Topic
Traffic wave
About: Traffic wave is a research topic. Over the lifetime, 2106 publications have been published within this topic receiving 62117 citations. The topic is also known as: phantom traffic jam & ghost jams.
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TL;DR: The results show that by increasing the percentage of heavy vehicles, there will be more severe traffic congestion around on-ramp system, lower saturated flow and capacity and the main road will have better performance.
Abstract: Although the on-ramp system has been widely studied, the influence of heavy vehicles is unknown because researchers only investigate the traffic dynamics around on-ramp system under homogeneous traffic conditions, which is different in real-world settings. This paper uses an improved cellular automaton model to study the heterogeneous traffic around on-ramp system. The forward motion rules are improved by considering the differences of driving behavior in different vehicle combinations. The lane change rules are improved by reflecting the aggressive behavior in mandatory lane changes. The phase diagram, traffic flow, capacity and spatial-temporal diagram are analyzed under the influences of heavy vehicles. The results show that by increasing the percentage of heavy vehicles, there will be more severe traffic congestion around on-ramp system, lower saturated flow and capacity. Also, the interactions between main road and on-ramp have been investigated. Increasing the percentage of heavy vehicles at the ups...
9 citations
TL;DR: This paper proposes a traffic jam awareness and observation system using mobile phones that can tell a driver how many vehicles ahead are trapped in traffic jam and how much time the driver would probably wait, and it can provide real-time video streams from the head vehicles of the traffic queue so that the driver can see what causes the traffic jams and the progress of handling the traffic jam.
Abstract: Traffic jam is a very common and very annoying thing in urban traffic. The most annoying part in traffic jams is not that you have to wait for a long time but that you do not even know how long you have to wait and what causes the traffic jam. However, the pain of being trapped in traffic jams seems to be neglected by existing research works; they put their focuses on either mathematical modeling or optimal routing for those not trapped in traffic jams. In this paper, we propose a traffic jam awareness and observation system using mobile phones. It can tell a driver how many vehicles ahead are trapped in traffic jam and how much time the driver would probably wait. Moreover, it can provide real-time video streams from the head vehicles of the traffic queue so that the driver can see what causes the traffic jam and the progress of handling the traffic jam. The system is environment independen; it can even work when the traffic jam happens in a tunnel. Experiments show that our system can find the head vehicles of the traffic queue and give the queue length accurately, and the video streams coming from the head vehicles reflect the actual situation of the traffic jam basically.
9 citations
01 Aug 2006
TL;DR: Zhang et al. as discussed by the authors developed geometric models to calculate sight distance for unprotected left-turning vehicles for different geometric configurations, which can be used by traffic engineers to lay out intersection configurations or renew the existing leftturn lane design to provide sufficient sight distance.
Abstract: During the permitted left-turn green phase at intersections on divided highways, if the median of the major road is relatively wide, the simultaneously-turning vehicles in the opposite left-turn lane frequently block the driver’s view. This situation may contribute not only to a serious safety problem because of drivers’ misjudging gaps among the opposing through traffic, but may also lead to needless delays for the left-turning traffic since those cautious drivers reject physically adequate gaps. To improve intersection sight distances and traffic operation for unprotected left-turn drivers at signalized intersections, this study aimed at developing geometric models to calculate sight distance for unprotected left-turning vehicles for different geometric configurations. These models can be used by traffic engineers to lay out intersection configurations or renew the existing left-turn lane design to provide sufficient sight distance and enhance efficiency of the intersection traffic operation. Furthermore, to provide a better understanding of the relationship between highway visibility and traffic operation, a field study was conducted to investigate the influence of the sight-distance problem on left-turn gap-acceptance behaviors and traffic capacity. From field observations and data analysis, this study confirmed the serious negative effect of the sight-distance problem on both traffic safety and operation efficiency for unprotected left-turn traffic.
9 citations
TL;DR: The model is applied to speed distribution of vehicles in the same LANE TRAFFIC DENSITY, speed of oncoming vehicles, and MINIMUM SIGHT DISTANCes for overTAKING.
9 citations