarket parking and roadside. In addition, based on the Bass prediction model, the main parameters are modeled in the model, and the price correction coefficient is introduced. The improved Bass model is used to predict the growth trend of electric vehicles in different regions in different years and in different incentive sites. By predicting the ownership of urban electric vehicles and accurately grasping the distribution and operation of electric vehicles, this paper can provide guidance and suggestions for the planning and construction of charging facilities in different regions, effectively reduce the investment cost of charging facilities and guide local governments to formulate reasonable planning schemes. |
| first_indexed | 2024-03-10T05:00:52Z |
| format | Article |
| id | doaj.art-24861ed58d08436a8f70ef1e87a23b83 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T05:00:52Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-24861ed58d08436a8f70ef1e87a23b832023-11-23T01:44:05ZengMDPI AGSymmetry2073-89942021-10-011311205210.3390/sym13112052Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership PredictionHui Gao0Lutong Yang1Anyue Zhang2Mingxin Sheng3College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaIn order to play the important role of electric vehicles to promote the realization of the 3060 double carbon target, electric vehicles have seen explosive growth. However, due to the tight symmetry between the number and distribution of electric vehicles and their corresponding charging facilities, the layout of charging facilities has higher requirements. This paper collects travel data in the form of a traffic travel questionnaire for electric vehicle users. Based on the vehicle parking demand model of the queuing theory and Monte Carlo simulation, the paper gives the number of stopping vehicles and the time of vehicles stopping in different places such as residential areas, workplaces, supermarket parking and roadside. In addition, based on the Bass prediction model, the main parameters are modeled in the model, and the price correction coefficient is introduced. The improved Bass model is used to predict the growth trend of electric vehicles in different regions in different years and in different incentive sites. By predicting the ownership of urban electric vehicles and accurately grasping the distribution and operation of electric vehicles, this paper can provide guidance and suggestions for the planning and construction of charging facilities in different regions, effectively reduce the investment cost of charging facilities and guide local governments to formulate reasonable planning schemes.https://www.mdpi.com/2073-8994/13/11/2052electric vehicletravel rule statisticsM/M/c queuing theoryimproved Bass modelownership prediction |
| spellingShingle | Hui Gao Lutong Yang Anyue Zhang Mingxin Sheng Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction Symmetry electric vehicle travel rule statistics M/M/c queuing theory improved Bass model ownership prediction |
| title | Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction |
| title_full | Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction |
| title_fullStr | Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction |
| title_full_unstemmed | Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction |
| title_short | Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction |
| title_sort | analysis of urban electric vehicle trip rule statistics and ownership prediction |
| topic | electric vehicle travel rule statistics M/M/c queuing theory improved Bass model ownership prediction |
| url | https://www.mdpi.com/2073-8994/13/11/2052 |
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