信号控制与交通分配协同模型的自适应IOA 算法

交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (6): 77-84.

• 智能交通系统与信息技术 • 上一篇下一篇

信号控制与交通分配协同模型的自适应IOA 算法

段力^*，刘聪健，方炽霖，程紫微

华中科技大学土木工程与力学学院，武汉 430074

收稿日期:2019-07-05 修回日期:2019-08-22 出版日期:2019-12-25 发布日期:2019-12-25
作者简介:段力(1978-)，男，湖北武汉人，讲师，博士.
基金资助:
国家自然科学基金/National Natural Science Foundation of China (71571076，71171087)；国家社会科学基金重大项目/The Major Program of National Social Science Foundation of China(13-ZD175).

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

DUAN Li, LIU Cong-jian, FANG Zhi-lin, CHNEG Zi-wei

School of Civil Engineering, Huazhong University of Science and Technology,Wuhan 430074, China

Received:2019-07-05 Revised:2019-08-22 Online:2019-12-25 Published:2019-12-25

摘要/Abstract

摘要：

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.

关键词: 系统工程, 信号控制, 交通分配, 交通网络设计, IOA算法

Abstract:

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.

Key words: systems engineering, signal control, traffic assignment, transportation network design problem, IOA algorithm

中图分类号:

U491.1

段力，刘聪健，方炽霖，程紫微. 信号控制与交通分配协同模型的自适应IOA 算法[J]. 交通运输系统工程与信息, 2019, 19(6): 77-84.

DUAN Li, LIU Cong-jian, FANG Zhi-lin, CHNEG Zi-wei. Adaptive IOA Algorithm for Coordinated Model of Signal Control and Traffic Assignment[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(6): 77-84.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract19943.shtml

http://www.tseit.org.cn/CN/Y2019/V19/I6/77

参考文献

[1] 高自友, 张好智, 孙会君. 城市交通网络设计问题中双层规划模型、方法及应用[J]. 交通运输系统工程与信息, 2004, 4(1): 35-44. [GAO Z Y, ZHANG H Z, SUN H J. Bi-level programming models, approaches and applications in urban transportation network design problem[J]. Journal of Transportation Systems Engineering and Information Technology, 2004, 4(1): 35-44]

[2] FARAHANI R Z, MIANDOABCHI E, SZETO W Y, et al. A review of urban transportation network design problems[J]. European Journal of Operational Research, 2013, 229(2): 281-302.

[3] WANG D Z W, LO H K. Global optimum of the linearized network design problem with equilibrium flows[J]. Transportation Research Part B: Methodological, 2010, 44(4): 482-492.

[4] SZETO W Y, JIANG Y. Transit route and frequency design: Bi- level modeling and hybrid artificial bee colony algorithm approach[J]. Transportation Research Part B: Methodological, 2014(67): 235-263.

[5] LIU Z, WANG S, MENG Q. Optimal joint distance and time toll for cordon-based congestion pricing[J]. Transportation Research Part B: Methodological, 2014 (69): 81-97.

[6] 孙飞, 龙建成. 考虑速度限制的连续交通网络设计问题[J]. 交通运输系统工程与信息, 2015, 15(3): 146- 151. [SUN F, LONG J C. A continuous transportation network design problem with considering of speed limit [J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(3): 146-151.]

[7] ALLSOP R E. Some possibilities of using traffic control to influence trip distribution and route choice[C]. Proceedings of the 6th International Symposium on Transportation and Traffic Theory, Sydney, Australia, 1974: 345-373.

[8] 陈晓明, 邵春福, 郭钰愫. 用户平衡分配条件下交通控制优化研究[J]. 北京交通大学学报, 2007(3): 18-21, 26. [CHEN X M, SHAO C F, GUO Y S. Studies on traffic signal optimization combined with user equilibrium assignment[J]. Journal of Beijing Jiaotong University, 2007(3): 18-21, 26.]

[9] 李润梅, 汤淑明. 饱和路网中动态交通分配与路口控制一体化建模研究[J]. 系统仿真学报, 2007(8): 1811- 1815. [LI R M, TANG S M. Integrative modeling study of dynamic traffic assignment and traffic control in saturated network[J]. Journal of System Simulation, 2007 (8): 1811-1815.]

[10] HUANG W, LI L, LO H K. Adaptive traffic signal control with equilibrium constraints under stochastic demand[J]. Transportation Research Part C: Emerging Technologies, 2018(95): 394-413.

[11] CEYLAN H. A hybrid harmony search and TRANSYT hill climbing algorithm for signalized stochastic equilibrium transportation networks[J]. Transportation Research Part C: Emerging Technologies, 2012(25): 152-167.

