详情

原文传递 Corridor Level Adaptive Signal Control.

题名：	Corridor Level Adaptive Signal Control.
作者：	Liu, H.; Machemehl, R.
关键词：	Signal control methodology, Traffic condition, Signal timings, Traffic signal control systems, Traffic volumes, Historical information, Construction of infrastructure, Barron-Jensen/Frankowska (B-J/F)
摘要：	With the increasing number of vehicles, most cities around the world are suffering from traffic congestion. A variety of strategies can be employed to address the congestion problem, such as traffic signal control, constructing new roads, allocating dedicated lanes for public transit, and road space rationing. Out of these solutions, traffic signal control is the most cost-effective way to mitigate congestion since its objective is to maximize the traffic mobility by allocating the green time to each signal phase in the most reasonable way without monetary cost on the construction of infrastructure. Signal control can be classified into two groups: pre-timed signal control and adaptive signal control. In the pre-timed control method, signal timings are established based on historical data and implemented to the specific time of day. Unlike the pre-timed control, adaptive signal control optimizes signal timing plans based on real-time traffic conditions. Thanks to its ability to adjust and respond to the prevailing traffic condition, adaptive control is superior to pre-timed control. Although adaptive signal control offers promise in reducing traffic congestion, it is not very popular in urban networks because of its complexity. For instance, the City of Austin manages about 1,020 traffic signals and almost all of them are pre-timed. Therefore, the adaptive signal control is still a challenging and necessary research topic. The core elements of an adaptive signal control method include a traffic volume prediction model and a signal optimization model. The main considerations of the adaptive control method are prediction accuracy and computational speed. In most published adaptive signal control methods, upstream traffic detectors are frequently used for traffic prediction. Given the short distance between the sensors and the target intersection, this method cannot provide enough information for the projection horizon. Therefore, approximations are made based on the historical information and the correlation in time. Although these models may work well under traffic conditions with low variation, model efficiency cannot be assured when traffic volumes change dramatically. Moreover, the traffic flow model used in the optimization part is usually approximated. For example, to have a deterministic expression for the control delay during the red time, some models assume a Poisson process for the arriving vehicles and constant arrival rates in each iteration, which may not be appropriate in reality. This study proposes an adaptive signal control algorithm to optimize the signal timing for the incoming cycle at an isolated intersection. A CTM-based model predicts the traffic volumes for the target intersection by counting the current vehicle numbers in the upstream cells. This model does not make assumptions about the arrival process and the correlation of the flows between consecutive cycles. Through this method, the accuracy of the volume prediction is ensured even under rapidly varying traffic conditions. In addition, the signal optimization problem is modeled as a mixed integer linear program (MILP) based on the Barron-Jensen/Frankowska (B-J/F) solution to the Lighthill-Whitham-Richards (LWR) model. The sequence and the splits of phases can be optimized at the same time according to the current traffic condition. Finally, this study compares the new method to the critical lane flow ratio method, which is a commonly used strategy. It shows that the proposed method can reduce the traffic delay under various traffic congestion degrees for both balanced and unbalanced traffic volumes. The delay reduction percentage increases with decreases of the overall critical volume-to-capacity ratio. The reduction is statistically significant when the overall critical volume-to-capacity ratio is below 0.9, and it can reach 32% when the overall critical volume-to-capacity ratio is equal to 0.347 under unbalanced traffic conditions.
报告类型：	科技报告