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

被引：0
|
作者：
Duan L. [1 ]
Liu C.-J. [1 ]
Fang Z.-L. [1 ]
Cheng Z.-W. [1 ]
机构：
[1] School of Civil Engineering, Huazhong University of Science and Technology, Wuhan
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2019年 / 19卷 / 06期
基金：
中国国家自然科学基金;
关键词：
IOA algorithm; Signal control; Systems engineering; Traffic assignment; Transportation network design problem;
D O I：
10.16097/j.cnki.1009-6744.2019.06.012
中图分类号：
学科分类号：
摘要：
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. Copyright © 2019 by Science Press.
引用
收藏
页码：77 / 84
页数：7
相关论文
共 16 条
  • [1] Gao Z.Y., Zhang H.Z., Sun H.J., Bi-level programming models, approaches and applications in urban transportation network design problem, Journal of Transportation Systems Engineering and Information Technology, 4, 1, pp. 35-44, (2004)
  • [2] Farahani R.Z., Miandoabchi E., Szeto W.Y., Et al., A review of urban transportation network design problems, European Journal of Operational Research, 229, 2, pp. 281-302, (2013)
  • [3] Wang D.Z.W., Lo H.K., Global optimum of the linearized network design problem with equilibrium flows, Transportation Research Part B: Methodological, 44, 4, pp. 482-492, (2010)
  • [4] Szeto W.Y., Jiang Y., Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach, Transportation Research Part B: Methodological, 67, pp. 235-263, (2014)
  • [5] Liu Z., Wang S., Meng Q., Optimal joint distance and time toll for cordon-based congestion pricing, Transportation Research Part B: Methodological, 69, pp. 81-97, (2014)
  • [6] Sun F., Long J.C., A continuous transportation network design problem with considering of speed limit, Journal of Transportation Systems Engineering and Information Technology, 15, 3, pp. 146-151, (2015)
  • [7] Allsop R.E., Some possibilities of using traffic control to influence trip distribution and route choice, Proceedings of the 6th International Symposium on Transportation and Traffic Theory, pp. 345-373, (1974)
  • [8] Chen X.M., Shao C.F., Guo Y.S., Studies on traffic signal optimization combined with user equilibrium assignment, Journal of Beijing Jiaotong University, 3, (2007)
  • [9] Li R.M., Tang S.M., Integrative modeling study of dynamic traffic assignment and traffic control in saturated network, Journal of System Simulation, 8, pp. 1811-1815, (2007)
  • [10] Huang W., Li L., Lo H.K., Adaptive traffic signal control with equilibrium constraints under stochastic demand, Transportation Research Part C: Emerging Technologies, 95, pp. 394-413, (2018)
12