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

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.

A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas

“…Clustering Approach [1] traffic load analysis improved k-means clustering algorithm [2] traffic congestion analysis self-organizing maps neural network [3] traffic state classification k-medoids algorithm [4] road network level identification k-means algorithm [5] traffic congestion analysis grey relational clustering model [6] traffic accidents and pattern extraction ROCK algorithm [7] traffic accident pattern identification COOLCAT algorithm [8] traffic accident factor analysis k-means algorithm [9] road traffic accident modeling a comparative study of machine learning classifiers [10] traffic accident black spots identification HDBSCAN algorithm [11] traffic congestion analysis k-means algorithm [12] driving behavior risk analysis k-means algorithm [13] optimal path routing a modified K-medoids algorithm [14] analysis of pedestrian crash fatalities and severe injuries KDE method [15] traffic-management system DBSCAN agorithm [16] severity of traffic accident analysis DBSCAN algorithm [17] highway safety assessment k-means algorithm [18] pedestrian crash severity analysis KDE method [19] detection of road segments of spatially prolonged and high traffic accident risk a clustering algorithm based on the Gestalt principle of proximity…”

Evaluating Traffic Performance on Basra City Urban Roads Network

“…They reported that analysis and estimation of traffic flow in urban areas is required to understand the causes of traffic congestion. Zang et al [10] reported that for alleviating traffic congestion, scientific and quantitative evaluation of the traffic congestion should be carried out. Alkaissi and Hussain [11] estimated traffic volumes, vehicle compositions and travel time for some selected interrupted streets in Baghdad City to understand the causes of traffic congestion.…”

Travel Characteristics of Urban Residents Based on Taxi Trajectories in China: Beijing, Shanghai, Shenzhen, and Wuhan

“…He used speed performance indicators to evaluate the congestion of the existing road network and then introduced road segment and road network congestion indicators to measure the congestion of urban road segments and road networks, respectively [22]. Zang proposed a time-varying TPI-based self-organizing mapping (SOM) algorithm for clustering traffic congestion into more detailed and accurate patterns, which is applicable to the clustering of TPIs in different years, and this method helps to make decisions based on the different congestion patterns to make decisions for traffic management [23]. He and Zang analyzed possible congested areas based on urban morphology but did not analyze them in conjunction with cab trajectories.…”