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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 36/2023

29.04.2023 | S.I.: Evolutionary Computation based Methods and Applications for Data Processing

Comparative analysis of urban underground public space and user walking paths based on the social network model

verfasst von: Xinming Jia, Bo Yan, Ling Fang, Jinyao Wang, Simai Pang, Yang Liu, Minyao Xu

Erschienen in: Neural Computing and Applications | Ausgabe 36/2023

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Abstract

The operation status of the underground public space pedestrian system is of varying quality, but decision-makers and operators have no way of knowing its current operation status and how to retrofit it. In this paper, the social network model is adopted to compare and analyze the characteristic parameters of urban underground public space networks and users’ pedestrian routes in 13 cases in the main urban area of Chongqing to judge the suitability of the two networks. The results show a mismatch between the existing urban underground public spaces and users’ walking paths, with most users choosing to move within a fixed range and preferring to stop at nodes with larger areas, while there is an obvious waste of resources in the rest of the nodes. The overall connectivity and aggregation are generally low although the number of nodes in transit between the two networks matches. In addition, with different types of space compared, the cross-block urban underground public space network is more suitable for users’ pedestrian routes, followed by the square and street types, and the compound type is ranked last.
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Metadaten
Titel
Comparative analysis of urban underground public space and user walking paths based on the social network model
verfasst von
Xinming Jia
Bo Yan
Ling Fang
Jinyao Wang
Simai Pang
Yang Liu
Minyao Xu
Publikationsdatum
29.04.2023
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 36/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-08589-8

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