应急疏散交通组织优化方法研究
|
摘要
重大的自然和人为灾害极易造成巨大的人员伤亡和财产损失。为了预防和减轻重大灾害的不利后果,迅速有效地将受灾人群机动化地运送出危险区域,是世界各国广泛采用的最为有效的对策之一。国内外历次重大灾害救援实践表明,道路交通在应急疏散中发挥着极其重要的作用。然而,在尽可能短的时间内疏散大量的受灾人群是一项极其复杂而艰巨的挑战,需要对应急交通疏散进行精心规划和有效组织。
近几年我国呈现出重特大灾害事故频发的态势,尽管有关部门及时启动了应急疏散交通组织预案,对抢险救灾起到了一定的积极作用。但实践表明,仅仅依靠定性的应急预案是远远不够的。目前,在重大灾害条件下我国的道路交通管理主要采用行政命令、部门联动督促救援等行政管理方式,缺乏有针对性的快速交通规划、交通信号控制和交通信息诱导等应急疏散交通组织优化方法的支持,致使道路交通常常成为应急疏散的瓶颈之一。因此,开展道路交通应急疏散组织优化方法的研究,对提升我国重大灾害的处置能力具有重大意义。
本论文对道路交通应急疏散的组织优化方法进行了创新性研究,并取得了如下的研究成果。
(1)提出了基于信息核度的应急疏散路网关键节点和关键路径的确定方法
为了鉴别出应急疏散路网交通组织和管理的重点,提出了应急疏散路网关键节点和关键路径的基本概念,基于信息核度设计了应急疏散路网效率关键节点、连通关键节点、综合关键节点和关键路径的确定方法,并提出了疏散路径潜在瓶颈节点的确定方法。所提出的方法有助于确定应急疏散路网交通规划的重点,可有效地减少应急疏散交通组织的复杂性。
(2)提出了基于CCRP的应急疏散动态交通分配方法
针对应急疏散过程高度动态性的特点,以提高应急疏散交通规划的有效性为目标,在考虑路径安全性和通行质量的条件下,通过引入惩罚函数,对具有通行能力约束的路径规划(CCRP)算法进行了改进,并据此提出了一种应急疏散动态交通分配的新方法。不但有效提高了动态交通分配的速度,而且通过将疏散路网中的突发拥堵点作为虚拟疏散原点纳入动态交通分配过程,提高了应急疏散交通分配的应变能力,为进一步提高应急疏散交通规划的有效性提供技术支持。
(3)基于混合整数线性规划、非线性二次规划和仿人智能控制,提出了应急疏散通道交通信号控制方法
针对应急疏散通道通常处于过饱和状态的特点,以疏散通道通行能力最大为目标,一方面,综合考虑应急疏散通道应对突发交通拥堵等的应变性,在关键交叉口为路径转移的交通流分配合理的相位,基于混合整数线性规划与非线性二次规划提出了一种干路应急疏散通道交通信号控制方法；另一方面,考虑到匝道交通流对城市快速路和高速公路疏散交通流的影响,基于仿人智能控制理论,提出了一种应急疏散快速道路入口匝道的交通信号控制方法。前者改善了应急疏散通道的疏散效率,并使疏散通道具有必要的应变能力；后者解决了匝道交通流可插车状态的判别问题,使匝道交通流既充分利用城市快速路和高速公路的通行能力,又减少对疏散通道交通流的干扰。
(4)提出了基于模糊决策的应急疏散交通信息诱导策略与方案的生成方法
为了提高应急疏散交通信息诱导的针对性和有效性,以应急疏散交通信息需求调查为基础,考虑到应急疏散条件下驾驶员路径选择的主观性和模糊性特性,运用模糊决策理论,提出了交通信息条件下驾驶员路径选择行为模型,揭示了影响驾驶员接受推荐路径的重要因素,并据此提出了应急疏散交通信息诱导策略与方案的生成方法,对应急疏散交通信息诱导方法的研究进行了进一步的探索。
以MatLab2007、SQL Server2005和交通仿真软件TransModeler2.6为工具,以某省会城市及周边高等级道路组成的路网为对象,对上述方法进行了编程实现和仿真验证。结果表明,本论文所提出的应急疏散交通规划、信号控制和信息诱导相关的方法,可有效提高应急疏散交通组织的效率,有助于针对时变的应急疏散条件与需求快速生成交通组织优化方案,并有效改善应急疏散交通组织的快速响应能力和动态调整能力。
本论文的研究内容、研究方法和研究结论是对应急疏散交通组织优化方法的有益探索,可以为重大灾害条件下道路交通应急疏散的交通组织与管理提供重要的技术支持。
Large-scale natural or man-made disasters can have devastating impacts in terms of loss of life, human injury and property damage. Evacuation from dangerous areas is one of the most common protective actions that can be taken for disasters. Recent natural or man-made disasters around the world have provide compelling evidence that transportation system plays a crucial role in the emergency evacuation and have stressed the need for effective evacuation traffic organization to maximize the utilization of the transportation system and to minimize fatalities and losses. However, evacuating a large population exposed to immediate or foreseeable life-threatening danger in the shortest time is an extremely complicated and difficult challenge, which is primarily dependent on careful planning and effective organization.
In recent years, it shows the trend of frequent disasters in China. Even through an emergency evacuation plan promptly launched by authorities played a positive role, However, Practice shows that relying solely on qualitative emergency plan is not enough. On the whole, the study on sudden disasters and emergency evacuation in China starts lately. Traffic management in the emergency evacuation mainly takes executive orders and departmental interaction supervise rescue. Without strong support of traffic organization optimization technology including rapid traffic planning, real-time traffic control and traffic information guidance, emergency evacuation traffic management are becoming lack of the optimal strategy to respond whenever necessary with flexibility, coordination and speed, thus leading to the road traffic is one of the bottlenecks in emergency evacuation. Therefore, it is of great significance to research on the methods of traffic emergency evacuation organization optimization for improving the capability of emergency traffic organization.
This dissertation studies the methods on traffic organization optimization for emergency evacuation from the following aspecets, and obtains the corresponding creative relults.
(1) Proposes a novel method to identify the key nodes and critical routes of road network for emergency evacuation based on information centrality.
In order to identify the key of traffic organization and management for emergency evacuation, this dissertation proposes the concepts of the key nodes and critical routes of road network. Moreover, based on the basic principle of information centrality, presents a method to determine the efficiency key nodes, the connectivity key nodes, the comprehensive key nodes and the critical routes of road network for emergency evacuation. Finally, proposes the method to identify the potential bottleneck of emergency evacuation routes. This method helps determine the focus of traffic management for emergency evacuation, improve the effectiveness of traffic plans, and can effectively reduce the complexity of traffic organization for emergency evacuation.
(2) Presents a real-time dynamic traffic assignment method for emergency evacuation on the basis of CCRP algorithm.
