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.

Real-time path planning to prevent traffic jam through an intelligent transportation system

Abstract: Congestion is a major problem in large cities. One of the main causes of congestion is the sudden increase of vehicle traffic during peak hours. Current solutions are based on perceiving road traffic conditions and re-routing vehicles to avoid the congested area. However, they do not consider the impact of these changes on near future traffic patterns. Hence, these approaches are unable to provide a long-term solution to the congestion problem, since when suggesting alternative routes they create new bottlenecks at roads closer to the congested one, thus just transferring the problem from one point to another. With this issue in mind, we propose an intelligent traffic system called CHIMERA, which improves the overall spatial utilization of a road network and also reduces the average vehicle travel costs by avoiding vehicles from getting stuck in traffic. Simulation results show that our proposal is more efficient in forecasting congestion and is able to re-route vehicles appropriately, performing a proper load balance of vehicular traffic.

Method of presuming traffic conditions by using floating car data and system for presuming and presenting traffic conditions by using floating data

Abstract: A method of presuming traffic conditions for implementing a forecast and a presumption of traffic jam situation in an area where probe cars are not traveling currently, in which the probe cars send floating car data that is times and positions of traveled areas to center facilities, and the center accumulates the floating car data in a floating car data database by traffic conditions presumption means and also presumes forecast traffic jam information in the forward areas of the probe cars and presumed traffic jam information in the backward areas thereof by using the current floating car data and the floating car data database accumulated from the past to the present.

Mixed manual/semi-automated traffic: a macroscopic analysis

Abstract: The use of advanced technologies and intelligence in vehicles and infrastructure could make the current highway transportation system much more efficient. Semi-automated vehicles with the capability of automatically following a vehicle in front as long as it is in the same lane and in the vicinity of the forward looking ranging sensor are expected to be deployed in the near future. Their penetration into the current manual traffic will give rise to mixed manual/semi-automated traffic. In this paper, we analyze the fundamental flow–density curve for mixed traffic using flow–density curves for 100% manual and 100% semi-automated traffic. Assuming that semi-automated vehicles use a time headway smaller than today’s manual traffic average due to the use of sensors and actuators, we have shown using the flow–density diagram that the traffic flow rate will increase in mixed traffic. We have also shown that the flow–density curve for mixed traffic is restricted between the flow–density curves for 100% manual and 100% semi-automated traffic. We have presented in a graphical way that the presence of semi-automated vehicles in mixed traffic propagates a shock wave faster than in manual traffic. We have demonstrated that the presence of semi-automated vehicles does not change the total travel time of vehicles in mixed traffic. Though we observed that with 50% semi-automated vehicles a vehicle travels 10.6% more distance than a vehicle in manual traffic for the same time horizon and starting at approximately the same position, this increase is marginal and is within the modeling error. Lastly, we have shown that when shock waves on the highway produce stop-and-go traffic, the average delay experienced by vehicles at standstill is lower in mixed traffic than in manual traffic, while the average number of vehicles at standstill remains unchanged.

Improving Traffic Flow Efficiency by In-Car Advice on Lane, Speed, and Headway

Abstract: This paper presents a new in-car advisory system that gives advices on lane, speed, and headway. The advices are determined at a traffic management center based on a new lane level traffic state prediction model, in order to prevent or solve suboptimal traffic flow conditions. The system aims for an optimal lane distribution in high flow conditions, decreasing the chance of spillback by advising drivers away from the right lane, and a reduction in the capacity drop by advising drivers to maintain a short (but safe) headway at the end of congestion. The system is implemented in microscopic simulation to evaluate the potential benefits for different penetration and compliance rates. Benefits at both low and high rates are found as only a small redistribution of traffic over the lanes may be required to stabilize flow. The capacity drop is mainly reduced at high rates as it is required that many vehicles accelerate more. The maximum benefit found is a reduction of 49% in travel time delay. Effects are smaller at lower rates. Negative side effects are also found, including oversaturation of lanes partially by advised lane changes and increased probability of spillback taking effect.