客运枢纽行人交通行为模型与仿真算法研究
|
摘要
随着社会经济的快速发展和城市化进程的加速,城市机动车拥有量不断增长,交通需求急剧增加,由此带来的交通拥堵、交通事故、环境污染和能源短缺等交通相关问题已成为世界各国共同面临的难题。鼓励公共交通,重视行人交通成为当前许多国家和地区改善交通环境、实现绿色交通的一项重要管理举措。作为城市客运交通系统网络节点的综合客运枢纽,是多种交通方式、不同方向客流集散、换乘的场所,通过其功能的发挥将各种交通方式有效的衔接配合。客运交通枢纽的服务水平、换乘效率直接影响着公共交通各方式对出行者的吸引力,同时也是提高城市客运交通系统便捷性、舒适性和安全性的关键环节之一。
行人流仿真是深入理解客运枢纽环境行人流复杂演化规律的有效方法,基于多智能体复杂系统建模技术建立客运枢纽行人流仿真模型以及算法,对于定量分析客运枢纽设施规划与设计方案,为提高客运枢纽服务水平及运行效率具有重要的理论价值和现实意义。现有行人流仿真模型多针对安全疏散问题而设计,且模型参数多采用国外研究标准,对于模拟复杂场景下交通活动类型多样的惯常行人流复杂演化过程,有待于从微观行人交通理论及行人智能体导航模型与算法方面完善和改进。
本文首先介绍了行人流模型及相关仿真技术的国内外研究现状,并基于现场获取的实验数据,对客运枢纽行人交通特性进行分析,包括客运枢纽行人交通行为特征分析,各类设施行人流的速度-密度-流量的回归关系,使用Lyapunov指数对短时行人交通流进行了混沌特征判定。分析结果表明客运枢纽相对于道路等环境的行人期望速度偏高,各类设施的短时行人流普遍具有混沌特性,仅扶梯除外。
基于交通工程学和认知心理学以及行人交通特性分析,总结了现有的行人行为理论和有关模型;根据已有的惯常三层次行人交通行为模型和建立的行人信息加工模型,完整提出了微观行人行为理论框架。在此框架下,分析了现有行为理论中欠缺的行人接受设施服务的活动过程,建立了服务活动模型;基于现场的实验数据对行人运动模型和扶梯与楼梯选择模型进行了标定工作。
通过对客运枢纽功能与结构的分析,基于认知心理学中的认知Schemata理论和客体Affordance理论,基于功能对客运枢纽设施进行了分类,建立了客运枢纽设施建模;定义了客运枢纽设施的特征点和Link,分析了Link的基本特点,基于实际案例建立了客运枢纽内的设施网络拓扑模型。
基于Montello导航理论、微观行人行为理论以及活动有限状态机模型,建立了客运枢纽环境下完整的行人Agent导航模型。给出了三个层次交通行为的具体算法;考虑行人在路径选择时视野受到障碍物或设施遮挡,使用当前路段行人流速度信息作为行人选择路径的主要考虑因素,提出了准静态路径选择算法。引入行人流惯常状态下行人之间的有限度竞争最优路径策略,提出了通道内的Agent动态路径目标节点更新算法,解决了通道弯道处由于Agent竞争最优路径目标节点可能导致的死锁现象问题;在行人流交织区域,由于流量较小方向上的行人流在仿真过程中容易被流量较大方向上的人群阻滞,通过研究人群子整体的判断方法,提出了行人Agent对人群障碍物的动态避障算法,解决了交织区域小流量方向上的行人Agent的死锁问题。
最后针对空间连续、时间离散的行人流仿真系统的输出数据,给出了仿真行人流特征参数的统计算法,研究了行人密度分布的动态显示算法;采用K-S检验方法,使用PSSITH行人流仿真系统对行人运动模型进行了有效性验证,在Netlogo多智能体仿真平台上,对标定的社会力模型和其它社会力模型进行了仿真结果误差的对比分析;采用T检验方法对行人换乘时间仿真结果误差进行了分析;使用Lyapunov指数分析了仿真短时行人流的混沌特性。最后使用PSSITH仿真系统,进行了实例的设施瓶颈分析和交通需求量加载分析。
本文的研究将丰富和拓展行人交通行为的理论框架和方法体系,提出的行人Agent导航模型及算法,可为客运枢纽等环境的行人流仿真系统提供核心算法与理论支持,从而更加深入地分析行人交通流复杂演化过程。
With the quick development of society economy and continuous acceleration of urbanization process, urban vehicle population continues to increase and urban traffic demands increase sharply. Many problems about traffic that includes traffic congestion, traffic accidents, environmental pollution and energy shortage have become a universal problem. Encourage public transport and pay attention to pedestrian traffic, has been the important management strategy to achieve green traffic in many countries and regions. As the complex urban transportation network node, passenger hub is the area of vary transport mode transforming and vary direction passenger flow assembling based on joining the vary transport mode and exerting its function fully. The service level and transfer efficiency of passenger hub affects the attractive power to the passengers directly, and at the same time it is the key joint to improve the convenience, amenity and safety of urban transportation system.
Constructing micro behavior models and related algorithms based on computer function and data simulation technology is one of the efficient approaches to understanding the complex evolution phenomenon of pedestrian traffic flow in passenger transfer hub. Most of current pedestrian flow simulation models and software are designed for the safety evacuation simulation and parameters of the models were always foreign standards. Its needed improved in the micro pedestrian traffic behavior theory and navigation model and algorithm of pedestrian agent for simulating the complex evolution process of pedestrian traffic flow in the complex passenger transfer hub in which the activities are varies.
Firstly, pedestrian traffic flow models and related simulation technology developing status were introduced in this paper, the pedestrian traffic characteristics were analyzed in the passenger transfer hub based on the field experimental data, including the pedestrian traffic behavior, fundamental diagram of pedestrian traffic flow of vary pedestrian facilities, chaos characteristics of short-term pedestrian traffic flows. The outputs showed that the pedestrian free speed in transfer hub is higher than other environments and most pedestrian facility traffic flow have chaos characteristics, except escalators.
Pedestrian behavior theory and models were analyzed based on traffic engineering and cognitive psychology theories. Pedestrian activity for receiving service of facility was analyzed, which was absent in current pedestrian behavior theory, and service activity model was constructed. A micro pedestrian behavior theory framed was proposed completely, pedestrian dynamic model and choice model of stairs and escalators were calibrated based on field data.
