Congestion Propagation Based Bottleneck Identification in Urban Road Networks

Abstract

Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method.

Learning Traffic Network Embeddings for Predicting Congestion Propagation

“…where σ(•) is the Sigmiod function, EP (r i , r i ) = e src i • (e trg i ) T . For a path pa =< r i → ... → r i >, the probability that the propagation occurs along the path pa is calculated as (21) where edge < r a , r b >∈ pa indicates that r a and r b are consecutive road segments in path pa.…”

“…The work in [20] proposed a spatiotemporal co-location congestion pattern mining method to discover an orderly set of roads with congestion propagation in urban traffic, which reveals the process of congestion propagation and uncovers the main propagation paths that lead to large-scale congestion. The work in [21] identified the locations of traffic bottlenecks by mining congestion propagations. It built congestion prop-agation graphs to model congestion propagations, constructed maximal spanning trees to model the causal relationship, and utilized the Markov analysis to determine the probabilities of congestion propagations, based on which bottlenecks are identified.…”

Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study

“…Some other works [46,47,42,48,49,50,43] analyze the network structure using complexity networks theory to evaluate, design and optimize traffic networks with sustainability and maintainability. Detecting network bottlenecks with poor connectivity and high congestion vulnerability is studied in [51,52,53,54,55,56,57]. It is noteworthy that some recent works [58,59,60] address mixed traffic taking autonomous vehicles (AVs) into consideration in their analysis.…”

Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices

“…Then, the bottleneck is identified by comparing the changes in the index in the spatial and temporal domain. In [ 13 ], authors use a speed threshold to detect congestion that is defined as of the average vehicle speeds on the observed road segment, where n varies between 10 and 90.…”