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

ABSTRACT

Electric 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.

KEYWORDS:

• Electric Taxi (ET)
• substitution scale
• discrete-event simulation
• decision support
• multi-objective optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research was supported in part by the National Natural Science Foundation of China [Grant 41771473] and Fundamental Research Funds for the Central Universities [Grant 2042020kfxg24].

Notes on contributors

Zhixiang Fang

Zhixiang Fang is currently a Professor with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. His research interests include crowd dynamics-oriented observation, human mobility, and intelligent navigation.

Xiaofan Wang

Xiaofan Wang received MS degree from Wuhan University. Her research interests include geospatial big data analytic and smart transportation.

Ying Zhuang

Ying Zhuang received MS degree from Wuhan University. She was also a Visiting Scholar with the Singapore-MIT Alliance for Research and Technology (SMART). Her research interests include geospatial big data analytic and smart transportation.

Xianglong Liu

Xianglong Liu is currently a researcher with Key Laboratory of Advanced Public Transportation Science, China Academy of Transportation Sciences. His research interests include smart public transportation, and MaaS.