Based on Schemata terminology and the object affordance theory in cognitive psychology field, the constructing and function of passenger transfer hub were analyzed, passenger transfer hub facilities were classified and abstract by the function and construction analyzing. The facility topology network model was constructed based on the link and trait point definition.
A complete agent navigation model was constructed based on Montello navigation theory, micro pedestrian traffic behavior and finite state machine model, and algorithms of behavior model in different level were proposed. Considering the limited vision of pedestrian agent,the velocity of current path as the main factor to route choice behavior, a quasi-static route choice model was proposed in this paper. Cooperation strategy of agents in the normal pedestrian traffic flow was concerned, a dynamic target updating algorithm for agent in the passageway was proposed for resolving the deadlock of pedestrian agent at the curve conduit. By the proposed crowd holonic search method in this paper, dynamic crowd obstacles avoidance algorithm for pedestrian agent who in the little value pedestrian flow direction in the traffic interweave areas was proposed for resolve the deadlocks appearance in the interweave areas.
Finally, considering the contiguous space and discrete time data of simulation system output, statistic method of pedestrian traffic flow parameters and density dynamic showing algorithm were proposed. The pedestrian social force model was validated based on field data and output data of PSSITH simulation system by using the Kolmogorove-Smirnove test method. The social force model which calibrated in this paper was compared with the related research data by analyzing the mean standard error of simulation and real data based on netlogo multi-agent simulation platform. The transfer time mean value of pedestrian in passenger transfer hub was analyzed by using student’s test method based on the real data which were collected from field. The chaotic characteristic of simulation short term pedestrian traffic volume was analyzing by using Lyapunov exponent index. Two real case were analyzed based on the PSSITH, bottlenecks analyze and pedestrian traffic demand loading methods were proposed in the case.
This paper will enrich the pedestrian traffic behavior theory and improve the methodology, the navigation model and algorithm proposed in this paper can offer the key algorithm and theoretical foundation, so that researchers can analyze complex evolution process of pedestrian traffic flow more specifically.
行人流仿真是深入理解客运枢纽环境行人流复杂演化规律的有效方法,基于多智能体复杂系统建模技术建立客运枢纽行人流仿真模型以及算法,对于定量分析客运枢纽设施规划与设计方案,为提高客运枢纽服务水平及运行效率具有重要的理论价值和现实意义。现有行人流仿真模型多针对安全疏散问题而设计,且模型参数多采用国外研究标准,对于模拟复杂场景下交通活动类型多样的惯常行人流复杂演化过程,有待于从微观行人交通理论及行人智能体导航模型与算法方面完善和改进。
本文首先介绍了行人流模型及相关仿真技术的国内外研究现状,并基于现场获取的实验数据,对客运枢纽行人交通特性进行分析,包括客运枢纽行人交通行为特征分析,各类设施行人流的速度-密度-流量的回归关系,使用Lyapunov指数对短时行人交通流进行了混沌特征判定。分析结果表明客运枢纽相对于道路等环境的行人期望速度偏高,各类设施的短时行人流普遍具有混沌特性,仅扶梯除外。
基于交通工程学和认知心理学以及行人交通特性分析,总结了现有的行人行为理论和有关模型;根据已有的惯常三层次行人交通行为模型和建立的行人信息加工模型,完整提出了微观行人行为理论框架。在此框架下,分析了现有行为理论中欠缺的行人接受设施服务的活动过程,建立了服务活动模型;基于现场的实验数据对行人运动模型和扶梯与楼梯选择模型进行了标定工作。
通过对客运枢纽功能与结构的分析,基于认知心理学中的认知Schemata理论和客体Affordance理论,基于功能对客运枢纽设施进行了分类,建立了客运枢纽设施建模;定义了客运枢纽设施的特征点和Link,分析了Link的基本特点,基于实际案例建立了客运枢纽内的设施网络拓扑模型。
基于Montello导航理论、微观行人行为理论以及活动有限状态机模型,建立了客运枢纽环境下完整的行人Agent导航模型。给出了三个层次交通行为的具体算法;考虑行人在路径选择时视野受到障碍物或设施遮挡,使用当前路段行人流速度信息作为行人选择路径的主要考虑因素,提出了准静态路径选择算法。引入行人流惯常状态下行人之间的有限度竞争最优路径策略,提出了通道内的Agent动态路径目标节点更新算法,解决了通道弯道处由于Agent竞争最优路径目标节点可能导致的死锁现象问题;在行人流交织区域,由于流量较小方向上的行人流在仿真过程中容易被流量较大方向上的人群阻滞,通过研究人群子整体的判断方法,提出了行人Agent对人群障碍物的动态避障算法,解决了交织区域小流量方向上的行人Agent的死锁问题。
最后针对空间连续、时间离散的行人流仿真系统的输出数据,给出了仿真行人流特征参数的统计算法,研究了行人密度分布的动态显示算法;采用K-S检验方法,使用PSSITH行人流仿真系统对行人运动模型进行了有效性验证,在Netlogo多智能体仿真平台上,对标定的社会力模型和其它社会力模型进行了仿真结果误差的对比分析;采用T检验方法对行人换乘时间仿真结果误差进行了分析;使用Lyapunov指数分析了仿真短时行人流的混沌特性。最后使用PSSITH仿真系统,进行了实例的设施瓶颈分析和交通需求量加载分析。
本文的研究将丰富和拓展行人交通行为的理论框架和方法体系,提出的行人Agent导航模型及算法,可为客运枢纽等环境的行人流仿真系统提供核心算法与理论支持,从而更加深入地分析行人交通流复杂演化过程。
With the quick development of society economy and continuous acceleration of urbanization process, urban vehicle population continues to increase and urban traffic demands increase sharply. Many problems about traffic that includes traffic congestion, traffic accidents, environmental pollution and energy shortage have become a universal problem. Encourage public transport and pay attention to pedestrian traffic, has been the important management strategy to achieve green traffic in many countries and regions. As the complex urban transportation network node, passenger hub is the area of vary transport mode transforming and vary direction passenger flow assembling based on joining the vary transport mode and exerting its function fully. The service level and transfer efficiency of passenger hub affects the attractive power to the passengers directly, and at the same time it is the key joint to improve the convenience, amenity and safety of urban transportation system.
