事故影响下随机交通网络动态可靠性
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摘要
道路交通网络在外部因素影响下,路网性能存在随机性。这些外部因素包括可重复的随机因素和不可重复的随机因素两类,可重复性因素如日常的道路拥堵带来的路段通行能力的下降以及日变的交通需求等,此类因素的特征是长时间内具有持续性；不可重复性因素如交通事故等突发事件对路段通行能力的影响,此类因素的特征是只在事件持续期内影响路网。这些随机因素影响路网通行能力及出行行为,改变路网性能。
交通事故等突发事件造成的非重复性拥挤是影响路网性能随机变化的一个重要原因。交通事故造成路网局部能力的随机下降,打破路网原有的平衡状态,可能导致路网性能的急剧波动,进而降低路网容纳交通量的能力。而从用户的角度,交通事故等突发事件所造成的非重复性拥挤具有弱预测性,难以在出行前作出相应反应,导致出行时间可能大幅增加。因此,分析交通事故影响下路网状态的演变规律及事故对路网性能的影响,对于路网改建,交通管制及事故预防措施等均具有重要意义。
为分析事故影响下交通网络可靠性,需先分析事故影响下的路网状态及路径选择行为。因此论文构建了事故影响下的流量加载模型,分析了在途路径选择行为,在此基础上提出了动态行程时间可靠性和动态容量可靠性定义,为事故影响下的网络性能描述提供了理论支撑。论文的主要研究内容包括以下几个部分：
(1)总结了三类动态流量加载模型——速度-密度流量加载模型,元胞传输模型及路段传输模型。速度-密度模型以速度和密度之间的函数关系为基础,后两个模型以流量和密度之间的函数关系为基础。三个模型均包括路段模型和节点模型两部分。比较了三类模型的优缺点：速度-密度模型假设流量在路段上均匀分布操作简单但是误差较大。元胞传输模型计算每个元胞的驶入驶出流量,计算量大,路段传输模型则需要更多的存储空间。
(2)改进了事故影响下的动态流量加载模型,基于Logit选择原则描述出发时刻的路径选择概率,建立了事故影响下的拟动态模型。在速度-密度模型中,利用分流合流模型及速度-密度函数,分别建立路段容纳车辆数和非事故路段走行时间模型,通过分析事故路段交通流的演化过程,利用交通波理论估计排队长,建立事故路段走行时间模型。
给出了可变元胞传输模型和路段传输模型中路段走行时间及路段流量密度的计算方法。分析了交通事故影响下路网中走行时间与用户择路概率的相互作用及其演变规律,结果表明：事故持续期到排队完全消散期内,路径走行时间和路径选择概率呈现此消彼长并持续震荡的状态；事故持续期和事故清除后,事故路段上的排队位置发生转移。
(3)为分析事故影响下出行者在途路径选择行为,引入混合Logit模型描述出行者在节点处的路径转变概率。采用Logit模型描述出发时刻的路径选择概率,结合路段传输模型加载流量,得到影响路径转变概率的影响因素值,改进了路段传输模型节点模型中选择概率的计算,分析了考虑在途路径选择情况下路径选择概率和路径走行时间的变化规律。结果表明：事故持续期间,路径上的选择概率及走行时间均呈现震荡状态,与只考虑出发时刻路径选择的区别在于,路径选择概率及路径走行时间的震幅相对较小,表明了出行者对出发前选择路径的依赖性。
(4)为计算事故持续期内路网可靠性,定义了动态行程时间可靠性,将交通事故持续时间离散化,建立以路段传输模型和Logit路径选择模型为基础的拟动态模型,得到时段内到达车辆数及其走行时间,计算车辆平均走行时间；将事故持续时间,事故严重程度及事故发生位置看作随机变量,基于蒙特卡洛技术计算路网行程时间可靠性。结果表明：出行需求越大,可靠度越低；时间阈值越大,可靠度越高；持续时间均值越大,可靠度越低；可靠度随着持续时间方差的变化则根据不同时间阈值的大小有递减和递增两种趋势。事故越严重,可靠性越低,可靠性值随着事故发生位置的改变而改变。
(5)为计算事故影响期内路网可靠性,定义了动态容量可靠性,将交通事故影响时间离散化,建立了以路段传输模型和Logit路径选择模型为基础的拟动态模型,得到各时段内允许驶入路网的总车辆数作为路网容量指标;将事故持续时间,事故严重程度及事故发生位置看作随机变量,基于概率解析法计算路网容量可靠性。结果表明：容量需求阈值越大,容量可靠度越低；持续时间均值越大,容量可靠度越低；可靠度随着持续时间方差的变化则有递增和递减两种趋势。
Road network's performance presents stochastic under the influence of external factors. These factors include recurrent factors and non-recurrent factors. Recurrent factors include capacity degradation and traffic demand variation due to day-to-day congestion, the character is continuous for a long time; Non-recurrent factors include capacity degradation due to accidents and significant meetings, the character is that it only influences network during duration time.
Non-recurrent congestion caused by traffic incident and other emergencies has great impacts on the network service level. Incident often brings random capacity degradation for partial links, breaks the original equilibrium of network, leads to sharp fluctuations in network performance and declines network efficiency. From the perspective of travelers, non-recurrent congestion caused by incident and other emergencies has weak predictability and is difficult to respond before trip, which may lead to travel time increase greatly. Therefore, analyzing interaction between network travel time and route choice probability under incident condition has important significance in real-time traffic management and control. Definitions of dynamic travel time reliability and capacity reliability are presented to describe responsive ability to incident based on travel behavior. One hand, reliability data can be issued to travelers for basis of route choice before departure, and route choice behavior considering reliability can decrease travel time variation due to incident; the other hand, for network managers, reliability can be used to analyze vulnerable links, assess network performance and provide guidance for traffic manage and incident prevention.
