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author:

Yao, Wei (Yao, Wei.) [1] | Zhao, Zhiyuan (Zhao, Zhiyuan.) [2] | Wu, Sheng (Wu, Sheng.) [3]

Indexed by:

EI PKU CSCD

Abstract:

The term 'equality ' refers to the fairness of rights or resource distribution among social group members, which is crucial for traffic planning. The accessibility of public transport reflects whether residents of corresponding geographical analysis unit have reasonable access to public transports given the size of their population. Public taxi service (including both ride-hailing taxi and traditional taxi) is the primary means to meet urban residents’personalized daily demand for public transport. Exploring the equality of public taxi services can provide support for optimizing personalized travel service in urban public travel. In this study, based on the taxi trajectory and dynamic population data of Xiamen, we used Gini coefficient and Theil index to calculate the equality of taxi service for three types of taxi service, i.e., cruise only, ride-hailing only, and mixed receiving taxi service. Then we analyzed the contribution of urban functional areas to equality of tax service and extracted areas with potential inequity. The results show that the proposed method can quantify the inequality of different taxi service and its changes under the context of COVID-19, based on the reclassification of taxis service. Specifically, (1) after the COVID-19 outbreak, the number of vehicles providing ride-hailing service decreased by 18.8% in Xiamen, with a 10% increase in service inequality relative to the dynamic population, while the number of taxis using cruising only to pick up orders remained relatively stable, and the inequality was also at a high level constantly; (2) In Xiamen city the overall inequality of ride-hailing only service was 66% that of cruise only service. The use of online car hailing platform improved the fairness of personalized public travel services for urban residents; (3) the equality of taxi service differed significantly among different types of urban functional areas. For cruising only service, 57.49% of the overall inequality was contributed by commercial land use in Xiamen. For ride-hailing only service, industrial and tourism land contributed 35.07% and 19.32% of the inequality, respectively. After the COVID-19 outbreak, the contribution of tourism land to the inequality of online shopping service increased significantly, by 81.67% compared to the pre-epidemic period; (4) Xiamen City contains two parts: inside-island and outside-island. The number of potentially inequitable service areas was much larger outside-island than inside-island. Areas with a low level of passenger service but a high level dynamic population are typically located in the island's natural parks, while areas with a high level of passenger service but a low level dynamic population are located beyond the island's natural parks; (5) Compared with the results based on the dynamic population, the inequality based on the static population assessment was overestimated by up to 89.25%. The research results of this paper demonstrate that the theoretical framework and analysis method can systematically reveal the inequality characteristics of public taxi service, providing guiding principle in urban traffic optimization. © 2023 Journal of Geo-Information Science. All rights reserved.

Keyword:

Cell proliferation COVID-19 Land use Motor transportation Population statistics Taxation Taxicabs Tourism Urban transportation

Community:

  • [ 1 ] [Yao, Wei]Academy of Digital China (Fujian), Fuzhou University, Fuzhou; 350003, China
  • [ 2 ] [Yao, Wei]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350003, China
  • [ 3 ] [Zhao, Zhiyuan]Academy of Digital China (Fujian), Fuzhou University, Fuzhou; 350003, China
  • [ 4 ] [Zhao, Zhiyuan]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350003, China
  • [ 5 ] [Zhao, Zhiyuan]The Digital Economy Alliance of Fujian, Fuzhou; 350116, China
  • [ 6 ] [Wu, Sheng]Academy of Digital China (Fujian), Fuzhou University, Fuzhou; 350003, China
  • [ 7 ] [Wu, Sheng]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350003, China
  • [ 8 ] [Wu, Sheng]The Digital Economy Alliance of Fujian, Fuzhou; 350116, China

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Source :

地球信息科学学报

ISSN: 1560-8999

Year: 2023

Issue: 8

Volume: 25

Page: 1637-1654

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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