Wang, D., Chen, H. orcid.org/0000-0002-1339-9669, Li, C. et al. (1 more author) (2023) Exploring the Relationship between Land Use and Congestion Source in Xi’an: A Multisource Data Analysis Approach. Sustainability, 15 (12). 9328. ISSN 2071-1050
Abstract
Traffic congestion is a critical problem in urban areas, and understanding the relationship between land use and congestion source is crucial for traffic management and urban planning. This study investigates the relationship between land-use characteristics and congestion pattern features of source parcels in the Second Ring Road of Xi’an, China. The study combines cell-phone data, POI data, and land-use data for the empirical analysis, and uses a spatial clustering approach to identify congested road sections and trace them back to source parcels. The correlations between building factors and congestion patterns are explored using the XGBoost algorithm. The results reveal that residential land and residential population density have the strongest impact on congestion clusters, followed by lands used for science and education and the density of the working population. The study also shows that a small number of specific parcels are responsible for the majority of network congestion. These findings have important implications for urban planners and transportation managers in developing targeted strategies to alleviate traffic congestion during peak periods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). |
Keywords: | human mobility; congestion source analysis; land use; cell-phone data; machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Aug 2023 14:26 |
Last Modified: | 10 Aug 2023 14:26 |
Published Version: | https://www.mdpi.com/2071-1050/15/12 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/su15129328 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202215 |