The Higher Order Macroscopic Traffic Flow Models in Reproducing Stop-And-Go Phenomenon: Systematic Review - Loh, Sik Chin; Siti Suhana Jamaian (Enhanced Knowledge in Sciences and Technology, 2022)
In large scale cities, traffic congestion is a common problem faced by drivers on the road especially during rush hour. Macroscopic traffic models are crucial in understanding the traffic phenomenon from an overall view. However, the first order Lighthill-Whitham-Richards (LWR) models unable to capture a variety of real-world traffic patterns. This research aims to review the higher-order LWR models in reproducing stop-and-go phenomenon and assess their performance. A systematic review of the higher-order macroscopic traffic flow models was conducted in order to assess the models’ performance and capability in reproducing stop-and-go phenomenon with the aid of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. There was a total of five papers selected for this review that fulfilled the inclusion criteria. Based on the findings of each paper, we concluded that the stop-and-go phenomenon was successfully reproduced by the improved macro models by considering different factors including road capacity, anticipation optimal velocity, existence of ramps, moving and static bottlenecks. The study of macroscopic traffic model on other complex phenomena such as hysteresis and capacity drop also can be investigated in systematic way.