Estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy: a case study of Zhengzhou City

ABSTRACTElectric Taxis (ETs) are the most favored alternatives to Gasoline Taxis (GTs) in cities that aim to reduce environmental pollution. How to develop a reasonable scale on which GTs are substituted by ETs remains a challenge to governments due to the dynamics and complexity of the taxi system....

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Main Authors: Zhixiang Fang, Xiaofan Wang, Ying Zhuang, Xianglong Liu
Format: Article
Language:English
Published: Taylor & Francis Group 2023-07-01
Series:Geo-spatial Information Science
Subjects:
Online Access:https://www.tandfonline.com/doi/10.1080/10095020.2022.2113342
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author Zhixiang Fang
Xiaofan Wang
Ying Zhuang
Xianglong Liu
author_facet Zhixiang Fang
Xiaofan Wang
Ying Zhuang
Xianglong Liu
author_sort Zhixiang Fang
collection DOAJ
description ABSTRACTElectric Taxis (ETs) are the most favored alternatives to Gasoline Taxis (GTs) in cities that aim to reduce environmental pollution. How to develop a reasonable scale on which GTs are substituted by ETs remains a challenge to governments due to the dynamics and complexity of the taxi system. To address this challenge, this paper develops a discrete-event-based simulation framework to simulate participants in the system and estimate the results under different substitution scales, which are helpful to understanding the status changing law of entities under different substitution scales, such as the operating indices of ETs, the unsatisfied travel requirements of passengers, and the usage state of charging facilities. The framework abstracts the behavioral process of ETs into three elements, namely, entity, behavior, and event. The entities are constructed from the information derived from the trajectory data. The behaviors are defined by rules following behavioral logic under anxiety psychology, which is caused by the limited range of ETs. The events are triggered based on rules from reality. With the help of this framework, a multi-objective optimization model is developed to obtain the optimal substitution scale of GTs in the case study area of Zhengzhou City. Overall, the approach could provide a practical tool to address this challenge, which could support further studies of the effect of ETs on urban taxis.
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spelling doaj.art-12c80820c8ca42bcad1d3f04eaca53382023-10-26T11:21:36ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532023-07-0126351453910.1080/10095020.2022.2113342Estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy: a case study of Zhengzhou CityZhixiang Fang0Xiaofan Wang1Ying Zhuang2Xianglong Liu3State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaKey Laboratory of Advanced Public Transportation Science, China Academy of Transportation Sciences, Beijing, ChinaABSTRACTElectric Taxis (ETs) are the most favored alternatives to Gasoline Taxis (GTs) in cities that aim to reduce environmental pollution. How to develop a reasonable scale on which GTs are substituted by ETs remains a challenge to governments due to the dynamics and complexity of the taxi system. To address this challenge, this paper develops a discrete-event-based simulation framework to simulate participants in the system and estimate the results under different substitution scales, which are helpful to understanding the status changing law of entities under different substitution scales, such as the operating indices of ETs, the unsatisfied travel requirements of passengers, and the usage state of charging facilities. The framework abstracts the behavioral process of ETs into three elements, namely, entity, behavior, and event. The entities are constructed from the information derived from the trajectory data. The behaviors are defined by rules following behavioral logic under anxiety psychology, which is caused by the limited range of ETs. The events are triggered based on rules from reality. With the help of this framework, a multi-objective optimization model is developed to obtain the optimal substitution scale of GTs in the case study area of Zhengzhou City. Overall, the approach could provide a practical tool to address this challenge, which could support further studies of the effect of ETs on urban taxis.https://www.tandfonline.com/doi/10.1080/10095020.2022.2113342Electric Taxi (ET)substitution scalediscrete-event simulationdecision supportmulti-objective optimization
spellingShingle Zhixiang Fang
Xiaofan Wang
Ying Zhuang
Xianglong Liu
Estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy: a case study of Zhengzhou City
Geo-spatial Information Science
Electric Taxi (ET)
substitution scale
discrete-event simulation
decision support
multi-objective optimization
title Estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy: a case study of Zhengzhou City
title_full Estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy: a case study of Zhengzhou City
title_fullStr Estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy: a case study of Zhengzhou City
title_full_unstemmed Estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy: a case study of Zhengzhou City
title_short Estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy: a case study of Zhengzhou City
title_sort estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy a case study of zhengzhou city
topic Electric Taxi (ET)
substitution scale
discrete-event simulation
decision support
multi-objective optimization
url https://www.tandfonline.com/doi/10.1080/10095020.2022.2113342
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