Due to the highly dynamic and uncertain natures involved in the process of emergency evacuation, this paper, on the basis of the Capacity Constrained Routing Planning (CCRP) approach, proposes the real-time traffic assignment method for emergency evacuation aiming at improving the efficiency of traffic assignment. In order to overcome the shortcomings that CCRP algorithm does not take into account the safety and access quality requirements of evacuation routes, and can not solve the sudden congestion in evacuation, the presented algorithm, based on the real-time information, introduces the penalty function in the calculation of link travel time to generate high quality routes and bypass the danger zone. Moreover, the sudden congestion points are taken as temporary virtual origin. Thus, the proposed algorithm can effectively complete real-time route optimization and redistribution of traffic flow as an ideal reference point for signal control and information guidance program for emergency evacuation. This method can effectively improve the speed of the dynamic traffic assignment, further improve the effectiveness of emergency evacuation, and provide technical support for transportation planning of emergency evacuation.
(3) Proposes a traffic signal control method for evacuation corridor based on mixed integer linear programming, non-linear quadratic programming and human-simulated intelligent control.
In order to meet the requirements of oversaturated corridor, real-time and contingency, this dissertation, aiming at maximizing the efficiency of traffic corridor for evacuation corridor, for one thing, proposes a two-step phase-oriented real-time traffic signal control algorithm by combination of a mixed-integer linear programming and a non-linear quadratic programming to maximize evacuation throughput, fully utilize storage capacity of the road, and provide reasonable service to the transfer inflow and outflow at the key intersections; for another, enhancing the real-time efficiency of ramp, this paper, based on the human-simulated intelligent control, proposes a novel traffic signal control algorithm of ramp metering of urban freeway and expressway for emergency evacuation. The former not only improves the efficiency of emergency evacuation, but has the necessary contingency ability for evacuation corridor; the latter fully utilizes the capacity of urban freeway or expressway and reduces the interference to the operating flow in the main corridor.
(4) Presents a method of traffic information guidance strategy and program generation for emergency evacuation on the basis of fuzzy decision.
In order to improve the effectiveness of emergency evacuation route guidance information, this paper takes into account the subjective and fuzzy factors of driver's route choice behaviour in emergency evacuation, based on the actual Stated Preference (SP) investigation, uses concepts from fuzzy set theory and approximate reasoning and proposes the driver route choice behavior model in the presence of traffic information. On this basis, this paper reveals the important factors for drivers to accept the recommenden path, and presents the method of route information guidance strategy and program generation.
Taking a capital city and surrounding road network consisting of high-grade roads as research objective, this thesis realizes the above methods through programming and simulation using programming software MATLAB2007, SQL Server2005, and traffic simulation software TransModeler2.6as tools. The results show that the proposed methods of traffic planning, signal control, and information guidance can effectively improve the efficiency of traffic organization for emergency evacuation, help to rapidly generate traffic organization optimization scheme for emergency evacuaion, thus effectively to enhance the rapid response and real-time adjustment of the transportation system in critical situations.
The research contents, methods and conclusions are useful exploration for the traffic organization optimization methods under disasters, and provide a theoretical basis and engineering reference to real-time road traffic organization and management for emergency evacuation.
近几年我国呈现出重特大灾害事故频发的态势,尽管有关部门及时启动了应急疏散交通组织预案,对抢险救灾起到了一定的积极作用。但实践表明,仅仅依靠定性的应急预案是远远不够的。目前,在重大灾害条件下我国的道路交通管理主要采用行政命令、部门联动督促救援等行政管理方式,缺乏有针对性的快速交通规划、交通信号控制和交通信息诱导等应急疏散交通组织优化方法的支持,致使道路交通常常成为应急疏散的瓶颈之一。因此,开展道路交通应急疏散组织优化方法的研究,对提升我国重大灾害的处置能力具有重大意义。
本论文对道路交通应急疏散的组织优化方法进行了创新性研究,并取得了如下的研究成果。
(1)提出了基于信息核度的应急疏散路网关键节点和关键路径的确定方法
为了鉴别出应急疏散路网交通组织和管理的重点,提出了应急疏散路网关键节点和关键路径的基本概念,基于信息核度设计了应急疏散路网效率关键节点、连通关键节点、综合关键节点和关键路径的确定方法,并提出了疏散路径潜在瓶颈节点的确定方法。所提出的方法有助于确定应急疏散路网交通规划的重点,可有效地减少应急疏散交通组织的复杂性。
(2)提出了基于CCRP的应急疏散动态交通分配方法
针对应急疏散过程高度动态性的特点,以提高应急疏散交通规划的有效性为目标,在考虑路径安全性和通行质量的条件下,通过引入惩罚函数,对具有通行能力约束的路径规划(CCRP)算法进行了改进,并据此提出了一种应急疏散动态交通分配的新方法。不但有效提高了动态交通分配的速度,而且通过将疏散路网中的突发拥堵点作为虚拟疏散原点纳入动态交通分配过程,提高了应急疏散交通分配的应变能力,为进一步提高应急疏散交通规划的有效性提供技术支持。
(3)基于混合整数线性规划、非线性二次规划和仿人智能控制,提出了应急疏散通道交通信号控制方法
针对应急疏散通道通常处于过饱和状态的特点,以疏散通道通行能力最大为目标,一方面,综合考虑应急疏散通道应对突发交通拥堵等的应变性,在关键交叉口为路径转移的交通流分配合理的相位,基于混合整数线性规划与非线性二次规划提出了一种干路应急疏散通道交通信号控制方法；另一方面,考虑到匝道交通流对城市快速路和高速公路疏散交通流的影响,基于仿人智能控制理论,提出了一种应急疏散快速道路入口匝道的交通信号控制方法。前者改善了应急疏散通道的疏散效率,并使疏散通道具有必要的应变能力；后者解决了匝道交通流可插车状态的判别问题,使匝道交通流既充分利用城市快速路和高速公路的通行能力,又减少对疏散通道交通流的干扰。
(4)提出了基于模糊决策的应急疏散交通信息诱导策略与方案的生成方法
为了提高应急疏散交通信息诱导的针对性和有效性,以应急疏散交通信息需求调查为基础,考虑到应急疏散条件下驾驶员路径选择的主观性和模糊性特性,运用模糊决策理论,提出了交通信息条件下驾驶员路径选择行为模型,揭示了影响驾驶员接受推荐路径的重要因素,并据此提出了应急疏散交通信息诱导策略与方案的生成方法,对应急疏散交通信息诱导方法的研究进行了进一步的探索。
以MatLab2007、SQL Server2005和交通仿真软件TransModeler2.6为工具,以某省会城市及周边高等级道路组成的路网为对象,对上述方法进行了编程实现和仿真验证。结果表明,本论文所提出的应急疏散交通规划、信号控制和信息诱导相关的方法,可有效提高应急疏散交通组织的效率,有助于针对时变的应急疏散条件与需求快速生成交通组织优化方案,并有效改善应急疏散交通组织的快速响应能力和动态调整能力。
本论文的研究内容、研究方法和研究结论是对应急疏散交通组织优化方法的有益探索,可以为重大灾害条件下道路交通应急疏散的交通组织与管理提供重要的技术支持。
Large-scale natural or man-made disasters can have devastating impacts in terms of loss of life, human injury and property damage. Evacuation from dangerous areas is one of the most common protective actions that can be taken for disasters. Recent natural or man-made disasters around the world have provide compelling evidence that transportation system plays a crucial role in the emergency evacuation and have stressed the need for effective evacuation traffic organization to maximize the utilization of the transportation system and to minimize fatalities and losses. However, evacuating a large population exposed to immediate or foreseeable life-threatening danger in the shortest time is an extremely complicated and difficult challenge, which is primarily dependent on careful planning and effective organization.
