Recurrent and Non-recurrent Congestion Based Gridlock Detection on Chula-SSS Urban Road Network

Abstract

Traffic congestion on not only highways but also complex urban road networks has attracted the attention of many researchers. Traffic congestion growing in urban road networks is an inevitably important problem especially for populated cities during rush hours. A traffic blockage can be realized as the source of traffic congestion, which can propagate to form queues and sometimes a gridlock. Traffic blockages are triggered by complicated factors ranging from temporal and spatial situations. Recurrent congestion is a traffic congestion that occurs during morning and evening rush hours e.g. from school buses and parent vehicles to drive their children to-and-from schools. In addition, unforeseen, unexpected events that can cause as non-recurrent traffic congestion e.g. car breakdowns, accidents, road maintenance, and severe weather conditions, which can disorder normal traffic flows and reduce road capacity. Traffic blockage may spread its negative impacts to neighbouring upstream and downstream links. And that can lead to the formation of congestion gridlock, which further reduce traffic flow efficiency in a complex urban road network. These problems are vital but often tough to resolve in urban road networks. In this paper, the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset has been used with Simulation of Urban Mobility (SUMO) to simulate recurrent and non-recurrent congestion cases. The detection is based on the information from simulated lane area detectors. For non-recurrent case, lanes are closed to simulate the gridlock occurrences. With the morning case of calibrated Chula-SSS dataset, both recurrent and nonrecurrent congestion based gridlock have been studied with upstream and downstream nearby detectors and preliminary results are herein reported upon the gridlock status as detected by using different combinations of traffic jam length and mean speed conditions at both the upstream and downstream detectors of every intersection within the critical looped road segments.

Application of Traffic Light Control in Oversaturated Urban Network Using Multi-Agent Deep Reinforcement Learning

“…3) the HIGH traffic demand with 25000 vehicles for the simulation network from 3 PM to 7 PM. In earlier studies [24], [27], we have found that bottleneck congestion starts in traffic jams and spreads to upstream and downstream vicinities [28]. Such a circumstance frequently results in gridlock in a complex urban road network, reducing traffic efficiency to a near-halting state.…”

“…The state is a numerical representation of each intersection agent's observations of the road network environment. If each interconnected intersection in a road network is filled with slow-moving vehicles, then vehicles approaching from opposing directions cannot proceed while getting green traffic light [24]. The region surrounding local intersections has a significant impact on traffic regulation.…”

“…Such a circumstance frequently results in gridlock in a complex urban road network, reducing traffic efficiency to a near-halting state. As a result, previous works [24], [27] have proposed gridlock detection and prediction based on recurrent and non-recurrent congestion using the characteristics of gridlock, which can be assessed by the length of traffic jams and the speed of upstream and downstream links at intersections. According to empirical observations in the current area, as indicated in Fig.…”