期刊文献+

一种增量式多目标优化的智能交通路径诱导方法 被引量:5

An Increment Searching based Multi-objective Path Guidance Method in Intelligent Transportation
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摘要 路径诱导是一种主动引导车辆合理分流来解决城市交通拥堵的方法.本文提出了一种基于增量搜索的多目标优化路径诱导方法.该方法首先利用图论法将复杂路网抽象为点线的赋权图,引入多目标优化变量,建立路网模型;然后在启发式搜索基础上引入增量搜索,结合全局规划和局部动态重规划,实现车辆的实时路径诱导.仿真结果表明该方法能有效地解决复杂路网中车辆的实时路径诱导问题. Route guidance can effectively solve the increasingly crowded urban traffic problem. In this paper, a research on multi-objective path guidance based on increment searching was presented. Firstly, the graph theory method was used to abstract complex road networks to weighted graph that consists of points and lines. Then, a road network model was established by introducing multi-objective optimization variables. Secondly, a heuristic search algorithm based on incremental searching was proposed to achieve vehicle dynamic route guidance. This algorithm combines with the global planning and local dynamic re- planning. Finally, simulation results show that this method can effectively solve the vehicle real-time dy- namic route guidance problem in complex road networks.
出处 《湖南大学学报(自然科学版)》 EI CAS CSCD 北大核心 2013年第5期55-60,共6页 Journal of Hunan University:Natural Sciences
基金 国家自然科学基金资助项目(61071096 61073103 61003233 61202342) 高等学校博士学科点专项科研基金资助项目(20100162110012 20110162110042) 湖南省科技计划科研基金资助项目(2011GK3214)
关键词 动态重规划 增量搜索 路径诱导 dynamic re-planning method incremental searching route guidance
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  • 1朱庆保.动态复杂环境下的机器人路径规划蚂蚁预测算法[J].计算机学报,2005,28(11):1898-1906. 被引量:51
  • 2KOENIG S, LIKHACHEV M, FURCY D. Lifelong planning A [J]. Artificial Intelligence, 2004, 155(1/2): 93-146.
  • 3YANG J, ZHANG M. A two-level path planning method for on-road autonomous driving[C]//2th International Conference on Intelligent System Design and Engineering Application, Sanya, China: 2012 : 661- 664.
  • 4席裕庚,张纯刚.一类动态不确定环境下机器人的滚动路径规划[J].自动化学报,2002,28(2):161-175. 被引量:93
  • 5STEWART S B,WHITE C C. Multiobjective A* [J]. Journal of the ACM, 1991, 38(4): 775-814.
  • 6OLIVEIRA RIOS L H, CHAIMOWICZ L. A survey and classification of A* based best-first heuristic search algorithms [C]//Advanees in Artificial Intelligence-SBIA. Sao Bernardo do Campo, Brazil: 2010:253-262.
  • 7MANDOW L, PEREZ J L DE AL CRUZ. A new approach to multi-objective A* search[C]//Proceedings of the 19th International Joint Conference on Artificial Intelligence. San Francisco, USA: 2005:218- 223.
  • 8DASGUPTA P, CHAKRABARTI P P, DESAKAR S C. Multiobjective heuristic search[M]. Morgan Kaufman,1999.
  • 9XU Y Y,YUE W Y. A generalized framework for BDD-based replanning A* search [C]// Artifical Intelligences, Networking and Parallel/Distributed Computing. 10th ACIN International Conference on Software Engineering. Daegu South Korea, 2009: 133-139.
  • 10HARIKUMAR S, KUMAR S. Iterative deepening multiobjective A* [J]. Information Proeessing Letters, 1996, 58(1) : 11-15.

二级参考文献9

共引文献139

同被引文献33

  • 1张赫,杨兆升,王炜.基于实时交通流信息的中心式动态路径诱导系统行车路线优化技术研究[J].公路交通科技,2004,21(9):91-94. 被引量:18
  • 2李威武,王慧,钱积新.智能交通系统中路径诱导算法研究进展[J].浙江大学学报(工学版),2005,39(6):819-825. 被引量:33
  • 3DESAI P, LOKE S W, DESAI A0 etal. Multi-agent based vehicular congestion management [-C]//lntelligent Vehicles Symposium (IV). Germany: 2011 IEEFo IEEE, 2011: 1031-1036.
  • 4LIAO Gan-li, SHANG Peng-jian. Scaling and complexity-entropy a- nalysis in diriminating traffic dynamics [J]. Fractals, 2012, 20(3/ 4) : 233-243.
  • 5HELBING D. Traffic and related self-driven many-particle systems [J]. Reviews of Modern Physics, 2001, 73(4) : 1067- 1141.
  • 6SHEFFI Y. Urban transportation networks: equilibrium analysis with rnathernatieal programming methods ELM]. USA: PRENTICE- HALL INC, 1985:18-24.
  • 7NARZT W, WILFLINGSEDER U, POMBERGER G, et al. Self- organizing congestion evasion strategies using ant-based pheromones [J]. Intelligent Transport Systems, lET, 2010, 4(1): 93-102.
  • 8BUSCEMA D, IGNACCOLO M, INTURRI G, etal. The impact of real time information on transport network muting through intelligent agent-based simulation [C]//Science and Technology for Humanity (TIC-STH). Canada: 2009 IEEE Toronto International Conference. IEEE, 2009: 72-77.
  • 9ARNAOUT G M, KHASAWNEH M T, ZHANG J, etal. An In- telliDrive application for reducing traffic congestions using agent-based approach [C]//Systems and Information Engineering Design Sympo- sium (SIEI). USA: 2010 IEEE. IEEE, 2010 221-224.
  • 10GRIMM V, BERGER U, DEANGELIS D L, et al. The ODD pro- tocol: a review and first update [J]. Ecological Modelling, 2010, 221 (23) : 2760-2768.

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