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A platform for research: civil engineering, architecture and urbanism
Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen
https://orcid.org/0000-0001-9010-3158
Abstract Online ride-hailing has gradually become a popular travel choice worldwide, while it also brought policy challenges to balance the traditional taxi industry and online ride-hailing services. Understanding the operation patterns of urban online ride-hailing services is essential for government policy-making. However, insufficient attention has been paid to the operating characteristics of online ride-hailing vehicles due to limited empirical data. This paper proposes a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. The customer order and GPS data of online ride-hailing vehicles and traditional taxis in Xiamen, China is used in this study. The k-means++ clustering algorithm is used based on the proposed intensity and stability indices of ride-hailing vehicle operating characteristics. The results show that there are three types of online ride-hailing operation patterns, namely full-time (which accounts for 52.801%), part-time (29.502%), and occasional (17.697%). The operation pattern of full-time ride-hailing vehicles is similar to that of traditional taxis, but with lower intensity and stability due to a reduced workload and flexible time schedule. Part-time ride-hailing vehicles are operated unsteadily and irregularly in the drivers’ spare time, and the working time periods are mainly concentrated in the morning and evening peak hours. Occasional ride-hailing vehicles provide very limited service. Finally, several policy suggestions for online ride-hailing from the perspective of government management, e.g., the number of licenses and operation places and time periods, are proposed based on the results.
Highlights Proposed a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. Designed four clustering indices to reflect the operating intensity and stability of online ride-hailing. Analyzed the operation patterns of online ride-hailing in terms of temporal and spatial characteristics. Compared the operating characteristics between the full-time online ride-hailing vehicles and traditional taxis. Provided suggestions for taxi market management and refined policy making.
Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen
https://orcid.org/0000-0001-9010-3158
Abstract Online ride-hailing has gradually become a popular travel choice worldwide, while it also brought policy challenges to balance the traditional taxi industry and online ride-hailing services. Understanding the operation patterns of urban online ride-hailing services is essential for government policy-making. However, insufficient attention has been paid to the operating characteristics of online ride-hailing vehicles due to limited empirical data. This paper proposes a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. The customer order and GPS data of online ride-hailing vehicles and traditional taxis in Xiamen, China is used in this study. The k-means++ clustering algorithm is used based on the proposed intensity and stability indices of ride-hailing vehicle operating characteristics. The results show that there are three types of online ride-hailing operation patterns, namely full-time (which accounts for 52.801%), part-time (29.502%), and occasional (17.697%). The operation pattern of full-time ride-hailing vehicles is similar to that of traditional taxis, but with lower intensity and stability due to a reduced workload and flexible time schedule. Part-time ride-hailing vehicles are operated unsteadily and irregularly in the drivers’ spare time, and the working time periods are mainly concentrated in the morning and evening peak hours. Occasional ride-hailing vehicles provide very limited service. Finally, several policy suggestions for online ride-hailing from the perspective of government management, e.g., the number of licenses and operation places and time periods, are proposed based on the results.
Highlights Proposed a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. Designed four clustering indices to reflect the operating intensity and stability of online ride-hailing. Analyzed the operation patterns of online ride-hailing in terms of temporal and spatial characteristics. Compared the operating characteristics between the full-time online ride-hailing vehicles and traditional taxis. Provided suggestions for taxi market management and refined policy making.
Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen
https://orcid.org/0000-0001-9010-3158
Abstract Online ride-hailing has gradually become a popular travel choice worldwide, while it also brought policy challenges to balance the traditional taxi industry and online ride-hailing services. Understanding the operation patterns of urban online ride-hailing services is essential for government policy-making. However, insufficient attention has been paid to the operating characteristics of online ride-hailing vehicles due to limited empirical data. This paper proposes a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. The customer order and GPS data of online ride-hailing vehicles and traditional taxis in Xiamen, China is used in this study. The k-means++ clustering algorithm is used based on the proposed intensity and stability indices of ride-hailing vehicle operating characteristics. The results show that there are three types of online ride-hailing operation patterns, namely full-time (which accounts for 52.801%), part-time (29.502%), and occasional (17.697%). The operation pattern of full-time ride-hailing vehicles is similar to that of traditional taxis, but with lower intensity and stability due to a reduced workload and flexible time schedule. Part-time ride-hailing vehicles are operated unsteadily and irregularly in the drivers’ spare time, and the working time periods are mainly concentrated in the morning and evening peak hours. Occasional ride-hailing vehicles provide very limited service. Finally, several policy suggestions for online ride-hailing from the perspective of government management, e.g., the number of licenses and operation places and time periods, are proposed based on the results.
Highlights Proposed a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. Designed four clustering indices to reflect the operating intensity and stability of online ride-hailing. Analyzed the operation patterns of online ride-hailing in terms of temporal and spatial characteristics. Compared the operating characteristics between the full-time online ride-hailing vehicles and traditional taxis. Provided suggestions for taxi market management and refined policy making.
Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen
Xiong, Ziyue (author) / Jian Li (author) / Wu, Hangbin (author)
Transport Policy ; 101 ; 100-118
2020-12-15
19 pages
Article (Journal)
Electronic Resource
English
Traditional taxi, e-hailing or ride-hailing? A GSEM approach to exploring service adoption patterns
| Springer Verlag | 2024