In recent years, it shows the trend of frequent disasters in China. Even through an emergency evacuation plan promptly launched by authorities played a positive role, However, Practice shows that relying solely on qualitative emergency plan is not enough. On the whole, the study on sudden disasters and emergency evacuation in China starts lately. Traffic management in the emergency evacuation mainly takes executive orders and departmental interaction supervise rescue. Without strong support of traffic organization optimization technology including rapid traffic planning, real-time traffic control and traffic information guidance, emergency evacuation traffic management are becoming lack of the optimal strategy to respond whenever necessary with flexibility, coordination and speed, thus leading to the road traffic is one of the bottlenecks in emergency evacuation. Therefore, it is of great significance to research on the methods of traffic emergency evacuation organization optimization for improving the capability of emergency traffic organization.
This dissertation studies the methods on traffic organization optimization for emergency evacuation from the following aspecets, and obtains the corresponding creative relults.
(1) Proposes a novel method to identify the key nodes and critical routes of road network for emergency evacuation based on information centrality.
In order to identify the key of traffic organization and management for emergency evacuation, this dissertation proposes the concepts of the key nodes and critical routes of road network. Moreover, based on the basic principle of information centrality, presents a method to determine the efficiency key nodes, the connectivity key nodes, the comprehensive key nodes and the critical routes of road network for emergency evacuation. Finally, proposes the method to identify the potential bottleneck of emergency evacuation routes. This method helps determine the focus of traffic management for emergency evacuation, improve the effectiveness of traffic plans, and can effectively reduce the complexity of traffic organization for emergency evacuation.
(2) Presents a real-time dynamic traffic assignment method for emergency evacuation on the basis of CCRP algorithm.
Due to the highly dynamic and uncertain natures involved in the process of emergency evacuation, this paper, on the basis of the Capacity Constrained Routing Planning (CCRP) approach, proposes the real-time traffic assignment method for emergency evacuation aiming at improving the efficiency of traffic assignment. In order to overcome the shortcomings that CCRP algorithm does not take into account the safety and access quality requirements of evacuation routes, and can not solve the sudden congestion in evacuation, the presented algorithm, based on the real-time information, introduces the penalty function in the calculation of link travel time to generate high quality routes and bypass the danger zone. Moreover, the sudden congestion points are taken as temporary virtual origin. Thus, the proposed algorithm can effectively complete real-time route optimization and redistribution of traffic flow as an ideal reference point for signal control and information guidance program for emergency evacuation. This method can effectively improve the speed of the dynamic traffic assignment, further improve the effectiveness of emergency evacuation, and provide technical support for transportation planning of emergency evacuation.
(3) Proposes a traffic signal control method for evacuation corridor based on mixed integer linear programming, non-linear quadratic programming and human-simulated intelligent control.
In order to meet the requirements of oversaturated corridor, real-time and contingency, this dissertation, aiming at maximizing the efficiency of traffic corridor for evacuation corridor, for one thing, proposes a two-step phase-oriented real-time traffic signal control algorithm by combination of a mixed-integer linear programming and a non-linear quadratic programming to maximize evacuation throughput, fully utilize storage capacity of the road, and provide reasonable service to the transfer inflow and outflow at the key intersections; for another, enhancing the real-time efficiency of ramp, this paper, based on the human-simulated intelligent control, proposes a novel traffic signal control algorithm of ramp metering of urban freeway and expressway for emergency evacuation. The former not only improves the efficiency of emergency evacuation, but has the necessary contingency ability for evacuation corridor; the latter fully utilizes the capacity of urban freeway or expressway and reduces the interference to the operating flow in the main corridor.
(4) Presents a method of traffic information guidance strategy and program generation for emergency evacuation on the basis of fuzzy decision.
In order to improve the effectiveness of emergency evacuation route guidance information, this paper takes into account the subjective and fuzzy factors of driver's route choice behaviour in emergency evacuation, based on the actual Stated Preference (SP) investigation, uses concepts from fuzzy set theory and approximate reasoning and proposes the driver route choice behavior model in the presence of traffic information. On this basis, this paper reveals the important factors for drivers to accept the recommenden path, and presents the method of route information guidance strategy and program generation.
Taking a capital city and surrounding road network consisting of high-grade roads as research objective, this thesis realizes the above methods through programming and simulation using programming software MATLAB2007, SQL Server2005, and traffic simulation software TransModeler2.6as tools. The results show that the proposed methods of traffic planning, signal control, and information guidance can effectively improve the efficiency of traffic organization for emergency evacuation, help to rapidly generate traffic organization optimization scheme for emergency evacuaion, thus effectively to enhance the rapid response and real-time adjustment of the transportation system in critical situations.
The research contents, methods and conclusions are useful exploration for the traffic organization optimization methods under disasters, and provide a theoretical basis and engineering reference to real-time road traffic organization and management for emergency evacuation.
引文
[1]Akhilesh Pal, Andrew J. Graettinger, Michael H. Triche Emergency Evacuation Modeling Based On Geographical Information System Data [C]. Transportation Research 2003. Washington,D.C.
[2]Elba Urbina, Brian Wolshon. National review of hurricane evacuation plans and policies:a comparison and contrast of state practices[C]. Transportation Research. Part A:Policy and Practice.2003. Washington,D.C.,U.S.A.
[3]宁乐然.道路交通安全通论[M].2006,北京：中国人民公安大学出版社.164.
[4]崔新书.城市道路交通组织设计探讨[J].城市道桥与防洪,2004(6)：p.21-26.
[5]翟忠民.道路交通组织优化[M].2004,北京：人民交通出版社.
[6]Georgiana L. Hamza-Lup, Kien A. Hua, Rui Peng, Yao H. Ho. Real-Time Traffic Evacuation Management under Multiple Coordinated Human-Caused Threats [C].12th World Congress on ITS.2005. San Francisco.
[7]Hamza-Lup, Georgiana L. Exit-Point based Approaches for Real-Time Traffic Evacuation Management[C]. IEEE Intelligent Transportation Systems Conference. 2006 Sept. Toronto, Canada.
[8]胡红,刘小明,魏恒,杨孝宽.奥运应急疏散路网规划系统方法[J].北京工业大学学报,2008.34(8)：p.865-872.
[9]Thomas J. Cova, Justin P. Johnson. A network flow model for lane-based evacuation routing[C]. Transportation Research Part A:Policy and Practice,2003 (August).37(7): p.579-604.
[10]Chi Xie, Dung-Ying Lin, S. Travis Waller. A dynamic evacuation network optimization problem with lane reversal and crossing elimination strategies[J]. Transportation Research Part E,2010(46):p.295-316.