Constructing micro behavior models and related algorithms based on computer function and data simulation technology is one of the efficient approaches to understanding the complex evolution phenomenon of pedestrian traffic flow in passenger transfer hub. Most of current pedestrian flow simulation models and software are designed for the safety evacuation simulation and parameters of the models were always foreign standards. Its needed improved in the micro pedestrian traffic behavior theory and navigation model and algorithm of pedestrian agent for simulating the complex evolution process of pedestrian traffic flow in the complex passenger transfer hub in which the activities are varies.
Firstly, pedestrian traffic flow models and related simulation technology developing status were introduced in this paper, the pedestrian traffic characteristics were analyzed in the passenger transfer hub based on the field experimental data, including the pedestrian traffic behavior, fundamental diagram of pedestrian traffic flow of vary pedestrian facilities, chaos characteristics of short-term pedestrian traffic flows. The outputs showed that the pedestrian free speed in transfer hub is higher than other environments and most pedestrian facility traffic flow have chaos characteristics, except escalators.
Pedestrian behavior theory and models were analyzed based on traffic engineering and cognitive psychology theories. Pedestrian activity for receiving service of facility was analyzed, which was absent in current pedestrian behavior theory, and service activity model was constructed. A micro pedestrian behavior theory framed was proposed completely, pedestrian dynamic model and choice model of stairs and escalators were calibrated based on field data.
Based on Schemata terminology and the object affordance theory in cognitive psychology field, the constructing and function of passenger transfer hub were analyzed, passenger transfer hub facilities were classified and abstract by the function and construction analyzing. The facility topology network model was constructed based on the link and trait point definition.
A complete agent navigation model was constructed based on Montello navigation theory, micro pedestrian traffic behavior and finite state machine model, and algorithms of behavior model in different level were proposed. Considering the limited vision of pedestrian agent,the velocity of current path as the main factor to route choice behavior, a quasi-static route choice model was proposed in this paper. Cooperation strategy of agents in the normal pedestrian traffic flow was concerned, a dynamic target updating algorithm for agent in the passageway was proposed for resolving the deadlock of pedestrian agent at the curve conduit. By the proposed crowd holonic search method in this paper, dynamic crowd obstacles avoidance algorithm for pedestrian agent who in the little value pedestrian flow direction in the traffic interweave areas was proposed for resolve the deadlocks appearance in the interweave areas.
Finally, considering the contiguous space and discrete time data of simulation system output, statistic method of pedestrian traffic flow parameters and density dynamic showing algorithm were proposed. The pedestrian social force model was validated based on field data and output data of PSSITH simulation system by using the Kolmogorove-Smirnove test method. The social force model which calibrated in this paper was compared with the related research data by analyzing the mean standard error of simulation and real data based on netlogo multi-agent simulation platform. The transfer time mean value of pedestrian in passenger transfer hub was analyzed by using student’s test method based on the real data which were collected from field. The chaotic characteristic of simulation short term pedestrian traffic volume was analyzing by using Lyapunov exponent index. Two real case were analyzed based on the PSSITH, bottlenecks analyze and pedestrian traffic demand loading methods were proposed in the case.
This paper will enrich the pedestrian traffic behavior theory and improve the methodology, the navigation model and algorithm proposed in this paper can offer the key algorithm and theoretical foundation, so that researchers can analyze complex evolution process of pedestrian traffic flow more specifically.
引文
[1]Daamen. W, Hoogendoorn. SP. Free speed distributions for pedestrian traffic[C], PrePrints (CD-ROM) 85th Annual Meeting Transportation Research Board, 2006: 1-13.
[2]Bowman, B.L., R.L. Vecellio. Pedestrian walking speeds and conflicts at urban median locations[C], Transportation Research Record 1438, 1984: 67- 73.
[3]Wilson, D.G., G.B. Grayson, Age-Related Differences in the Road Crossing Behavior of Adult Pedestrians[R], Transport and Road Reasearch Laboratory, Crowthorne Berkshire England, 1980.
[4]Daamen, W. Modelling Passenger flows in Public Transport Facilities. PhD thesis[D]. Delft University Press, 2004.
[5]Pauls, J. Calculating evacuation times for tall buildings[J]. Fire Safety Journal 1987, 12: 213-236.
[6]Fruin, J.J. Design for pedestrians: A level-of-service concept[C], Highway Research Record 355, 1971: 1-15.
[7]Fruin, J.J. Pedestrian Planning and Desig[M], Metropolitan association of urban designers and environmental planners, New York, 1971.
[8] Hunte-Zaworski. K. Transit Capacity and Quality of Service Manual. (2nd Edition)[M]. Transportation Research Board, National Academy Press, Washington D.C., 2004.
[9]Lovas, G.C. Modeling and simulation of pedestrian traffic flow[J], Transportation Research Part B, 1994, 28: 429-443.
[10]The statistics of crowd fluids[J]. Nature, 229, 1971, page: 381-383
[11]Helbing D. A fluid dynamic model for the movement of pedestrians[J]. Complex Systems ,1992, 6: 391-415.
[12] Roger L. Hughes. A continuum theory for the flow of pedestrians. Transportation Research Part B, 2002. 36: 507-535.
[13]Okazaki, S. A Study of Pedestrian Movement in Architectural Space, Part 1: Pedestrian Movement by the Application on of Magnetic Models[C]. Trans. of A.I.J., No.283. 1979: 111-119.
[14]Helbing, D., P. Molnar. Social force model for pedestrian dynamics[J]. Physical Review E51(5), 1995: 4282-4286.
[15]Blue, V.J. & J.L. Adler. Emergent fundamental pedestrian flows from cellular automata micro-simulation[C]. Transportation Research Record 1644, 1998, page: 29-36.
[16]Schadschneider A. Cellular automaton approach to pedestrian dynamics theory[M]. In: M. Schreckenberg, S. Sharma, Pedestrian and Evacuation Dynamics, Springer, Berlin, 2005: 75-85.
[17]Antonini, G., Bierlaire, M. , Weber, M. Discrete choice models of pedestrian walking behavior[J]. Transportation Research Part B: Methodological, 2006, 40: 667-687.
[18]Hirtle, S. C. and J. Hudson. Acquisition of Spatial Knowledge for Routes[J]. Journal of Environmental Psychology, 1991,11: 335-345.
[19]Hunt, E., Waller, D. Orientation and wayfinding: A review[R]. ONR Technical Report N00014-96-0380. 1999.
[20]M. Raubal. Human wayfinding in unfamiliar buildings: a simulation with a cognizing agent[J]. Cognitive Processing 23, 2001: 363-388.
