Identifying Traffic Congestion Patterns of Urban Road Network Based on Traffic Performance Index

Jinrui Zang, Pengpeng Jiao (), Sining Liu, Xi Zhang, Guohua Song and Lei Yu
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Jinrui Zang: Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Pengpeng Jiao: Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Sining Liu: Beijingxi Railway Station, China Railway Being Group Co., Ltd., Beijing 100844, China
Xi Zhang: Beijing Key Laboratory of Urban Traffic Operation Simulation and Decision Support, Beijing Transport Institute, Beijing 100073, China
Guohua Song: Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
Lei Yu: College of Science and Technology, Texas Southern University, Houston, TX 77004, USA

Sustainability, 2023, vol. 15, issue 2, 1-22

Abstract: Urban congestion has become a global problem with urbanization and motorization. The analysis of time-varying traffic congestion patterns is necessary to formulate effective management strategies. The existing studies have focused on traffic flow patterns developed by the volume, speed and density of road sections in a limited district, while the long-time analysis of congestion patterns of the macro road network at the city level is inadequate. This paper aims to recognize traffic congestion patterns of the urban road network based on the traffic performance index (TPI) of 699 days in 2018, 2019 and 2021 in Beijing. The self-organizing maps (SOM) method improved by an automatic clustering number determination algorithm is proposed to cluster congestion patterns based on time-varying TPI. The traffic congestion of the macro road network is clustered into Mondays, Fridays, ordinary weekdays, congested weekdays, weekdays of winter and summer vacation, Saturdays, Sundays and festivals patterns. Patterns of Mondays and congested weekdays have a prominent morning peak, while patterns of Fridays, ordinary weekdays, and weekdays of winter and summer vacation have a prominent evening peak. Saturdays, Sundays and festivals are less congested than weekday patterns. It is verified that the SOM method proposed in this paper clusters traffic congestion into more detailed and accurate patterns, and it is applicable to TPI clustering in different years. The degree of congestion in 2021 increases by 7.15% in peak hours and decreases by 7.50% in off-peak hours compared with that in 2019 due to COVID-19. This method is helpful for traffic management in terms of making decisions according to different congestion patterns.

Keywords: congestion patterns; traffic performance index; clustering methods; traffic conditions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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