|Table of Contents|

Traffic control and VMS collaborative technique in sudden disaster(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2012年06期
Page:
104-110,118
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Traffic control and VMS collaborative technique in sudden disaster
Author(s):
LIN Ci-yun1; 2;  GONG Bo-wen1; 2;  ZHAO Ding-xuan1; 3;  LIU Xue-lian4
1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, Jilin, China; 2. School of Traffic, Jilin University, Changchun 130012, Jilin, China; 3. School of Mechanical Science and Engineering, Jilin University, Changchun 130012, Jilin, China; 4. School of Mechanical Engineering, Ningbo University of Technology, Ningbo 315211, Zhejiang, China
Keywords:
traffic control;  collaborative optimization;  bi-level programming;  variable message signs;  sudden disaster
PACS:
U491
DOI:
-
Abstract:
The influence scope of variable message signs(VMS)was estimated, and a collaborative model integrated of traffic control and VMS was constructed. The route choice behavior of driver was impacted by VMS, and the development of network traffic flow was guided by VMS to the optimization distribution mode. The interception and shunt of network traffic flow were fulfilled by adjusting intersection signal parameters in traffic control to form an optimal traffic flow distribution mode. The model was optimized and solved by combining Frank-Wolfe equilibrium assignment and genetic algorithm. The model and algorithm were developed by using Paramics API. In the condition of network with burst disaster, the model and algorithm were verified by taking software Paramics as simulation platform and Zibo New District of Shandong Province as simulation network. Verified result shows that with the increase of road network saturation,compared with Synchro model, the effect of the model is more obvious in improving performance indexes of road network traffic flow, the ability promoting the stability of road network's traffic flow is stronger, and the equilibrium assignment ability of road network loading is better. When the evacuation of traffic flow for sudden disaster completes 80%, and the link saturations of road network are not more than 0.8, between 0.8 and 1.0, more than 1.0 respectively, compared with Synchro model, the evacuation times respectively decrease by 11.55, 21.84, 25.64 min, the evacuation speed respectively increase by 25.98%, 31.83%, 20.16%. 1 tab, 3 figs, 20 refs.

References:

[1] GHODS A H, FU L, RAHIMI-KIAN A. An efficient optimization approach to real-time coordinated and integrated freeway traffic control[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(4): 873-884.
[2] WU Xin-kai, LIU H X, GEROLIMINIS N. An empirical analysis on the arterial fundamental diagram[J]. Transportation Research Part B: Methodological, 2011, 45(1): 255-266.
[3] GARTNER N H, STAMATIADIS C. Integration of dynamic traffic assignment with real-time traffic adaptive control system[J]. Transportation Research Record, 1998(1644): 150-156.
[4] ABDELFATAH A S, MAHMASSANI H S. System optimal time-dependent path assignment and signal timing in traffic network[J]. Transportation Research Record, 1998(1645): 185-193.
[5] LO H K. A cell-based traffic control formulation: strategies and benefits of dynamic timing plans[J]. Transportation Science, 2001, 35(2): 148-164.
[6] VARIA H R, DHINGRA S L. Dynamic optimal traffic assignment and signal time optimization using genetic algorithms[J]. Computer-Aided Civil and Infrastructure Engineering, 2004, 19(4): 260-273.
[7] UKKUSURI S V, RAMADURAI G, PATIL G. A robust transportation signal control problem accounting for traffic dynamics[J]. Computers and Operations Research, 2010, 37(5): 869-879.
[8] KAROONSOONTAWONG A, WALLER S T. Integrated network capacity expansion and traffic signal optimization problem: robust bi-level dynamic formulation[J]. Networks and Spatial Economics, 2010, 10(4): 525-550.
[9] MITSAKIS E, SALANOVA J M, GIANNOPOULOS G. Combined dynamic traffic assignment and urban traffic control models[J]. Procedia Social and Behavioral Sciences, 2011(20): 427-436.
[10] 卢守峰.基于元胞自动机的交通信号控制与路径诱导的协同研究[D].长春:吉林大学,2006.LU Shou-feng. The study on combined traffic signal control and route guidance based on cellular automata[D]. Changchun: Jilin University, 2006.(in Chinese)
[11] 杨庆芳,杨 朝.基于Q-学习算法的交通控制与诱导协同模式的在线选择[J].吉林大学学报:工学版,2010,40(5):1215-1219. YANG Qing-fang, YANG Chao. On-line selection method of the traffic control and route guidance collaboration mode based on Q-learning algorithm[J]. Journal of Jilin University: Engineering and Technology Edition, 2010, 40(5): 1215-1219.(in Chinese)
[12] 崔后盾.信号控制与VMS诱导的协同策略及信息发布的优化[D].北京:北京交通大学,2012. CUI Hou-dun. The coordination strategy between signal control and VMS and optimization for information publishing[D]. Beijing: Beijing Jiaotong University, 2011.(in Chinese)
[13] CHATTERJEE K, HOUNSELL N B, FIRMIN P E, et al. Driver response to variable message sign information in London[J]. Transportation Research Part C: Emerging Technologies, 2002, 10(2): 149-169.
[14] 尚华艳,黄海军,高自友.可变信息标志诱导下的路径选择行为[J].系统工程理论与实践,2009,29(7):166-172. SHANG Hua-yan, HUANG Hai-jun, GAO Zi-you. Route choice behavior under guidance of variable message signs[J]. Systems Engineering—Theory and Practice, 2009, 29(7): 166-172.(in Chinese)
[15] LEE C, RAN B, YANG F, et al. A hybrid tree approach to modeling alternate route choice behavior with online information[J]. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 2010, 14(4): 209-219.
[16] TAY R, DEB A. Effectiveness of road safety messages on variable message signs[J]. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(3): 18-23.
[17] HENDERSON J M. A planning model for optimizing locations of changeable message signs[D]. Waterloo: University of Waterloo, 2004.
[18] CHEN L W, HU T Y. Dynamic flow equilibrium for flow-responsive signal settings and time-dependent traffic assignment[C]∥IEEE. Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems. Saint Louis: IEEE, 2009: 615-620.
[19] 李润梅,李 伟.饱和路网中动态交通分配和控制一体化建模研究[J].信息与控制,2004,33(6):641-645. LI Run-mei, LI Wei. The integrative modeling of dynamic traffic assignment and traffic control in saturated networks[J]. Information and Control, 2004, 33(6): 641-645.(in Chinese)
[20] KIM E Y, KIN S C, SEONG B S. Structure of attractive and repulsive hard-core Yukawa fluids: density functional perturbation theory[J]. Fluid Phase Equilibria, 2011, 308(1/2): 90-97.

Memo

Memo:
-
Last Update: 2012-12-30