[11]Sangho Kim, Shashi Shekhar. Contraflow Network Reconfiguration for Evacuation Planning:A Summary of Results[C]. the 13th annual ACM international workshop on Geographic information systems.2005:ACM New York, NY, USA
[12]Sangho Kim, Shashi Shekhar, Manki Min. Contraflow Transportation Network Reconfiguration for Evacuation Route Planning[C]. IEEE Transactions On Knowledge And Data Engineering.2008.
[13]Campos V. B. G, Da Silvap. A. L., Nettop. O. B. Evacuation transportation planning: A method of identify optimal independent routes[C], International conference on urban transport No5.1999, WIT Press, Southampton, ROYAUME-UNI (2000) (Monographie):Rhodes, GRECE
[14]刘丽霞,杨骅飞.突发事件等复杂情形下的交通路径选择问题[J].北京联合大学学报(自然科学版),2004.18(3)：p.67-71.
[15]姚清林.优选地震救灾路径的图与模糊集算法[J].自然灾害学报,2006.15(2)：p.143-148.
[16]肖国清,温丽敏,陈宝智,王浩.毒气泄漏时的最佳疏散路径[J].东北大学学报(自然科学版),2001.22(6)：p.674-676.
[17]Qingsong Lu, Yan Huang, and Shashi Shekhar. Evacuation Planning:A Capacity Constrained Routing Approach[J]. Indian Standards Institution,2003:p.111-125.
[18]Qingsong Lu, Betsy George, and Shashi Shekhar. Capacity Constrained Routing Algorithms for Evacuation Planning:A Summary of Results[J]. SSTD:international symposium on spatial and temporal databases 2005.3633:p.291-307.
[19]Maimai Zeng, Cheng Wang. Evacuation Route Planning Algorithm:Longer Route Preferential[J]. ISNN,2009:p.1062-1071.
[20]Sangho Kim, Betsy George, Shashi Shekhar. Evacuation Route Planning:Scalable Heuristics[C]. Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems.2007.
[21]Rene Bustamante, Jeffrey M. R. Wolff. Capacity Constrained Route Planner (CCRP) with Node Edge Parameter Time-Series (NEPTS)[C].2007.
[22]Han, Lee D. Global Optimization of EmergencyEvacuation Assignments [J]. Interfaces, 2006 36(6):p.502-513.
[23]Chung-Yung Wang, Shou-Ren Hu. A Study on Emergency Evacuation and Resue Network Reconstruction for Natural Disasters with Multi-Class Users Travel Behavior Constraints [J]. Journal of the Eastern Asia Society for Transportation Studies, 2005.6:p.4269-4284.
[24]Ying Liu, Xiaorong Lai. Two-Level Integrated Optimization System for Planning of Emergency Evacuation[J]. Journal of Transportation Engineering,2006:p.800-807.
[25]Mohammad Saadatseresht, Ali Mansourian, Mohammad Taleai. Evacuation planning using multiobjective evolutionary optimization approach[J]. European Journal of Operational Research,2009.198:p.305-314.
[26]Chen, Ming. Traffic Signal Timing for Urban Evacuation[D], Department of Civil Engineering.2005, the University of Maryland.
[27]Liu, Ying. An Integrited Optimal Control System for Emergency Evacuation[D] Philosophy Department.2007, the University of Maryland.
[28]胡红,刘小明,魏恒,杨孝宽.基于路口和路段入口综合控制的应急疏散交通控制算法[J].交通运输工程学报,2009.9(3)：p.108-116.
[29]Timothy J.Hall. GPS-Based Traffic Control Preemption System[P].1996, Minnesota Mining and Manufacturing Company,St. Paul,Minn.:U.S.A.
[30]W.Bentrott, Donald. Emergency Vehicle Command and Control System for Traffic Signal Preemption[P].1999, Interlog,Inc.,Falls Church,Va.:U.S.A.
[31]Bachelder, Aaron D. Forwarding System for Long-range Preemption and Corridor Clearance for Emergency Response[P].2005:U.S.A. p.1-17.
[32]Xinkai Wu, Henry X. Liu, Douglas Gettman. Identification of oversaturated intersections using high-resolution traffic signal data[J]. Transportation Research Part C,2010.18:p.626-638.
[33]Papageorgiou, Markos. An Integrated Control Approach for Traffic Corridors[J]. Transportation Rearch,1995.3(1):p.19-30.
[34]K. Aboudolas, M. Papageorgiou, E. Kosmatopoulos. Store-and-forward based methods for the signal control problem in large-scale congested urban road networks[J]. Transportation Research Part C,2009.18:p.163-174.
[35]K. Aboudolas, M. Papageorgiou, A. Kouvelas, E. Kosmatopoulos. A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks[J]. Transportation Research Part C,2010.18:p.680-694.
[36]Lin Zhang, Honglong Li, Panos D. Prevedouros. Signal Control for Oversaturated Intersections Using Fuzzy Logic[C]. The 2005 Annual Meeting of the TRB.2005. Washington D.C,U.S.A.
[37]Edward B. Lieberman, Jinil Chang. Formulation of a Real-Time Control Policy for Oversaturated Arterials[C]. the Transportation Research Board's 79th Annual Meeting. 2000. Washington, D.C.
[38]Michele C. Weigle, Stephan Olariu. Intelligent Highway Infrastructure for Planned Evacuations[C]. IEEE International Performance, Computing, and Communications Conference,2007. IPCCC 2007..2007.
[39]J.C.Severson, V.Maier-Speredelozzi, J.H.Wang,C.E.Collyer. Enhancing the Preparedness and Response of the Rhode Island Transportation System in Natural or Human-caused Disasters[C]. Transportation Research. Washington,D.C.
[40]Rizvi, S.R. Olariu, S. Weigle, M.C. Rizvi, M.E.. A Novel Approach to Reduce Traffic Chaos in Emergency and Evacuation Scenarios[J]. Vehicular Technology Conference,2007. VTC-2007 Fall.2007 IEEE 66th.2007 Oct.
[41]吕能超,严新平,刘正林.突发事件交通网络VMS发布点优化[J].中国科技论文在线,2008.3(10)：p.716-719.
[42]于悦.重大灾害条件下应急交通诱导系统关键技术研究[D],交通学院.2010,吉林大学：长春.p.151-168.
[43]林赐云.突发灾害下应急交通保障决策支持系统关键技术研究[D],交通学院.2010,吉林大学：中国,长春.p.1-150.
[44]金美莲.城市突发公共事件下混合流疏散问题研究[D],工程物理系.2008,清华大学：北京.p.1-113.
[45]金美莲,陈涛,申世飞.城市混合流疏散中不同交通方式配比对疏散时间的影响[J].清华大学学报(自然科学版),2009.49(2)：p.179-182.
[46]M. Wardman, P. W. Bonsall, J. D. Shires. Driver Response to Variable Message Signs a Stated Preference Investigation[J]. Transportation Research,1997.5(6):p.389-405.
[47]Alena Erke, Fridulv Sagberg, Rolf Hagman. Effects of Route Guidance Variable Message Signs on Driver Behaviour[J]. Transportation Research Part F,2007(10):p. 447-457.
