2019
DOI: 10.3390/su11205761
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A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Abstract: Electric vehicles (EVs) are promising alternatives to replace traditional gasoline vehicles. The relationship between available charging stations and electric vehicles has to be precisely coordinated to facilitate the increasing promotion and usage of EVs. This paper aims to investigate the choice of the charging location with global positioning system (GPS) trajectories of 700 Plug-in Hybrid Electric Vehicle (PHEV) users as well as the charging facility data in Shanghai. First, the recharge accessibility of e… Show more

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Cited by 21 publications
(10 citation statements)
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References 28 publications
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“…e traditional density-based spatial clustering of applications with noise (DBSCAN) algorithm [14][15][16] clusters trajectory points; however, because this algorithm is datadriven and is sensitive to the input data when mining trajectory points, it is limited by the required computing space and has poor speed. Such shortcomings are manifested in terms of (1) different parameter combinations in the DBSCAN algorithm that will greatly affect the clustering results and the parameter values that are generally determined empirically and (2) the clustering quality that will drop if the taxi pick-up points have a nonuniform distribution and the clustering distances are largely different.…”
Section: Related Researchmentioning
confidence: 99%
“…e traditional density-based spatial clustering of applications with noise (DBSCAN) algorithm [14][15][16] clusters trajectory points; however, because this algorithm is datadriven and is sensitive to the input data when mining trajectory points, it is limited by the required computing space and has poor speed. Such shortcomings are manifested in terms of (1) different parameter combinations in the DBSCAN algorithm that will greatly affect the clustering results and the parameter values that are generally determined empirically and (2) the clustering quality that will drop if the taxi pick-up points have a nonuniform distribution and the clustering distances are largely different.…”
Section: Related Researchmentioning
confidence: 99%
“…In recent years, the rapid development of sensor data collection and communication technology has greatly facilitated the collection of taxi data, making it possible to use big data techniques to improve urban taxi service, such as in the areas of exhaust emission reduction [21], research on the contribution of road traffic to overall air pollution [22], taxi service demand [23], and traffic congestion [22,24,25]. These and other advances have played a positive guiding role in urban planning and provided new insights for the deployment of urban public toll collection facilities [26]. Among them, the analysis of urban traffic congestion based on GPS positioning data has become one of the top research issues.…”
Section: Related Workmentioning
confidence: 99%
“…They evaluated extensive data from a total of six million charging events from 64 000 electric vehicle users. Yun et al (2019) analyzed real-time data from 90% of charging stations in Shanghai for charging infrastructure planning. Hardinghaus et al (2020) studied the spatial distribution of charging stations and their demand for the city of Berlin.…”
Section: Introductionmentioning
confidence: 99%
“…Other studies discuss or calculate scenarios with low utilization rates, e.g. Yi et al (2020), Yun et al (2019) or Muratori et al (2021), without measuring exact real-world utilization for charging stations.…”
Section: Introductionmentioning
confidence: 99%