Abstract
This paper aims to investigate the impact of occasional traffic crashes on the urban traffic network flow. Toward this purpose, an extended model of coupled Nagel–Schreckenberg (NaSch) and Biham–Middleton–Levine (BML) models is presented. This extended model not only improves the initial conditions of the coupled models, but also gives the definition of traffic crashes and their spatial/time distribution. Further, we simulated the impact of the number of traffic crashes, their time distribution, and their spatial distribution on urban network traffic flow. This research contributes to the comprehensive understanding of the operational state of urban network traffic flow after traffic crashes, towards mastering the causes and propagation rules of traffic congestion. This work also a theoretical guidance value for the optimization of urban traffic network flow and the prevention and release of traffic crashes.
Highlights
A fluent urban traffic network is important for a city’s normal operation
Traffic crashes can lead to traffic congestion, reduced road capacity, and increased travel time, fuel consumption, and environmental pollution
Urban traffic management is concerned with ways to reduce the impact of traffic crashes on urban road networks and ensure the efficient operation of traffic
Summary
A fluent urban traffic network is important for a city’s normal operation. with the acceleration of urbanization and the rapid development of urban road traffic, traffic crashes have increased significantly. Scholars made many improvements to the BML model in order to study urban road traffic crashes by considering many factors, such as average vehicle speed, traffic signals, and so on [16,17]. The above-mentioned articles have carried out in-depth analyses of the characteristics, predictions, influences, and evaluations of traffic crashes from different aspects, and obtained some good results These studies are mainly aimed at highway traffic flow, while urban road networks are mainly weaves of a large number of roads in different directions, there are many intersections and signal lights, and the traffic organization is more complicated. We found that in the BML model, the vehicle clusters from the lower-left corner to the upper-right corner were fSourstmaineadbilbityy 2t0h1e9,v1e1h, xicFlOesR tPrEaEvRelRiEnVgIEnWorthward and the vehicles traveling eastward It c5anof b1e5 seen that the direction of the traffic flow determines the final global cluster configuration. 2.5 2.5 p =0p.1=0.1p =0p.2=0.2 p =0p.3=0.3p =0p.4=0.4 p =0p.5=0.5 p=0.p6=0.6p =0p.7=0.7 p =0p.8=0.8p =0p.9=0.9
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