[21]Hoogendoorn, S.P. and P.H.L. Bovy. Pedestrian Route-Choice and Activity Scheduling Theory and Models[J]. Transportation Research Part B, 2004, 38: 169-190.
[22]Daamen. W, Bovy. PHL, Hoogendoorn. SP, Van de Reijt. Passenger Route Choice concerning Level Changes in Railway Stations[C]. In Transportation Research Board Annual Meeting, 2005.
[23]Kitamura, R., C. Chen, R. Pendyala & R. Narayanan. Micro-simulation of daily activity-travel patterns for travel demand forecasting[J]. Transportation, 2000, (27):25-51.
[24]Stretz, P. TRANSIMS research highlights: Portland activity generation[C]. 80th Annual Transportation Research Board Meeting, January 2001, Washington, DC.
[25]Theo A. Arentze, Harry J. P. Timmermans. Measuring the goodness-of-fit of decision-tree models of discrete and continuous activity-travel choice: methods and empirical illustration[J]. Journal of Geographical Systems, 2003, 5(2): 185-206.
[26]Daniel Thalmann, Soraia Raupp Musse. Crowd Simulation[M].Springer-Verlag London Limited, 2007:119-122.
[27]Michael J. Wooldridge, Nicholas R. Jennings. Agent theories, architectures, and languages: A survey[M]. Intelligent Agents. Springer Berlin, Heidelberg, volume 890, 1995: 1-39.
[28]王红卫.建模与仿真[M].科学技术出版社,2002.3: 195-196.
[29]Carlos A. Iglesias, Mercedes Garijo and Jos′e C. Gonz′alez. A survey of agent-oriented methodologies. Intelligent agent, Agent Theories[M], Architectures, and Languages. Springer, 1998: 330-343.
[30]Bernard Moulin, Mario Brassard. A scenario-based design method and an environment for the development of multiagent systems[C]. In D. Lukose and C. Zhang, editors, First Australian Workshop on Distributed Artificial Intelligentce, (LNAI volumen 1087). Springer-Verlag: Heidelberg, Germany, 1996: 216–231.
[31]Charles M. Macal, Michael J. North. Tutorial on Agent-Based Modeling and Simulation[C]. Proceeding of the 2005 Winter Simulation Conference, 2005.
[32]Nicole Amy Ronald. Agent-based Approaches to Pedestrian Modelling[D]. Master thesis, University of Melbourne, 2007.
[33]X. Pan, C. S. Han, K. Dauber, K. H. Law. A Multi-agent Based Framework for Simulating Human and Social Behaviors during Emergency Evacuations[C] Social Intelligence Design, Stanford University, Stanford, 2005: 24-26.
[34]Still, G. Keith. Crowd Dynamics[D]. Mathematics Department, Warwick University. 2000.
[35]Mankyu Sunge, Michael Gleicher, Stephen Chenney. Scalable behaviors for crowd simulation[J]. Eurograhics, 2004, 23(3).
[36]Isaac Rudom?′n, Erik Milla′n, Benjam?′n Herna′ndez. Fragment shaders for agent animation using finite state machines[J]. Simulation Modelling Practice and Theory, 2005(13): 741–751.
[37]Kisko T. M., Francis R. L., Nobel C. R.: EVACNET4 User’s Guide[M]. University of Florida, 1998.
[38]Cheung, C.Y. and Lam, W.H.K. Pedestrian route choices between escalator and stairway in MTR stations[J]. ASCE Journal of Transportation Engineering, 1998(124): 277-285.
[39]Hai-Jun Huang,William H.K. LAM,A Stochastic Model for Combined Activity Destination Route Choice Problems[C]. Annals of Operations Research, 2005: 111-125.
[40]张培红,陈宝智.火灾时人员疏散的行为规律[J];东北大学学报(自然科学版); 2001, 22(1): 54-57.
[41]Yang Li zhong, Fang Weifeng, Li Jian, Huang Rui, Fan Weicheng. Cellular automata pedestrian movement model considering human behavior[J]. Chinese Science Bulletin, 2003, 48(16):1695-1699
[42]宋卫国,于彦飞,陈涛.出口条件对人员疏散的影响及其分析[J].火灾科学, 2003,12(2): 100-104.
[43]曲昭伟,周立军,王殿海.城市信号交叉口自行车及行人到达与释放规律[J].公路交通科技,2004,21(8): 93-94.
[44]陈然,董力耘.中国大都市行人交通特征的实测和初步分析[J].上海大学学报,2005,11(1):93-97.
[45]裴玉龙,冯树民.城市行人过街速度研究[J].公路交通科技,2006,23 (9):104-107.
[46]陈鹏.基于多智能主体的人群流动形态动态模拟研究[D].同济大学,2006.
[47]李得伟;城市轨道交通枢纽乘客集散模型及微观仿真理论[D].北京交通大学,2007.
[48]赵光华,行人仿真在奥运地铁站的应用研究[D].北京工业大学,2007.
[49]史建港,大型活动行人交通特性研究[D].北京工业大学,2007.
[50]于彦飞,人员疏散的多作用力元胞自动机模型研究[D].中国科学技术大学,2008.
[51]交通工程学[M].李作敏,人民交通出版社, 2000.
[52]交通工程基础[M]. Homburger著,任福田译,中国建筑工业出版社,1990.
[53]傅丹,冯卫东,于起峰,徐一丹,等.一种摄像机自标定的线性方法[J].光电工程. Vol(35), 2008,1:71-75.
[54]机器视觉[M].张广军,科学出版社,2005: 24-27.
[55]Ganem, J. (1998). A behavioral demonstration of Fermat's principle[J]. The Physics Teacher, 1998, 36: 76-78.
[56]Helbing D, Traffic and related self-driven many-particle systems[J]. Reviewsof Modern Physics 73, Issue 4, 2001: 1067-1141.
[57]Helbing, Dirk, Farkas, Illes J. and Vicsek, Tamas, Simulating Dynamical Features of Escape Panic[J]. Nature, Vol. 407,2000: 487-490.
[58]任福田,刘小明,荣建等编著.交通工程学[M].北京:人民交通出版社, 2003.
[59]混沌时间序列分析及其应用[M].吕金虎,陆君安,陈士华编著.武汉大学出版社, 2002: 28-80.
[60]A. Wolf. J.B. Swift, H. L. Swinney and J. A. Vastano. Determining Lyapunov exponents from a time series[J]. Physica 16D, 1985: 285-317.
[61]Kennel M.B., Brown R., Abarbanel H.D.I. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Physical Review A, 1992, 45(6): 3403-3411.
