城市主干路交叉口直行待行區(qū)相位差優(yōu)化研究
[Abstract]:Intersections delay is the main part of urban trunk road delay, and the research of intersection is one of the research hotspots in traffic field. The straight line waiting area is an effective measure to reduce the intersection delay. The phase difference optimization can minimize the delay of the whole trunk road as far as possible. The previous phase difference optimization model did not consider the situation of the intersection with the straight line waiting area, so the delay of the straight line waiting area of the main road intersection was not minimized as far as possible. In view of the above problems, this paper analyzes the intersection delay from the two aspects of vehicle conservation, based on phase difference, under the condition of setting up the straight line waiting area at the intersection. The delay at the starting and end points of the trunk road only considers the delay caused by the vehicle leaving the main road, and the delay at the other intersections considers the delay of the upper and lower directions. On the basis of the traffic wave theory, the continuous control is taken as an example. From the point of view of queue length, the range of phase difference is analyzed, and finally, combining with the delay and phase difference range of general intersection, the phase difference optimization model of some intersections with straight line waiting area is established. An improved genetic algorithm is adopted to solve the optimal phase difference. At the same time, an improved selection strategy is proposed to solve the problem that the selection operation is easy to fall into the local optimum, and the fixed probability of the crossover and mutation operations is not conducive to the solution of the optimal result. In the phase of crossover and mutation, adaptive crossover probability and mutation probability are adopted, and an operator correction method is designed to solve the problem that the phase difference has constraints and the penalty function method increases the computational complexity. Finally, the improved genetic algorithm and genetic algorithm are used to solve the optimal phase difference under the minimum delay on the matlab platform respectively. The simulation results show that the improved genetic algorithm converges faster than the genetic algorithm. Two traffic examples combined with the optimal phase difference are simulated on the vissim platform. The model is proved to be correct by comparing the delay before and after the phase difference optimization at each intersection. The validity of the model is proved by comparison of intersections delay affected by the straight waiting area in reference [13].
【學(xué)位授予單位】:山東科技大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2017
【分類(lèi)號(hào)】:U491
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