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Author:

Zhang, Tao (Zhang, Tao.) | Yan, Yibo (Yan, Yibo.) | Chen, Qi (Chen, Qi.) | Liu, Ze (Liu, Ze.)

Indexed by:

SSCI Scopus

Abstract:

Given the accelerating speed and scale of urbanization in China, a rational formulation of a composite development plan to increase the vitality and value of various areas is required. Thus, this study proposes a method for evaluating the spatial relationship among facilities around bus terminals by combining urban points-of-interest data and street view image data from two perspectives: the current level of development and potential of the terminals, and an evaluation of the surrounding pedestrian environment. This is in response to the lack of quantitative descriptions of the composite development of existing bus terminals. The validity and applicability of the methods are verified using the samples of five planned composite development bus terminals in the city of Zhengzhou. These results offer strategic suggestions for the composite development of the Zhengzhou bus terminals. This study demonstrates innovation in integrating geographic information data and street view image data. It reflects the spatial characteristics of the built environment using geographic information data and the visual characteristics of the built environment using street view images.

Keyword:

spatial relationship semantic segmentation bus terminal machine learning built environment composite development

Author Community:

  • [ 1 ] [Zhang, Tao]Henan Coll Transportat, Enrollment & Employment Div, Zhengzhou 450001, Peoples R China
  • [ 2 ] [Yan, Yibo]Henan Univ Technol, Coll Civil Engn, Zhengzhou 450001, Peoples R China
  • [ 3 ] [Chen, Qi]Henan Univ Technol, Coll Civil Engn, Zhengzhou 450001, Peoples R China
  • [ 4 ] [Liu, Ze]Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China

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Source :

LAND

Year: 2022

Issue: 10

Volume: 11

ESI Discipline: GEOSCIENCES;

ESI HC Threshold:38

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 8

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

Affiliated Colleges:

Online/Total:1107/10575872
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