Regional traffic coordinating and optimizing control system and method based on vehicle average delay

Abstract

The invention relates to a regional traffic coordinating and optimizing control system and method based on vehicle average delay. The regional traffic coordinating and optimizing control system comprises intersection nodes and a regional coordinating control node. The intersection nodes are used for collecting local traffic flow status information and outputting a timing plan of an intersection; and the regional coordinating control node is used for collecting regional road network traffic flow statuses, coordinating control over all intersections and outputting timing plans of all the intersections. The regional coordinating control node comprises a traffic information collecting module, a coordinating and optimizing module and a timing plan outputting module. The traffic information collecting module is used for collecting the traffic flow statuses of road networks; the coordinating and optimizing module is used for coordinating and optimizing the signal timing plans of all the intersections through a multi-intersection coordinating control device; and the timing plan outputting module is used for outputting the timing plans coordinated by the coordinating and optimizing module to all intersection nodes to be carried out. According to the regional traffic coordinating and optimizing control system and method based on vehicle average delay, dynamic characteristics of the regional road network traffic flow are analyzed, with the purpose of minimum vehicle delay, a regional vehicle delay model and the coordinating control method are established, according to the high-dimensional feature of the delay model and the system real-time requirements, a chaos genetic algorithm is adopted to coordinate, optimize and control the regional traffic signals, and the traffic efficiencyof the regional road networks can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation, and in particular relates to a regional traffic coordination optimization control system and method based on vehicle average delay. Background technique [0002] With the rapid economic development and the acceleration of urbanization, the trend of traffic motorization is becoming more and more obvious, the road traffic volume is increasing rapidly, the road network density is increasing, and the urban traffic congestion is becoming more and more serious. The requirements of cities for traffic signal control are becoming higher and higher. The regional traffic signal coordination control method with the region or the whole city as the research object has attracted more and more attention from researchers. efficient way. In the existing regional signal coordination control technology, some researchers use the particle swarm algorithm to optimize the phase sequence on the basis ...

AI Technical Summary

Problems solved by technology

In the existing regional signal coordination control technology, some researchers use the particle swarm algorithm to optimize the phase sequence on the basis of the vehicle delay model Webster, which reduces the average delay time of vehicles to a certain extent, but the delay model is only suitable for In the low-saturation traffic state, when the saturation is close to 1, the delay time obtained by the calculation formula tends to infinity, which is contrary to the actual situation; some researchers adopt the transition delay model and propose a regional traffic signal with balanced traffic flow distribution Timing optimization method, and use co-evolutionary algorithm to obtain the optimal solution of the model, but this algorithm does not consider the phase difference factors between intersections, making it difficult to achieve overall coordinated control of the area; some researchers have integrated the delay The advantages of the model and the bandwidth model algorithm, using the genetic algorithm to optimize the parameters such as the green signal ratio and phase difference, have obtained a certain control effect, but the optimization of the relevant parameters such as the green signal ratio and phase difference is not carried out at the same time, and the genetic algorithm The algorithm itself has the disadvantages of premature convergence and slow convergence speed, resulting in inaccurate calculation results

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