[62]卢宇法,陈宇红,贺国光.应用改进型小数据量法计算交通流的最大Lyapunov指数[J].系统工程理论与实践, 2007, 1(1): 86-90.
[63]Hoogendoorn SP, Bovy PHL. Normative pedestrian behaviour theory and modelling[M] In Michael A.P. Taylor (Ed.), Transportation and traffic theory in the 21st century. Oxford: Pergamon, Elsevier science, 2002: 219-245.
[64]Hoogendoorn SP, Bovy PHL. Pedestrian route-choice and activity scheduling theory and models[J]. Transportation Research Part B, 2004, 38 (2): 169-190.
[65]工程心理学与人的作业[M].C.D. Wichens,J. Hollands著,朱祖祥等译,华东师范大学出版社,2003: 354-402.
[66]心理学与生活[M]. R. J. Gerrig,P. G. Zimbardo著,王垒等译,人民邮电出版社,2003:3-10.
[67]Johnson. M. The body in the mind. The bodily basis of meaning, imagination and reason[M]. The University of Chicago Press, 1987.
[68]Martin Raubal, M Worboys. A Formal Model of the Process of Wayfinding in Built Environments[C]. Proceeding of the International Conference on Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science, 1999: 381-399.
[69]Ujjal Chattaraj, Armin Seyfried, Partha Chakroborty. Comparison of Pedestrian Fundamental Diagram Across Cultures[J]. Advances in complex systems, 2009, 12(3):393-405.
[70]Hoogendoorn, S. P., W. Daamen , Microscopic calibration and validation ofpedestrian models:Cross-comparison of models using experimental data[M]. A. Schadschneider et. al. (eds.) Proceedings of Traffic and Granular Flow '05, Springer, Berlin, 2005: 329-340..
[71]A. Steiner, M. Philipp, A. Schmid. Parameter estimation for a pedestrian simulation model[C]. STRC: 7th Swiss Transport Research Conference, Monte VeritàAscona, 2007, 9: 1-29.
[72]A. Johansson, D. Helbing, P.K. Shukla. Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data[J]. Advances in Complex Systems, 2007, 10(4): 271–288.
[73]Marko Apel. Simulation of pedestrian flows based on the social force model using the Verlet Link Cell algorithm[D]. Poznan University of Technology, 2004.
[74]Lakoba, T.I., Kaup, D.J., Finkelstein, N.M. Modifications of the Helbing Molnár Farkas Vicsek social force model for pedestrian evolution[J]. Simulation, 2005(81): 339-352.
[75]J.J. Gibson. The theory of affordances[J]. In R. Shaw and J. Bransford, editors, Perceiving, Acting and Knowing, Wiley, New York, 1977: 67-82.
[76]Montello, D.R. Navigation[M]. In P. Shah & A. Miyake (Eds.), The Cambridge handbook of visuospatial thinking[M]. Cambridge University Press, 2005: 257-294.
[77]R. Nicole, C. W. Reynolds. Steering behavior for autonomous characters[C]. Proceedings of the Game Developers Conference, 1999: 763-782.
[78]W. Shao, D. Terzopoulos. Autonomous pedestrians[J]. Graphical Models, 2007(69): 246-274.
[79]S. Musse, D. Thalmann, Hierarchical model for real time simualtion of virtual human crowds[J]. IEEE Transactions on Visualization and Computer Graphics 2001, 7(2): 152-164.
[80]J. Gunge. AI for games and animation: a cognitive modeling approach[M]. AAI for Games and Animation. Ak Peters Natick Press, Mssachusetts, 1999: 29-44.
[81]T. F. Evers, S. R. Musse. Building Artificial Memory of Autonomous Agents Using Dynamical and Hierarchical Finite State Machine[J]. Computer Animation, Geneva, Switzerland, 2002, 6: 164-169.
[82]虚拟现实技术[M]. G.C. Burdea, P. Coiffet著,魏迎梅等译.电子工业出版社, 2005: 143-146.
[83]柳伍生,余朝玮.地铁站楼梯行人流交通特征的数据拟合分析[J].计算机工程与应用, 2008, 44(03): 50-52.
[84]杨晓光,滕靖等译.美国公共交通通行能力和服务质量手册[M].北京:中国建筑工业出版社,2010.
[85]http://ccl.northwestern.edu/netlogo/4.0.4/docs/NetLogo_manual_chinese.pdf
[86]任福田,刘小明,荣建等编著.交通工程学[M].北京:人民交通出版社.2003.
[87]杜先睿.基于多智能体仿真的交通疏散问题研究[D].哈尔滨工业大学硕士学位论文,2006.
[88]方伟峰,杨立中等.基于元胞自动机的多自主体人员行为模型及其在性能化设计中的应用[J].中国工程科学,2003,5(3):67-71.
[89]葛亮.城市综合客运换乘枢纽规划与设计方法研究[D].东南大学博士论文,2005.
[90]胡清梅,方卫宁,邓野.一种基于社会力的行人运动模型研究[J].系统仿真学报,2009,21(4):977-980.
[91]何民.混合交通流微观仿真关键技术研究[D].北京工业大学,2003.
[92]廖守亿.复杂系统基于的建模与仿真方法研究及应用.国防科技大学博士论文,2005.
[93] William H. K. Lam, Hung Hom and Chung-yu Cheung. Pedestrian Speed/Flow Relationships for Walking Facilities in Hong Kong[J]. Journal of Transportation Engineering, 2000, 126(4):343-349.
[94]Daamen, W., Hoogendoorn. SP. Controlled experiments to derive walking behavior[J]. Transport Infrastructure Res. 2003(1) 39-59.
[95]Daamen. W, Hoogendoorn. SP. Pedestrian traffic flow operations on a platform: observations and comparison with simulation tool SimPed[C]. In J Allan, C Brebbia & R.J. Hill (Eds.), Computers in Railways IX. Southampton: WIT Press. (TUD). 2004: 125-134.
[96] Stella Fiorenzo-Catalano, Sascha Hoogendoorn-Lanser, Rob van Nes. Choice set composition modelling in multimodal travelling[C]. 10th International Conference on Travel Behaviour Research Lucerne, 2003: 10-15.
[97]Hoogendoorn, SP, Daamen, W. Design assessment of Lisbon transfer stations using microscopic pedestrian simulation[C]. In J Allan, C Brebbia, R.J. Hill (Eds.), Computers in Railways IX, 2004: 135-147.