[48]Srinivas Peeta, Jorge L. Ramos, Raghubhushan Pasupathy. Content of Variable Message Signs and On-line Driver Behavior[C]. the 79th Annual Meeting of the Transportation Research Boad.2000. Washington D.C.
[49]Jun-Seok Oh, R.Jayakrishnan, Anthony Chen,Hai Yang. A Parametric Framework for Route Guidance in Advanced Traveler Information Systems with Endogenously Determined Driver Compliance[C]. Transportation Research.2000. Washington,D.C.
[50]Tsippy Lotan, Haris N. Koutsopoulos. Models for Route Choice Behavior in the Presence of Information using Concepts from Fuzzy Set Theory and Approximate Reasoning[J]. Transportation,1993(20):p.129-155.
[51]Srinivas Peeta, Jeong W. Yu. A Data-Consistent Fuzzy Approach for On-Line Driver Behavior under Information Provision[C].81th Annual Meeting of the Transportation Research Board.2002. Washington D.C.,U.S.A.
[52]Petros C. Vythoulkas, Haris N. Koutsopoulos. Modeling discrete choice behavior using concepts from fuzzy set theory, approximate reasoning and neural networks[J]. Transportation Research Part C,2003(11):p.51-73.
[53]Alexander Paz, Srinivas Peeta. Behavior-consistent Real-time Traffic Routing under Information Provision[J]. Transportation Research Part C,2009(17):p.642-661.
[54]Karthik K. Srinivasan, Hani S. Mahmassani. Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS:a dynamic kernel logit formulation[J]. Transportation Research Part B,2003(37):p.793-814.
[55]Jeong W.Yu, Srinivas Peeta. A Behavior-based Network Modeling Approach for Deployable Information Provision Strategies[C]. Transportation Research 2007. Washington,D.C.
[56]Richard H. M. Emmerink, Peter Nijkamp, Peet Rietveld, Josn. Van Ommeren. Variable Message Signs and Radio Traffic Information an Integrated Empirical Analysis of Drivers' Route Choice Behaviour[J]. Transpn. Research,1996.30(2):p.135-153.
[57]Asad J. Khattak, Xiaohong Pan, Billy Williams, Nagui Rouphail, Yingling Fan. Traveler Information Delivery Mechanisms Impacts on Consumer Behavior[C]. Transportation Research Board.2008. Washington, D.C., U.S.A.
[58]Bavelas, A. A Mathematical Model for Group Structures [J]. Human Organization, 1948(7):p.16-30.
[59]Bavelas, A. Communication Patterns in Task Oriented Groups [J]. Journal of the Acoustical Society of America,1950(22):p.271-282.
[60]Stefan Lammer, Bjorn Gehlsen, Dirk Helbing. Scaling Laws in the Spatial Structure of Urban Road Networks [J]. Physica A,2006.363(1):p.89-95.
[61]高自友,赵小梅,黄海军,毛保华.复杂网络理论与城市交通系统复杂性问题的相关研究[J].交通运输系统工程与信息,2006.6(3)：p.41-47.
[62]赵月,杜文,陈爽.复杂网络理论在城市交通网络分析中的应用[J].城市交通,2009.7(1)：p.57-65.
[63]吴彤.复杂网络研究及其意义[J].哲学研究,2004(8)：p.58-63.
[64]I. Vragovic, E. Louis, A. Diaz-Guilera. Efficiency of Informational Transfer in Regular and Complex Networks[J]. Physical Review E,2005.71(3):p.1-9.
[65]安世虎,都艺兵,曲吉林.节点集重要性测度[J].中国管理科学,2006.14(1)：p.106-111.
[66]赫南,李德毅,淦文燕,朱熙.复杂网络中重要性节点发掘综述[J].计算机科学, 2007.34(12)：p.1-5.
[67]王炜.城市交通规划理论与方法[M].1992,人民交通出版社：北京.32-33.
[68]姜桂艳,吴正言,常安德,张春勤.基于信息核度的应急疏散路网关键节点和路径确定方法[J].吉林大学学报(工学版),2010.40：p.163-168.
[69]Jiang Guiyan, Wu Zhengyan, Li Qiao,Zhang Chunqin. Key nodes and corridors identifying method for large-scale evacuation network based on information centrality[C].2010 2nd International Conference on Future Computer and Communication May,2010. Wuhan,China.
[70]Jiang, Bin. Street Hierarchies:A Minority of Streets Account for a Majority of Traffic Flow[J]. International Journal of Geographical Information 2009.23(8):p.1033-1048.
[71]Richard L. Church, Thomas J. Cova. Mapping evacuation risk on transportation networks using a spatial optimization model[J]. Transportation Research Part C, 2000(8):p.321-336.
[72]Henry X. Liu, Xiaozheng He, Xuegang (Jeff) Ban. A Cell-based Many-to-One Dynamic System Optimal Model and Its Heuristic Solution Method for Emergency Evacuation[C]. the 86th TRB Annual Meeting. July,2006. Washington D.C, USA.
[73]金元郁,顾兴源.改进的广义预测算法[J].信息与控制,1990(3)：p.8-14.
[74]庞中华,崔红.系统辨识与自适应控制MATLAB仿真.2009,北京：北京航空航天大学出版社.2、142.
[75]L. Fu, D. Sun, L.R. Rilett. Heuristic shortest path algorithms for transportation applications:State of the art[J]. Computers & Operations Research,2006.33:p. 3324-3343.
[76]B.Hoppe, E.Tardos. Polynomial Time Algorithms for Some Evacuation Problems. the 5th Annual ACM-SIAM Symposium on Discrete Algorithms.1994.
[77]张飞舟,范耀祖.交通控制工程[M].2005,中国铁道出版社：北京.219-226.
[78]师黎,陈铁军,李晓媛,姚利娜.智能控制理论及应用[M].2009,北京：清华大学出版社.382-390.
[79]Li Zushu, Wang Guiping. Schema Theory and Human Simulated Intelligent Control[C]. the 2003 IEEE International Conference on RoboticsJntelligent Systems and Signal Processing.2003. Changsha, China
[80]Zushu Li, Hua Zhang, Yongling Wen, Guiping Wang. Human Simulated Intelligent Control Based on Sensory-Motor Intelligent Schemas[C]. The 5th World Congress on Intelligent Control and Automation.2004. HangZhou,China.
[81]Nathan H. Gartner, Susan F. Assmann, Fernando Lasaga, Dennis L. Hou. A Multi-Band Approach to Arterial Traffic Signal Optimization [J]. Transportation Rearch, 1991.25B(1):p.55-74.
[82]Gazis, Denos C. Optimum Control of a System of Oversaturated Inter sections [J]. Operations Research,1964.12(6):p.815-831.
[83]Henry X. Liu, Xinkai Wu, Wenteng Ma, Heng Hu. Real-time Queue Length Estimation for Congested Signalized Intersections[J]. Transportation Research Part C, 2009.17:p.412-427.
[84]Hua Zhang, Zushu Li, Jiangong Gu, Guiqiang Chen, Yuanhong Dan. A General Parameters Optimization Method for Human-Simulated Intelligent Controller and its Application Research[C]. The 6th World Congress on Intelligent Control.2006. Dalian,China.
