Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Abstract

To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented.

Analysis of Traffic Evolution on Road Networks of a Roundabout

“…Note that in Figure 3, Q is the point of segment Ω ∩ {(γˆ1,γˆ2): γ 1 + γ 2 = γˆ3} closest to line in equation (16). We compute fluxes at the junction based on the following cases:…”

“…Thus, congestion leads to delays in the movement of both goods and passengers [14,15]. As a result, it affects the devel-opment of a national economy [16]. Real-time and precise prediction models are capable of analyzing traffic flow trends and characteristics on urban road network [17,18].…”

Road traffic analysis on the congestion problem using the Ford-Fulkerson algorithm

“…Recurrent congestion is known as peak hour traffic congestion that usually occurs in an area while nonrecurrent congestion is caused by a number of unexpected events that slow down the traffic flow (Ma et al, 2020). Non-recurrent congestion is caused by accidents, road maintenance, road closures for events and natural disasters (Peres et al, 2018; Karaer et al, 2020).…”

Research on Traffic Situation Analysis for Urban Road Network Through Spatiotemporal Data Mining: A Case Study of Xi’an, China

“…Unlike the randomness of occasional congestion, recurrent congestion has specific rules in forming, spreading, and dissipating. On different days, the generation and dissipation time of congestion is always the same in the temporal dimension, while the location, propagation direction, and influence range of congestion are highly similar in spatial dimension [11]. This study focused on the spatiotemporal patterns of recurrent congestion on working days.…”