[12] YU H, MA R, ZHANG H M, et al. Optimal traffic signal control under dynamic user equilibrium and link constraints in a general network[J]. Transportation Research Part B: Methodological, 2018(110): 302-325.

[13] UKKUSURI S V, DOAN K, AZIZ H M, et al. A bi-level formulation for the combined dynamic equilibrium based traffic signal control[J]. Procedia - Social and Behavioral Sciences, 2013(80): 729-752.

[14] 孙智源, 陆化普, 张晓利, 等. 城市交通控制与诱导协同的双层规划模型[J]. 东南大学学报(自然科学版), 2016, 46(2): 450-456. [SUN Z Y, LU H P, ZHANG X L, et al. Bi-level programming model for cooperation of urban traffic control and traffic flow guidance[J]. Journal of Southeast University (Natural Science Edition), 2016, 46(2): 450-456.]

[15] 王薇, 程泽阳, 张伟, 等. 基于层级控制的区域交通信号控制及交通流诱导协调模型[J].武汉理工大学学报 (交通科学与工程版), 2016, 40(4): 609-614. [WANG W, CHENG Z Y, ZHANG W, et al. A regional traffic signal control and traffic flow guidance coordinated model based on level control[J]. Journal of Wuhan University of Technology (Transportation Science- Engineering), 2016, 40(4): 609-614.]

[16] SHEFFI Y. Urban transportation networks: Equilibrium analysis with mathematical programming methods[M]. New Jersey: Prentice-Hall, 1985.

信号控制与交通分配协同模型的自适应IOA 算法

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	陈小红, 蓝秋雨. 不确定交通需求下交叉口信号配时区间优化模型[J]. 交通运输系统工程与信息, 2022, 22(2): 247-256.
[2]	戴庆, 林正奎, 曲毅, 张佳佳. 并列瓶颈路网下个人与家庭混合通勤行为研究[J]. 交通运输系统工程与信息, 2022, 22(1): 182-194.
[3]	宋浪, 王健, 杨滨毓, 安实. 连续流交叉口左转非机动车交通组织创新设计[J]. 交通运输系统工程与信息, 2022, 22(1): 225-233.
[4]	王庆云, 毛保华, 张国伍. 我国交通运输系统工程的演化[J]. 交通运输系统工程与信息, 2021, 21(5): 2-11.
[5]	王炜, 王建, 华雪东, 于维杰, 陈思远, 魏雪延. 基于网络交通分配方法族谱的交通分配一体化技术与工程应用[J]. 交通运输系统工程与信息, 2021, 21(5): 30-39.
[6]	张毅, 姚丹亚, 李力, 裴华鑫, 晏松, 葛经纬. 智能车路协同系统关键技术与应用[J]. 交通运输系统工程与信息, 2021, 21(5): 40-51.
[7]	龙建成, 郭嘉琪. 动态交通分配问题研究回顾与展望[J]. 交通运输系统工程与信息, 2021, 21(5): 125-138.
[8]	宋浪，朱湧，王少飞，沈剑波. 连续流交叉口信号协调配时及延误模型[J]. 交通运输系统工程与信息, 2021, 21(3): 55-63.
[9]	黄爱玲，徐笑涵，关伟，段梦媛. 基于加权耦合映像格子的地铁网络稳定性演化研究[J]. 交通运输系统工程与信息, 2021, 21(3): 140-149.
[10]	关伟，吴建军，高自友. 交通运输网络系统工程[J]. 交通运输系统工程与信息, 2020, 20(6): 9-21.
[11]	代磊磊，刘东波，华璟怡，徐棱，王波. 基于主线拥堵场景的快速路级联信号控制方法[J]. 交通运输系统工程与信息, 2020, 20(4): 56-62.
[12]	张水潮，蔡逸飞，黄锐，周竹萍. 基于预约需求的共享停车平台泊位分配方法[J]. 交通运输系统工程与信息, 2020, 20(3): 137-143.
[13]	李浩，宁煦棋，俞璐，陈浩. 弹性工作制和动态停车收费组合策略研究[J]. 交通运输系统工程与信息, 2020, 20(2): 1-7.
[14]	曲昭伟，潘昭天，陈永恒，李海涛，王鑫. 考虑博弈的多智能体强化学习分布式信号控制[J]. 交通运输系统工程与信息, 2020, 20(2): 76-82.
[15]	王庆云. 交通发展中的技术及技术含量的判断[J]. 交通运输系统工程与信息, 2019, 19(6): 1-5.