[85]Guiyan Jiang, Zhengyan Wu, Chunqin Zhang, Qiao Li. Real time traffic control framework based on simulated human intelligence for emergency evacuation[C].2010 8th World Congress on Intelligent Control and Automation July,2010. Jinan, China
[86]Yi-Chang Chiu, Pavan Korada, Pitu B. Mirchandani. Dynamic Traffic Management for Evacuation[C]. The 84th Annual Meeting of Transportation Research Board.2005. Washington,D.C. USA.
[87]Caspar G Chorus, Theo A. Arentze, Harry J.P. Timmermans. Traveler Compliance with Advice:A Bayesian Utilitarian Perspective[J]. Transportation Research Part E, 2009(45):p.486-500.
[88]Yu-Hsin Liu, Hani S. Mahmassani. Global maximum likelihood estimation procedure for multinomial probit (MNP) model parameters [J]. Transportation Research Part B, 2000(34):p.419-449.
[89]Dusan Teodorovic, Goran Pavkovic. The Fuzzy Set Theory Approach to the Vehicle Routing Problem When Demand at Nodes Is Uncertain[J]. Fuzzy Sets and Systems, 1996(82):p.307-317.
[90]Turan Arslan, C.Jotin Khisty. A Rational Approach to Handling Fuzzy Perceptions in Route Choice[J]. European Journal of Operational Research,2006(168):p.571-583.
[91]Alexander Paz, Srinivas Peeta. Information-based Network Control Strategies Consistent with Estimated Driver Behavior[J]. Transportation Research Part B, 2009(43):p.73-96.
[92]Zadeh, L. A. Fuzzy Sets[J]. Information and Control,1965.8(3):p.338-353.
[93]Zadeh, Lotfi A. Outline of a New Approach to the Analysis of Complex Systems and Decision Processes[C]. IEEE Transactions on Systems,Man,and Cybernetics.1973.
[94]Zadeh, L.A. The Concept of a Linguistic Variable and its Application to Approximate Reasoning[J]. Information Science,1975.8:p.199-249.
[95]A. Merve Acilar, Ahmet Arslan. Optimization of Multiple Input-Output Fuzzy Membership Using Clonal Selection Algorithm[J]. Expert Systems with Applications, 2011(38):p.1374-1381.
[96]David Peidro, Pandian Vasant. Transportation Planning with Modified S-curve Membership Functions Using an Interactive Fuzzy Multi-objective Approach[J]. Applied Soft Computing,2011(11):p.2656-2663.
[97]Hamza-Lup, Georgiana L. Exit-Point based Approaches for Real-Time Traffic Evacuation Management[C]. IEEE Intelligent Transportation Systems Conference. Sept.,2006. Toronto, Canada.
[2]Elba Urbina, Brian Wolshon. National review of hurricane evacuation plans and policies:a comparison and contrast of state practices[C]. Transportation Research. Part A:Policy and Practice.2003. Washington,D.C.,U.S.A.
[3]宁乐然.道路交通安全通论[M].2006,北京：中国人民公安大学出版社.164.
[4]崔新书.城市道路交通组织设计探讨[J].城市道桥与防洪,2004(6)：p.21-26.
[5]翟忠民.道路交通组织优化[M].2004,北京：人民交通出版社.
[6]Georgiana L. Hamza-Lup, Kien A. Hua, Rui Peng, Yao H. Ho. Real-Time Traffic Evacuation Management under Multiple Coordinated Human-Caused Threats [C].12th World Congress on ITS.2005. San Francisco.
[7]Hamza-Lup, Georgiana L. Exit-Point based Approaches for Real-Time Traffic Evacuation Management[C]. IEEE Intelligent Transportation Systems Conference. 2006 Sept. Toronto, Canada.
[8]胡红,刘小明,魏恒,杨孝宽.奥运应急疏散路网规划系统方法[J].北京工业大学学报,2008.34(8)：p.865-872.
[9]Thomas J. Cova, Justin P. Johnson. A network flow model for lane-based evacuation routing[C]. Transportation Research Part A:Policy and Practice,2003 (August).37(7): p.579-604.
[10]Chi Xie, Dung-Ying Lin, S. Travis Waller. A dynamic evacuation network optimization problem with lane reversal and crossing elimination strategies[J]. Transportation Research Part E,2010(46):p.295-316.
[11]Sangho Kim, Shashi Shekhar. Contraflow Network Reconfiguration for Evacuation Planning:A Summary of Results[C]. the 13th annual ACM international workshop on Geographic information systems.2005:ACM New York, NY, USA
[12]Sangho Kim, Shashi Shekhar, Manki Min. Contraflow Transportation Network Reconfiguration for Evacuation Route Planning[C]. IEEE Transactions On Knowledge And Data Engineering.2008.
[13]Campos V. B. G, Da Silvap. A. L., Nettop. O. B. Evacuation transportation planning: A method of identify optimal independent routes[C], International conference on urban transport No5.1999, WIT Press, Southampton, ROYAUME-UNI (2000) (Monographie):Rhodes, GRECE
[14]刘丽霞,杨骅飞.突发事件等复杂情形下的交通路径选择问题[J].北京联合大学学报(自然科学版),2004.18(3)：p.67-71.
[15]姚清林.优选地震救灾路径的图与模糊集算法[J].自然灾害学报,2006.15(2)：p.143-148.
[16]肖国清,温丽敏,陈宝智,王浩.毒气泄漏时的最佳疏散路径[J].东北大学学报(自然科学版),2001.22(6)：p.674-676.
[17]Qingsong Lu, Yan Huang, and Shashi Shekhar. Evacuation Planning:A Capacity Constrained Routing Approach[J]. Indian Standards Institution,2003:p.111-125.
[18]Qingsong Lu, Betsy George, and Shashi Shekhar. Capacity Constrained Routing Algorithms for Evacuation Planning:A Summary of Results[J]. SSTD:international symposium on spatial and temporal databases 2005.3633:p.291-307.
[19]Maimai Zeng, Cheng Wang. Evacuation Route Planning Algorithm:Longer Route Preferential[J]. ISNN,2009:p.1062-1071.
[20]Sangho Kim, Betsy George, Shashi Shekhar. Evacuation Route Planning:Scalable Heuristics[C]. Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems.2007.
[21]Rene Bustamante, Jeffrey M. R. Wolff. Capacity Constrained Route Planner (CCRP) with Node Edge Parameter Time-Series (NEPTS)[C].2007.
[22]Han, Lee D. Global Optimization of EmergencyEvacuation Assignments [J]. Interfaces, 2006 36(6):p.502-513.
[23]Chung-Yung Wang, Shou-Ren Hu. A Study on Emergency Evacuation and Resue Network Reconstruction for Natural Disasters with Multi-Class Users Travel Behavior Constraints [J]. Journal of the Eastern Asia Society for Transportation Studies, 2005.6:p.4269-4284.
[24]Ying Liu, Xiaorong Lai. Two-Level Integrated Optimization System for Planning of Emergency Evacuation[J]. Journal of Transportation Engineering,2006:p.800-807.
