What Kind of Travellers Are Using Carsharing in Beijing? A Study Based on Selective Ensemble Learning

Wei Luo, Yi Wang, Pengpeng Jiao, Zehao Wang and Pengfei Zhao
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Wei Luo: Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Yi Wang: Department of Civil Engineering, Tsinghua University, Beijing 100091, China
Pengpeng Jiao: Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Zehao Wang: Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Pengfei Zhao: Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Sustainability, 2022, vol. 14, issue 1, 1-24

Abstract: As a new urban travel mode, carsharing is significantly different from private cars, buses and other travel modes. Therefore, clarifying the typical characteristics of carsharing, such as individual users’ attributes, travel environment and travel behaviour, is conducive to accurately grasping the development of carsharing. In this study, a selective ensemble learning model is established to analyse typical travel characteristics of carsharing. Firstly, personal characteristics, environmental characteristics and behavioural characteristics were obtained through integrating order data, global positioning system data and station information. Then, based on a consolidated view of carsharing, different types of carsharing travel characteristics were distinguished using selective ensemble learning. Lastly, all kinds of carsharing travel are described in detail. It was identified through this research that carsharing travel can be divided into four kinds: long distance for leisure and entertainment, medium and short distances for business and commuting, a mixed category of medium and short distances for business and residence, and a mixed category of long distance for business and residence. This study can provide a theoretical reference and practical basis for precise planning and design and the scientific operation of carsharing.

Keywords: carsharing; travel characteristics; selective ensemble learning; Beijing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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