Freeway capacity decreases at sags due to local changes in car-following behavior. Consequently, sags are often bottlenecks in freeway networks. This article presents a microscopic traffic model that reproduces traffic flow dynamics at sags. The traffic model includes a new car-following model that takes into account the influence of freeway gradient on vehicle acceleration. The face-validity of the traffic model is tested by means of a simulation study. The study site is a sag of a Japanese freeway. The simulation results are compared to empirical traffic data presented in previous studies. We show that the model is capable of reproducing the key traffic phenomena that cause the formation of congestion at sags, including the lower capacity compared to normal sections, the location of the bottleneck around the end of the vertical curve, and the capacity drop induced by congestion. Furthermore, a sensitivity analysis indicates that the traffic model is robust enough to reproduce those phenomena even if some inputs are modified to some extent. The sensitivity analysis also shows what parameters need to be calibrated more accurately for real world applications of the model.
Stop-and-go waves are spatially-confined regions of low traffic speed that propagate upstream at a constant velocity. The occurrence of stop-and-go waves on freeways has negative impacts on both travel time and traffic safety. Sags are freeway sections along which gradient changes significantly from downwards to upwards. Stop-and-go waves often emerge on the uphill section of sags, both in uncongested and congested traffic conditions. According to previous studies, the formation of stop-and-go waves at sags can be caused by local changes in car-following behaviour as well as disruptive lane changes. However, it is not clear which of those two causes is more frequent. The aim of this paper is to identify the primary factor triggering stop-and-go waves at sags. To this end, we analyse vehicle trajectories collected by means of video cameras on a three-lane sag of the Tomei Expressway (Japan), identifying the causes of formation and growth of stopand-go waves on the study site. The results show that the primary factor triggering stop-and-go waves is related to car-following behaviour. This finding shows the relevance of developing systems to assist drivers in performing the acceleration task at sags.
Sags are freeway sections along which the gradient changes significantly from downwards to upwards. The capacity of sags is considerably lower than the capacity of normal sections. Consequently, sags are often freeway bottlenecks. Recently, several control measures have been proposed to improve traffic flow efficiency at sags. Those measures generally aim to increase the capacity of the bottleneck and/or to prevent traffic flow perturbations in nearly-saturated conditions. This paper presents an alternative type of measure based on the concept of mainstream traffic flow control. The proposed control measure regulates the traffic density at the bottleneck area in order to keep it below the critical density, hence preventing traffic from breaking down while maximizing outflow. Density is regulated by means of a variable speed limit section that regulates the inflow to the bottleneck. Speed limits are selected based on a feedback control law. We evaluate the effectiveness of the proposed control strategy by means of a simple case study using microscopic traffic simulation. The results show a significant increase in bottleneck outflow, particularly during periods of high demand, which leads to a considerable decrease in total delay. This finding suggests that mainstream traffic flow control strategies using variable speed limits have the potential to substantially improve the performance of freeway networks containing sags.
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