[25]Mohammad Saadatseresht, Ali Mansourian, Mohammad Taleai. Evacuation planning using multiobjective evolutionary optimization approach[J]. European Journal of Operational Research,2009.198:p.305-314.
[26]Chen, Ming. Traffic Signal Timing for Urban Evacuation[D], Department of Civil Engineering.2005, the University of Maryland.
[27]Liu, Ying. An Integrited Optimal Control System for Emergency Evacuation[D] Philosophy Department.2007, the University of Maryland.
[28]胡红,刘小明,魏恒,杨孝宽.基于路口和路段入口综合控制的应急疏散交通控制算法[J].交通运输工程学报,2009.9(3)：p.108-116.
[29]Timothy J.Hall. GPS-Based Traffic Control Preemption System[P].1996, Minnesota Mining and Manufacturing Company,St. Paul,Minn.:U.S.A.
[30]W.Bentrott, Donald. Emergency Vehicle Command and Control System for Traffic Signal Preemption[P].1999, Interlog,Inc.,Falls Church,Va.:U.S.A.
[31]Bachelder, Aaron D. Forwarding System for Long-range Preemption and Corridor Clearance for Emergency Response[P].2005:U.S.A. p.1-17.
[32]Xinkai Wu, Henry X. Liu, Douglas Gettman. Identification of oversaturated intersections using high-resolution traffic signal data[J]. Transportation Research Part C,2010.18:p.626-638.
[33]Papageorgiou, Markos. An Integrated Control Approach for Traffic Corridors[J]. Transportation Rearch,1995.3(1):p.19-30.
[34]K. Aboudolas, M. Papageorgiou, E. Kosmatopoulos. Store-and-forward based methods for the signal control problem in large-scale congested urban road networks[J]. Transportation Research Part C,2009.18:p.163-174.
[35]K. Aboudolas, M. Papageorgiou, A. Kouvelas, E. Kosmatopoulos. A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks[J]. Transportation Research Part C,2010.18:p.680-694.
[36]Lin Zhang, Honglong Li, Panos D. Prevedouros. Signal Control for Oversaturated Intersections Using Fuzzy Logic[C]. The 2005 Annual Meeting of the TRB.2005. Washington D.C,U.S.A.
[37]Edward B. Lieberman, Jinil Chang. Formulation of a Real-Time Control Policy for Oversaturated Arterials[C]. the Transportation Research Board's 79th Annual Meeting. 2000. Washington, D.C.
[38]Michele C. Weigle, Stephan Olariu. Intelligent Highway Infrastructure for Planned Evacuations[C]. IEEE International Performance, Computing, and Communications Conference,2007. IPCCC 2007..2007.
[39]J.C.Severson, V.Maier-Speredelozzi, J.H.Wang,C.E.Collyer. Enhancing the Preparedness and Response of the Rhode Island Transportation System in Natural or Human-caused Disasters[C]. Transportation Research. Washington,D.C.
[40]Rizvi, S.R. Olariu, S. Weigle, M.C. Rizvi, M.E.. A Novel Approach to Reduce Traffic Chaos in Emergency and Evacuation Scenarios[J]. Vehicular Technology Conference,2007. VTC-2007 Fall.2007 IEEE 66th.2007 Oct.
[41]吕能超,严新平,刘正林.突发事件交通网络VMS发布点优化[J].中国科技论文在线,2008.3(10)：p.716-719.
[42]于悦.重大灾害条件下应急交通诱导系统关键技术研究[D],交通学院.2010,吉林大学：长春.p.151-168.
[43]林赐云.突发灾害下应急交通保障决策支持系统关键技术研究[D],交通学院.2010,吉林大学：中国,长春.p.1-150.
[44]金美莲.城市突发公共事件下混合流疏散问题研究[D],工程物理系.2008,清华大学：北京.p.1-113.
[45]金美莲,陈涛,申世飞.城市混合流疏散中不同交通方式配比对疏散时间的影响[J].清华大学学报(自然科学版),2009.49(2)：p.179-182.
[46]M. Wardman, P. W. Bonsall, J. D. Shires. Driver Response to Variable Message Signs a Stated Preference Investigation[J]. Transportation Research,1997.5(6):p.389-405.
[47]Alena Erke, Fridulv Sagberg, Rolf Hagman. Effects of Route Guidance Variable Message Signs on Driver Behaviour[J]. Transportation Research Part F,2007(10):p. 447-457.
[48]Srinivas Peeta, Jorge L. Ramos, Raghubhushan Pasupathy. Content of Variable Message Signs and On-line Driver Behavior[C]. the 79th Annual Meeting of the Transportation Research Boad.2000. Washington D.C.
[49]Jun-Seok Oh, R.Jayakrishnan, Anthony Chen,Hai Yang. A Parametric Framework for Route Guidance in Advanced Traveler Information Systems with Endogenously Determined Driver Compliance[C]. Transportation Research.2000. Washington,D.C.
[50]Tsippy Lotan, Haris N. Koutsopoulos. Models for Route Choice Behavior in the Presence of Information using Concepts from Fuzzy Set Theory and Approximate Reasoning[J]. Transportation,1993(20):p.129-155.
[51]Srinivas Peeta, Jeong W. Yu. A Data-Consistent Fuzzy Approach for On-Line Driver Behavior under Information Provision[C].81th Annual Meeting of the Transportation Research Board.2002. Washington D.C.,U.S.A.
[52]Petros C. Vythoulkas, Haris N. Koutsopoulos. Modeling discrete choice behavior using concepts from fuzzy set theory, approximate reasoning and neural networks[J]. Transportation Research Part C,2003(11):p.51-73.
[53]Alexander Paz, Srinivas Peeta. Behavior-consistent Real-time Traffic Routing under Information Provision[J]. Transportation Research Part C,2009(17):p.642-661.
[54]Karthik K. Srinivasan, Hani S. Mahmassani. Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS:a dynamic kernel logit formulation[J]. Transportation Research Part B,2003(37):p.793-814.
[55]Jeong W.Yu, Srinivas Peeta. A Behavior-based Network Modeling Approach for Deployable Information Provision Strategies[C]. Transportation Research 2007. Washington,D.C.
[56]Richard H. M. Emmerink, Peter Nijkamp, Peet Rietveld, Josn. Van Ommeren. Variable Message Signs and Radio Traffic Information an Integrated Empirical Analysis of Drivers' Route Choice Behaviour[J]. Transpn. Research,1996.30(2):p.135-153.
[57]Asad J. Khattak, Xiaohong Pan, Billy Williams, Nagui Rouphail, Yingling Fan. Traveler Information Delivery Mechanisms Impacts on Consumer Behavior[C]. Transportation Research Board.2008. Washington, D.C., U.S.A.
[58]Bavelas, A. A Mathematical Model for Group Structures [J]. Human Organization, 1948(7):p.16-30.
[59]Bavelas, A. Communication Patterns in Task Oriented Groups [J]. Journal of the Acoustical Society of America,1950(22):p.271-282.
[60]Stefan Lammer, Bjorn Gehlsen, Dirk Helbing. Scaling Laws in the Spatial Structure of Urban Road Networks [J]. Physica A,2006.363(1):p.89-95.