[98]Pettre Julien, Grillon Helena, Thalmann Daniel. Crowds of moving objects: Navigation planning and simulation[C]. IEEE International Conference on Robotics and Automation,2007: 3062-3067
[99]Daniel Harney. Pedestrian modelling: current methods and future directions[J]. Road &Transport Research, 11(4):2–12, 2002.
[100]SP. Hoogendoorn and W. Daamen. Microscopic Parameter Identification of Pedestrian Models and its Implications to Pedestrian Flow Modeling[C]. Transportation Research Board Annual Meeting 2006.
[101]SP. Hoogendoorn and P. Bovy. Simulation of pedestrian flows by optimal control and differential games[J]. Optimal control applications and methods 24, 2003: 153-172.
[102]Thorsten Schelhorn, David O’Sullivan et al. STREETS: an agent-based pedestrian model [R]. Working paper series of Center For Advanced Spatial Analysis. Paper 9, 1999.
[103]Teknomo, K., Y. Takeyama, H. Inamura. Determination of pedestrian flow performance based on video tracking and microscopic simulations[C]. Proc. Infrastructure Planning Conf. Ashikaga, Japan. 2000, 23(1) 639–642.
[104]Teknomo, K., Takeyama, Y., & Inamura, H. Microscopic pedestrian simulation model to evaluate‘‘Lane-like segregation’’of pedestrian crossing[C]. In Proceedings of infrastructure planning conference. Kouchi, Japan. 2001(24)
[105]Takakuwa S. and Oyama, T. Simulation analysis of international departure passenger flows in an airport terminal [C]. Pro. of the 2003 Winter Simulation Conference, 2003 United States: IEEE, 2003:1627-1634.
[2]Bowman, B.L., R.L. Vecellio. Pedestrian walking speeds and conflicts at urban median locations[C], Transportation Research Record 1438, 1984: 67- 73.
[3]Wilson, D.G., G.B. Grayson, Age-Related Differences in the Road Crossing Behavior of Adult Pedestrians[R], Transport and Road Reasearch Laboratory, Crowthorne Berkshire England, 1980.
[4]Daamen, W. Modelling Passenger flows in Public Transport Facilities. PhD thesis[D]. Delft University Press, 2004.
[5]Pauls, J. Calculating evacuation times for tall buildings[J]. Fire Safety Journal 1987, 12: 213-236.
[6]Fruin, J.J. Design for pedestrians: A level-of-service concept[C], Highway Research Record 355, 1971: 1-15.
[7]Fruin, J.J. Pedestrian Planning and Desig[M], Metropolitan association of urban designers and environmental planners, New York, 1971.
[8] Hunte-Zaworski. K. Transit Capacity and Quality of Service Manual. (2nd Edition)[M]. Transportation Research Board, National Academy Press, Washington D.C., 2004.
[9]Lovas, G.C. Modeling and simulation of pedestrian traffic flow[J], Transportation Research Part B, 1994, 28: 429-443.
[10]The statistics of crowd fluids[J]. Nature, 229, 1971, page: 381-383
[11]Helbing D. A fluid dynamic model for the movement of pedestrians[J]. Complex Systems ,1992, 6: 391-415.
[12] Roger L. Hughes. A continuum theory for the flow of pedestrians. Transportation Research Part B, 2002. 36: 507-535.
[13]Okazaki, S. A Study of Pedestrian Movement in Architectural Space, Part 1: Pedestrian Movement by the Application on of Magnetic Models[C]. Trans. of A.I.J., No.283. 1979: 111-119.
[14]Helbing, D., P. Molnar. Social force model for pedestrian dynamics[J]. Physical Review E51(5), 1995: 4282-4286.
[15]Blue, V.J. & J.L. Adler. Emergent fundamental pedestrian flows from cellular automata micro-simulation[C]. Transportation Research Record 1644, 1998, page: 29-36.
[16]Schadschneider A. Cellular automaton approach to pedestrian dynamics theory[M]. In: M. Schreckenberg, S. Sharma, Pedestrian and Evacuation Dynamics, Springer, Berlin, 2005: 75-85.
[17]Antonini, G., Bierlaire, M. , Weber, M. Discrete choice models of pedestrian walking behavior[J]. Transportation Research Part B: Methodological, 2006, 40: 667-687.
[18]Hirtle, S. C. and J. Hudson. Acquisition of Spatial Knowledge for Routes[J]. Journal of Environmental Psychology, 1991,11: 335-345.
[19]Hunt, E., Waller, D. Orientation and wayfinding: A review[R]. ONR Technical Report N00014-96-0380. 1999.
[20]M. Raubal. Human wayfinding in unfamiliar buildings: a simulation with a cognizing agent[J]. Cognitive Processing 23, 2001: 363-388.
[21]Hoogendoorn, S.P. and P.H.L. Bovy. Pedestrian Route-Choice and Activity Scheduling Theory and Models[J]. Transportation Research Part B, 2004, 38: 169-190.
[22]Daamen. W, Bovy. PHL, Hoogendoorn. SP, Van de Reijt. Passenger Route Choice concerning Level Changes in Railway Stations[C]. In Transportation Research Board Annual Meeting, 2005.
[23]Kitamura, R., C. Chen, R. Pendyala & R. Narayanan. Micro-simulation of daily activity-travel patterns for travel demand forecasting[J]. Transportation, 2000, (27):25-51.
[24]Stretz, P. TRANSIMS research highlights: Portland activity generation[C]. 80th Annual Transportation Research Board Meeting, January 2001, Washington, DC.
[25]Theo A. Arentze, Harry J. P. Timmermans. Measuring the goodness-of-fit of decision-tree models of discrete and continuous activity-travel choice: methods and empirical illustration[J]. Journal of Geographical Systems, 2003, 5(2): 185-206.
[26]Daniel Thalmann, Soraia Raupp Musse. Crowd Simulation[M].Springer-Verlag London Limited, 2007:119-122.
[27]Michael J. Wooldridge, Nicholas R. Jennings. Agent theories, architectures, and languages: A survey[M]. Intelligent Agents. Springer Berlin, Heidelberg, volume 890, 1995: 1-39.
[28]王红卫.建模与仿真[M].科学技术出版社,2002.3: 195-196.
[29]Carlos A. Iglesias, Mercedes Garijo and Jos′e C. Gonz′alez. A survey of agent-oriented methodologies. Intelligent agent, Agent Theories[M], Architectures, and Languages. Springer, 1998: 330-343.
