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The Impacts of Multiscale Urban Road Network Centrality on Taxi Travel: A Case Study in Shenzhen

Author

Listed:
  • Dan Wang
  • Yongxi Gong
  • Xin Zhang
  • Yaoyu Lin

Abstract

As a crucial part of the urban system, road networks play a key role in the evolution of the urban structure. Therefore, studying the structural characteristics of urban road networks is pivotal for improving the efficiency of traffic network nodes and for relieving traffic pressure. This paper applies an urban road network analysis method to measure the centrality of the multiscale road network in Shenzhen, China. Taxi GPS data from October 17 to October 23, 2017, were selected for analysis of spatial distribution characteristics. This paper also established a regression model of taxi pick‐up and drop‐off frequency and road network centrality for further analysis. Several interesting observations were made. With respect to the increasing search radius, the closeness centrality indicator shifts from a multicentered distribution to a single‐centered distribution, while the betweenness centrality indicator shifts from a patchy distribution to a distribution along the main roads. In addition, the straightness centrality indicator turns from a dispersed distribution to a point‐axis distribution, concentrated in the southern part of the city. Second, there were variations between the centrality of the road network and the location of taxi pick‐up and drop‐off points. The regression model gets the highest value of R2, indicating a significant correlation in cases where the search radius is 3 km. Finally, the relationship exhibits a clear positive correlation between the betweenness centrality and taxi pick‐up and drop‐off points. On the other hand, closeness centrality is not correlated with these points. The straightness centrality has a negative correlation with the frequency of taxi pick‐up and drop‐off at 3 km and 8 km scale.

Suggested Citation

  • Dan Wang & Yongxi Gong & Xin Zhang & Yaoyu Lin, 2022. "The Impacts of Multiscale Urban Road Network Centrality on Taxi Travel: A Case Study in Shenzhen," Complexity, John Wiley & Sons, vol. 2022(1).
  • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:1780667
    DOI: 10.1155/2022/1780667
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    1. Sung, Hyungun & Choi, Keechoo & Lee, Sugie & Cheon, SangHyun, 2014. "Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership," Journal of Transport Geography, Elsevier, vol. 36(C), pages 134-140.
    2. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
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