信号控制与交通分配协同模型的自适应IOA算法

现有求解信号控制与交通分配协同问题的IOA(Iterative Optimization and Assignment)算法,是通过对两个子问题分别求解并迭代至收敛,其收敛速度快,但解的质量仍有待改善. 对IOA 算法改进,提出自适应IOA(Adaptive Iterative Optimization and Assignment, AIOA)算法,提升解质量的同时保持计算速度快的优点. 首先,把迭代过程中路径流量的差分值作为自适应修正项加入信号控制模型的输入参数中,增大解的变异程度,既可加快收敛速度,又可突破IOA寻优范围的局限性;其次,根据目标函数的变化趋势自适应地转入采用黄金分割法的局部搜索,避免解的劣化. 仿真结果表明：AIOA算法将IOA算法与全局最优解的差距平均缩小50.8%,时间成本降低10％,仅为遗传算法的1％;AIOA算法能在短时间内求得满意解,且适用于大规模路网.

Adaptive IOA Algorithm for Coordinated Model of Signal Control and Traffic Assignment

To solve the coordinated model of signal control and traffic assignment, the IOA (Iterative Optimization and Assignment) algorithm solves the sub- problem separately, and iterates to convergence. It converges quickly, but the solution quality needs to be improved. This paper proposes an adaptive IOA (Adaptive Iterative Optimization and Assignment, AIOA) algorithm to improve the solution quality while maintaining the calculation speed. Firstly, the difference value of link flow in the iterative process is added as an adaptive correction term to the input parameters of the signal control model, which increases the variation of the solution. It can not only accelerate the convergence speed but break through the limitation of the IOA search range. Secondly, the local search strategy of the golden section method is adaptively used according to the trend of the objective function to avoid the solution become bad. Simulation results show that the AIOA algorithm reduces the gap between the IOA algorithm and the global optimal solution by 50.8%, while the time cost is 10% lower, and only 1% of the genetic algorithm. The AIOA algorithm can obtain a satisfactory solution in a short time, and can be used in large road networks.