[30]Bernard Moulin, Mario Brassard. A scenario-based design method and an environment for the development of multiagent systems[C]. In D. Lukose and C. Zhang, editors, First Australian Workshop on Distributed Artificial Intelligentce, (LNAI volumen 1087). Springer-Verlag: Heidelberg, Germany, 1996: 216–231.
[31]Charles M. Macal, Michael J. North. Tutorial on Agent-Based Modeling and Simulation[C]. Proceeding of the 2005 Winter Simulation Conference, 2005.
[32]Nicole Amy Ronald. Agent-based Approaches to Pedestrian Modelling[D]. Master thesis, University of Melbourne, 2007.
[33]X. Pan, C. S. Han, K. Dauber, K. H. Law. A Multi-agent Based Framework for Simulating Human and Social Behaviors during Emergency Evacuations[C] Social Intelligence Design, Stanford University, Stanford, 2005: 24-26.
[34]Still, G. Keith. Crowd Dynamics[D]. Mathematics Department, Warwick University. 2000.
[35]Mankyu Sunge, Michael Gleicher, Stephen Chenney. Scalable behaviors for crowd simulation[J]. Eurograhics, 2004, 23(3).
[36]Isaac Rudom?′n, Erik Milla′n, Benjam?′n Herna′ndez. Fragment shaders for agent animation using finite state machines[J]. Simulation Modelling Practice and Theory, 2005(13): 741–751.
[37]Kisko T. M., Francis R. L., Nobel C. R.: EVACNET4 User’s Guide[M]. University of Florida, 1998.
[38]Cheung, C.Y. and Lam, W.H.K. Pedestrian route choices between escalator and stairway in MTR stations[J]. ASCE Journal of Transportation Engineering, 1998(124): 277-285.
[39]Hai-Jun Huang,William H.K. LAM,A Stochastic Model for Combined Activity Destination Route Choice Problems[C]. Annals of Operations Research, 2005: 111-125.
[40]张培红,陈宝智.火灾时人员疏散的行为规律[J];东北大学学报(自然科学版); 2001, 22(1): 54-57.
[41]Yang Li zhong, Fang Weifeng, Li Jian, Huang Rui, Fan Weicheng. Cellular automata pedestrian movement model considering human behavior[J]. Chinese Science Bulletin, 2003, 48(16):1695-1699
[42]宋卫国,于彦飞,陈涛.出口条件对人员疏散的影响及其分析[J].火灾科学, 2003,12(2): 100-104.
[43]曲昭伟,周立军,王殿海.城市信号交叉口自行车及行人到达与释放规律[J].公路交通科技,2004,21(8): 93-94.
[44]陈然,董力耘.中国大都市行人交通特征的实测和初步分析[J].上海大学学报,2005,11(1):93-97.
[45]裴玉龙,冯树民.城市行人过街速度研究[J].公路交通科技,2006,23 (9):104-107.
[46]陈鹏.基于多智能主体的人群流动形态动态模拟研究[D].同济大学,2006.
[47]李得伟;城市轨道交通枢纽乘客集散模型及微观仿真理论[D].北京交通大学,2007.
[48]赵光华,行人仿真在奥运地铁站的应用研究[D].北京工业大学,2007.
[49]史建港,大型活动行人交通特性研究[D].北京工业大学,2007.
[50]于彦飞,人员疏散的多作用力元胞自动机模型研究[D].中国科学技术大学,2008.
[51]交通工程学[M].李作敏,人民交通出版社, 2000.
[52]交通工程基础[M]. Homburger著,任福田译,中国建筑工业出版社,1990.
[53]傅丹,冯卫东,于起峰,徐一丹,等.一种摄像机自标定的线性方法[J].光电工程. Vol(35), 2008,1:71-75.
[54]机器视觉[M].张广军,科学出版社,2005: 24-27.
[55]Ganem, J. (1998). A behavioral demonstration of Fermat's principle[J]. The Physics Teacher, 1998, 36: 76-78.
[56]Helbing D, Traffic and related self-driven many-particle systems[J]. Reviewsof Modern Physics 73, Issue 4, 2001: 1067-1141.
[57]Helbing, Dirk, Farkas, Illes J. and Vicsek, Tamas, Simulating Dynamical Features of Escape Panic[J]. Nature, Vol. 407,2000: 487-490.
[58]任福田,刘小明,荣建等编著.交通工程学[M].北京:人民交通出版社, 2003.
[59]混沌时间序列分析及其应用[M].吕金虎,陆君安,陈士华编著.武汉大学出版社, 2002: 28-80.
[60]A. Wolf. J.B. Swift, H. L. Swinney and J. A. Vastano. Determining Lyapunov exponents from a time series[J]. Physica 16D, 1985: 285-317.
[61]Kennel M.B., Brown R., Abarbanel H.D.I. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Physical Review A, 1992, 45(6): 3403-3411.
[62]卢宇法,陈宇红,贺国光.应用改进型小数据量法计算交通流的最大Lyapunov指数[J].系统工程理论与实践, 2007, 1(1): 86-90.
[63]Hoogendoorn SP, Bovy PHL. Normative pedestrian behaviour theory and modelling[M] In Michael A.P. Taylor (Ed.), Transportation and traffic theory in the 21st century. Oxford: Pergamon, Elsevier science, 2002: 219-245.
[64]Hoogendoorn SP, Bovy PHL. Pedestrian route-choice and activity scheduling theory and models[J]. Transportation Research Part B, 2004, 38 (2): 169-190.
[65]工程心理学与人的作业[M].C.D. Wichens,J. Hollands著,朱祖祥等译,华东师范大学出版社,2003: 354-402.
[66]心理学与生活[M]. R. J. Gerrig,P. G. Zimbardo著,王垒等译,人民邮电出版社,2003:3-10.
[67]Johnson. M. The body in the mind. The bodily basis of meaning, imagination and reason[M]. The University of Chicago Press, 1987.
[68]Martin Raubal, M Worboys. A Formal Model of the Process of Wayfinding in Built Environments[C]. Proceeding of the International Conference on Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science, 1999: 381-399.
[69]Ujjal Chattaraj, Armin Seyfried, Partha Chakroborty. Comparison of Pedestrian Fundamental Diagram Across Cultures[J]. Advances in complex systems, 2009, 12(3):393-405.
[70]Hoogendoorn, S. P., W. Daamen , Microscopic calibration and validation ofpedestrian models:Cross-comparison of models using experimental data[M]. A. Schadschneider et. al. (eds.) Proceedings of Traffic and Granular Flow '05, Springer, Berlin, 2005: 329-340..