[61]高自友,赵小梅,黄海军,毛保华.复杂网络理论与城市交通系统复杂性问题的相关研究[J].交通运输系统工程与信息,2006.6(3)：p.41-47.
[62]赵月,杜文,陈爽.复杂网络理论在城市交通网络分析中的应用[J].城市交通,2009.7(1)：p.57-65.
[63]吴彤.复杂网络研究及其意义[J].哲学研究,2004(8)：p.58-63.
[64]I. Vragovic, E. Louis, A. Diaz-Guilera. Efficiency of Informational Transfer in Regular and Complex Networks[J]. Physical Review E,2005.71(3):p.1-9.
[65]安世虎,都艺兵,曲吉林.节点集重要性测度[J].中国管理科学,2006.14(1)：p.106-111.
[66]赫南,李德毅,淦文燕,朱熙.复杂网络中重要性节点发掘综述[J].计算机科学, 2007.34(12)：p.1-5.
[67]王炜.城市交通规划理论与方法[M].1992,人民交通出版社：北京.32-33.
[68]姜桂艳,吴正言,常安德,张春勤.基于信息核度的应急疏散路网关键节点和路径确定方法[J].吉林大学学报(工学版),2010.40：p.163-168.
[69]Jiang Guiyan, Wu Zhengyan, Li Qiao,Zhang Chunqin. Key nodes and corridors identifying method for large-scale evacuation network based on information centrality[C].2010 2nd International Conference on Future Computer and Communication May,2010. Wuhan,China.
[70]Jiang, Bin. Street Hierarchies:A Minority of Streets Account for a Majority of Traffic Flow[J]. International Journal of Geographical Information 2009.23(8):p.1033-1048.
[71]Richard L. Church, Thomas J. Cova. Mapping evacuation risk on transportation networks using a spatial optimization model[J]. Transportation Research Part C, 2000(8):p.321-336.
[72]Henry X. Liu, Xiaozheng He, Xuegang (Jeff) Ban. A Cell-based Many-to-One Dynamic System Optimal Model and Its Heuristic Solution Method for Emergency Evacuation[C]. the 86th TRB Annual Meeting. July,2006. Washington D.C, USA.
[73]金元郁,顾兴源.改进的广义预测算法[J].信息与控制,1990(3)：p.8-14.
[74]庞中华,崔红.系统辨识与自适应控制MATLAB仿真.2009,北京：北京航空航天大学出版社.2、142.
[75]L. Fu, D. Sun, L.R. Rilett. Heuristic shortest path algorithms for transportation applications:State of the art[J]. Computers & Operations Research,2006.33:p. 3324-3343.
[76]B.Hoppe, E.Tardos. Polynomial Time Algorithms for Some Evacuation Problems. the 5th Annual ACM-SIAM Symposium on Discrete Algorithms.1994.
[77]张飞舟,范耀祖.交通控制工程[M].2005,中国铁道出版社：北京.219-226.
[78]师黎,陈铁军,李晓媛,姚利娜.智能控制理论及应用[M].2009,北京：清华大学出版社.382-390.
[79]Li Zushu, Wang Guiping. Schema Theory and Human Simulated Intelligent Control[C]. the 2003 IEEE International Conference on RoboticsJntelligent Systems and Signal Processing.2003. Changsha, China
[80]Zushu Li, Hua Zhang, Yongling Wen, Guiping Wang. Human Simulated Intelligent Control Based on Sensory-Motor Intelligent Schemas[C]. The 5th World Congress on Intelligent Control and Automation.2004. HangZhou,China.
[81]Nathan H. Gartner, Susan F. Assmann, Fernando Lasaga, Dennis L. Hou. A Multi-Band Approach to Arterial Traffic Signal Optimization [J]. Transportation Rearch, 1991.25B(1):p.55-74.
[82]Gazis, Denos C. Optimum Control of a System of Oversaturated Inter sections [J]. Operations Research,1964.12(6):p.815-831.
[83]Henry X. Liu, Xinkai Wu, Wenteng Ma, Heng Hu. Real-time Queue Length Estimation for Congested Signalized Intersections[J]. Transportation Research Part C, 2009.17:p.412-427.
[84]Hua Zhang, Zushu Li, Jiangong Gu, Guiqiang Chen, Yuanhong Dan. A General Parameters Optimization Method for Human-Simulated Intelligent Controller and its Application Research[C]. The 6th World Congress on Intelligent Control.2006. Dalian,China.
[85]Guiyan Jiang, Zhengyan Wu, Chunqin Zhang, Qiao Li. Real time traffic control framework based on simulated human intelligence for emergency evacuation[C].2010 8th World Congress on Intelligent Control and Automation July,2010. Jinan, China
[86]Yi-Chang Chiu, Pavan Korada, Pitu B. Mirchandani. Dynamic Traffic Management for Evacuation[C]. The 84th Annual Meeting of Transportation Research Board.2005. Washington,D.C. USA.
[87]Caspar G Chorus, Theo A. Arentze, Harry J.P. Timmermans. Traveler Compliance with Advice:A Bayesian Utilitarian Perspective[J]. Transportation Research Part E, 2009(45):p.486-500.
[88]Yu-Hsin Liu, Hani S. Mahmassani. Global maximum likelihood estimation procedure for multinomial probit (MNP) model parameters [J]. Transportation Research Part B, 2000(34):p.419-449.
[89]Dusan Teodorovic, Goran Pavkovic. The Fuzzy Set Theory Approach to the Vehicle Routing Problem When Demand at Nodes Is Uncertain[J]. Fuzzy Sets and Systems, 1996(82):p.307-317.
[90]Turan Arslan, C.Jotin Khisty. A Rational Approach to Handling Fuzzy Perceptions in Route Choice[J]. European Journal of Operational Research,2006(168):p.571-583.
[91]Alexander Paz, Srinivas Peeta. Information-based Network Control Strategies Consistent with Estimated Driver Behavior[J]. Transportation Research Part B, 2009(43):p.73-96.
[92]Zadeh, L. A. Fuzzy Sets[J]. Information and Control,1965.8(3):p.338-353.
[93]Zadeh, Lotfi A. Outline of a New Approach to the Analysis of Complex Systems and Decision Processes[C]. IEEE Transactions on Systems,Man,and Cybernetics.1973.
[94]Zadeh, L.A. The Concept of a Linguistic Variable and its Application to Approximate Reasoning[J]. Information Science,1975.8:p.199-249.
[95]A. Merve Acilar, Ahmet Arslan. Optimization of Multiple Input-Output Fuzzy Membership Using Clonal Selection Algorithm[J]. Expert Systems with Applications, 2011(38):p.1374-1381.
[96]David Peidro, Pandian Vasant. Transportation Planning with Modified S-curve Membership Functions Using an Interactive Fuzzy Multi-objective Approach[J]. Applied Soft Computing,2011(11):p.2656-2663.
[97]Hamza-Lup, Georgiana L. Exit-Point based Approaches for Real-Time Traffic Evacuation Management[C]. IEEE Intelligent Transportation Systems Conference. Sept.,2006. Toronto, Canada.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |