Journal
SUSTAINABILITY
Volume 15, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/su15129328
Keywords
human mobility; congestion source analysis; land use; cell-phone data; machine learning
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This study investigates the relationship between land-use characteristics and congestion pattern features in the Second Ring Road of Xi'an, China. Using cell-phone data, POI data, and land-use data, the study identifies congested road sections and traces them back to source parcels. The results show that residential land and 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 highlights the role of specific parcels in network congestion.
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.
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