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,...

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Autores principales:	Hui Gao, Lutong Yang, Anyue Zhang, Mingxin Sheng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electric vehicle travel rule statistics M/M/c queuing theory improved Bass model ownership prediction Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/24861ed58d08436a8f70ef1e87a23b83
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spelling	oai:doaj.org-article:24861ed58d08436a8f70ef1e87a23b832021-11-25T19:06:21ZAnalysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction10.3390/sym131120522073-8994https://doaj.org/article/24861ed58d08436a8f70ef1e87a23b832021-10-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2052https://doaj.org/toc/2073-8994In 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.Hui GaoLutong YangAnyue ZhangMingxin ShengMDPI AGarticleelectric vehicletravel rule statisticsM/M/c queuing theoryimproved Bass modelownership predictionMathematicsQA1-939ENSymmetry, Vol 13, Iss 2052, p 2052 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electric vehicle travel rule statistics M/M/c queuing theory improved Bass model ownership prediction Mathematics QA1-939
spellingShingle	electric vehicle travel rule statistics M/M/c queuing theory improved Bass model ownership prediction Mathematics QA1-939 Hui Gao Lutong Yang Anyue Zhang Mingxin Sheng Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction
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.
format	article
author	Hui Gao Lutong Yang Anyue Zhang Mingxin Sheng
author_facet	Hui Gao Lutong Yang Anyue Zhang Mingxin Sheng
author_sort	Hui Gao
title	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_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_sort	analysis of urban electric vehicle trip rule statistics and ownership prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/24861ed58d08436a8f70ef1e87a23b83
work_keys_str_mv	AT huigao analysisofurbanelectricvehicletriprulestatisticsandownershipprediction AT lutongyang analysisofurbanelectricvehicletriprulestatisticsandownershipprediction AT anyuezhang analysisofurbanelectricvehicletriprulestatisticsandownershipprediction AT mingxinsheng analysisofurbanelectricvehicletriprulestatisticsandownershipprediction
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Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction

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