[71]A. Steiner, M. Philipp, A. Schmid. Parameter estimation for a pedestrian simulation model[C]. STRC: 7th Swiss Transport Research Conference, Monte VeritàAscona, 2007, 9: 1-29.
[72]A. Johansson, D. Helbing, P.K. Shukla. Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data[J]. Advances in Complex Systems, 2007, 10(4): 271–288.
[73]Marko Apel. Simulation of pedestrian flows based on the social force model using the Verlet Link Cell algorithm[D]. Poznan University of Technology, 2004.
[74]Lakoba, T.I., Kaup, D.J., Finkelstein, N.M. Modifications of the Helbing Molnár Farkas Vicsek social force model for pedestrian evolution[J]. Simulation, 2005(81): 339-352.
[75]J.J. Gibson. The theory of affordances[J]. In R. Shaw and J. Bransford, editors, Perceiving, Acting and Knowing, Wiley, New York, 1977: 67-82.
[76]Montello, D.R. Navigation[M]. In P. Shah & A. Miyake (Eds.), The Cambridge handbook of visuospatial thinking[M]. Cambridge University Press, 2005: 257-294.
[77]R. Nicole, C. W. Reynolds. Steering behavior for autonomous characters[C]. Proceedings of the Game Developers Conference, 1999: 763-782.
[78]W. Shao, D. Terzopoulos. Autonomous pedestrians[J]. Graphical Models, 2007(69): 246-274.
[79]S. Musse, D. Thalmann, Hierarchical model for real time simualtion of virtual human crowds[J]. IEEE Transactions on Visualization and Computer Graphics 2001, 7(2): 152-164.
[80]J. Gunge. AI for games and animation: a cognitive modeling approach[M]. AAI for Games and Animation. Ak Peters Natick Press, Mssachusetts, 1999: 29-44.
[81]T. F. Evers, S. R. Musse. Building Artificial Memory of Autonomous Agents Using Dynamical and Hierarchical Finite State Machine[J]. Computer Animation, Geneva, Switzerland, 2002, 6: 164-169.
[82]虚拟现实技术[M]. G.C. Burdea, P. Coiffet著,魏迎梅等译.电子工业出版社, 2005: 143-146.
[83]柳伍生,余朝玮.地铁站楼梯行人流交通特征的数据拟合分析[J].计算机工程与应用, 2008, 44(03): 50-52.
[84]杨晓光,滕靖等译.美国公共交通通行能力和服务质量手册[M].北京:中国建筑工业出版社,2010.
[85]http://ccl.northwestern.edu/netlogo/4.0.4/docs/NetLogo_manual_chinese.pdf
[86]任福田,刘小明,荣建等编著.交通工程学[M].北京:人民交通出版社.2003.
[87]杜先睿.基于多智能体仿真的交通疏散问题研究[D].哈尔滨工业大学硕士学位论文,2006.
[88]方伟峰,杨立中等.基于元胞自动机的多自主体人员行为模型及其在性能化设计中的应用[J].中国工程科学,2003,5(3):67-71.
[89]葛亮.城市综合客运换乘枢纽规划与设计方法研究[D].东南大学博士论文,2005.
[90]胡清梅,方卫宁,邓野.一种基于社会力的行人运动模型研究[J].系统仿真学报,2009,21(4):977-980.
[91]何民.混合交通流微观仿真关键技术研究[D].北京工业大学,2003.
[92]廖守亿.复杂系统基于的建模与仿真方法研究及应用.国防科技大学博士论文,2005.
[93] William H. K. Lam, Hung Hom and Chung-yu Cheung. Pedestrian Speed/Flow Relationships for Walking Facilities in Hong Kong[J]. Journal of Transportation Engineering, 2000, 126(4):343-349.
[94]Daamen, W., Hoogendoorn. SP. Controlled experiments to derive walking behavior[J]. Transport Infrastructure Res. 2003(1) 39-59.
[95]Daamen. W, Hoogendoorn. SP. Pedestrian traffic flow operations on a platform: observations and comparison with simulation tool SimPed[C]. In J Allan, C Brebbia & R.J. Hill (Eds.), Computers in Railways IX. Southampton: WIT Press. (TUD). 2004: 125-134.
[96] Stella Fiorenzo-Catalano, Sascha Hoogendoorn-Lanser, Rob van Nes. Choice set composition modelling in multimodal travelling[C]. 10th International Conference on Travel Behaviour Research Lucerne, 2003: 10-15.
[97]Hoogendoorn, SP, Daamen, W. Design assessment of Lisbon transfer stations using microscopic pedestrian simulation[C]. In J Allan, C Brebbia, R.J. Hill (Eds.), Computers in Railways IX, 2004: 135-147.
[98]Pettre Julien, Grillon Helena, Thalmann Daniel. Crowds of moving objects: Navigation planning and simulation[C]. IEEE International Conference on Robotics and Automation,2007: 3062-3067
[99]Daniel Harney. Pedestrian modelling: current methods and future directions[J]. Road &Transport Research, 11(4):2–12, 2002.
[100]SP. Hoogendoorn and W. Daamen. Microscopic Parameter Identification of Pedestrian Models and its Implications to Pedestrian Flow Modeling[C]. Transportation Research Board Annual Meeting 2006.
[101]SP. Hoogendoorn and P. Bovy. Simulation of pedestrian flows by optimal control and differential games[J]. Optimal control applications and methods 24, 2003: 153-172.
[102]Thorsten Schelhorn, David O’Sullivan et al. STREETS: an agent-based pedestrian model [R]. Working paper series of Center For Advanced Spatial Analysis. Paper 9, 1999.
[103]Teknomo, K., Y. Takeyama, H. Inamura. Determination of pedestrian flow performance based on video tracking and microscopic simulations[C]. Proc. Infrastructure Planning Conf. Ashikaga, Japan. 2000, 23(1) 639–642.
[104]Teknomo, K., Takeyama, Y., & Inamura, H. Microscopic pedestrian simulation model to evaluate‘‘Lane-like segregation’’of pedestrian crossing[C]. In Proceedings of infrastructure planning conference. Kouchi, Japan. 2001(24)
[105]Takakuwa S. and Oyama, T. Simulation analysis of international departure passenger flows in an airport terminal [C]. Pro. of the 2003 Winter Simulation Conference, 2003 United States: IEEE, 2003:1627-1634.