Multiperiod Traffic Congestion Estimation Model Based on an Urban Spatial Structure

Bibliographic Data

ID22415973
AuthorsShuguang Li (0000-0003-0680-8767, Chang’an Univ.), Wenxin Wang (0000-0002-7274-1970, Chang’an Univ.), Ming Bai (0000-0001-9197-5900, Shaan’xi College of Communications Technology)
Year2026
Volume152
Issue1
Publication date2026-03-01
Open AccessNo
TypeARTICLE
VenueJournal of Urban Planning and Development (JOURNAL)
Journal identifiersISSN: 0733-9488 • E-ISSN: 1943-5444
PublisherAmerican Society of Civil Engineers (ASCE) (PUBLISHER • US)
DOI10.1061/jupddm.upeng-5312
OpenAlexW4416609752
LanguageEN
References cited14

With the rapid development of new urbanization, traffic congestion in Chinese cities has worsened, a problem not fully addressed by current models due to its complex link to urban spatial structure. This study proposes a Multiple-period Traffic Congestion Estimation Model using a random forest regression approach to predict congestion across three daily periods. The model achieves an average accuracy of over 85%, demonstrating significant predictive capability. By analyzing urban spatial factors such as road network structure and functional distribution, this study enhances our understanding of the interactions between urban spatial structures and traffic patterns. It offers practical value for optimizing urban traffic management and planning, while also promising environmental benefits through reduced congestion and emissions.

Estimation · Regression analysis · Traffic congestion · Traffic congestion reconstruction with Kerner's three-phase theory · Traffic speed · Urban spatial structure · Human Mobility and Location-Based Analysis · Traffic control and management · Traffic Prediction and Management Techniques

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