In order to compute network reliability under incident condition, analysis of road network condition and route choice behavior is necessary component. Therefore, Travel behavior model under incident and definitions of dynamic reliability are constructed in this dissertation in order to provide theoretical support for network performance analysis during incident. The main contributions of this dissertation include:
(1) The existing models on dynamic network loading are reviewed including speed-density loading model, cell transmission model and link transmission model. Speed-density loading model is based on the function of speed and density; the other models are based on the function of flow and density. All the three models include two parts——link model and node model. Speed-density loading model has easier computation and larger errors due to the assumption that vehicles distribute homogeneous on the whole link. Cell transmission model has computational complexity due to calculation for every cell and link transmission has storage complexity.
(2) Improvement for dynamic network loading is presented and a quasi-dynamic model is established based on Link Transmission Model and logit principle. For speed-density loading model, diverge-merge model and speed-density function are adopted to calculate link accommodated vehicles and travel time of non-incident links firstly. Then through analyzing traffic flow evolution of incident link, Kinematic Wave Theory is used to compute queue length and travel time of incident links.
Methods of computing travel time and flow density based on cell transmission model and link transmission model are presented, interaction and evolution rule between network travel time and route choice probability is presented. The results indicate that:route travel time and route choice probability present concussed during incident period and queue dispersing period; the queue spot transfers during incident duration period and clearing period.
(3) In order to analyze en-route behavior, mixed-logit model is adopted to compute route conversion probability at nodes. Combined with link transmission model, influence factor value is calculated and route conversion probability value is computed by improving node model of LTM. Through analyzing interaction and evolution rule between network travel time and route choice probability under en-route condition, the results indicate that:route travel time and route choice probability present concussed, the differences exist that:The amplitudes of route travel time and route choice probability reveal smaller due to compliance of original routes before departure.
(4) In order to compute network reliability under incident, a definition of dynamic travel time reliability is presented. Distributing incident duration time, through establishing a quasi-dynamic model based on Link Transmission Model and logit principle, the number of arrived vehicles and travel time is obtained; Setting incident duration time, incident impact and incident spots as stochastic variables, Monte-Carlo method is adopted to calculate travel time reliability. The results indicate that:larger travel demand corresponds to lower reliability; larger time threshold corresponds to higher reliability; larger mean value of incident duration time corresponds to lower reliability; change trend of reliability corresponding to duration time variance presents ascent and descent with different time threshold. Lower reliability corresponds to more serious incident and reliability changes with different incident spots.
(5) In order to compute network reliability under incident, a definition of dynamic capacity reliability is presented. Through establishing a quasi-dynamic model based on Link Transmission Model and logit principle, the vehicle number of entering into network is obtained; setting incident duration time, incident impact and incident spots as stochastic variables, probability analysis method is adopted to calculate capacity reliability. The results indicate that:Larger capacity demand corresponds to lower reliability; larger mean value of incident duration time corresponds to lower reliability; change trend of reliability corresponding to duration time variance presents ascent and descent with different time threshold.
交通事故等突发事件造成的非重复性拥挤是影响路网性能随机变化的一个重要原因。交通事故造成路网局部能力的随机下降,打破路网原有的平衡状态,可能导致路网性能的急剧波动,进而降低路网容纳交通量的能力。而从用户的角度,交通事故等突发事件所造成的非重复性拥挤具有弱预测性,难以在出行前作出相应反应,导致出行时间可能大幅增加。因此,分析交通事故影响下路网状态的演变规律及事故对路网性能的影响,对于路网改建,交通管制及事故预防措施等均具有重要意义。
为分析事故影响下交通网络可靠性,需先分析事故影响下的路网状态及路径选择行为。因此论文构建了事故影响下的流量加载模型,分析了在途路径选择行为,在此基础上提出了动态行程时间可靠性和动态容量可靠性定义,为事故影响下的网络性能描述提供了理论支撑。论文的主要研究内容包括以下几个部分：
(1)总结了三类动态流量加载模型——速度-密度流量加载模型,元胞传输模型及路段传输模型。速度-密度模型以速度和密度之间的函数关系为基础,后两个模型以流量和密度之间的函数关系为基础。三个模型均包括路段模型和节点模型两部分。比较了三类模型的优缺点：速度-密度模型假设流量在路段上均匀分布操作简单但是误差较大。元胞传输模型计算每个元胞的驶入驶出流量,计算量大,路段传输模型则需要更多的存储空间。
(2)改进了事故影响下的动态流量加载模型,基于Logit选择原则描述出发时刻的路径选择概率,建立了事故影响下的拟动态模型。在速度-密度模型中,利用分流合流模型及速度-密度函数,分别建立路段容纳车辆数和非事故路段走行时间模型,通过分析事故路段交通流的演化过程,利用交通波理论估计排队长,建立事故路段走行时间模型。
给出了可变元胞传输模型和路段传输模型中路段走行时间及路段流量密度的计算方法。分析了交通事故影响下路网中走行时间与用户择路概率的相互作用及其演变规律,结果表明：事故持续期到排队完全消散期内,路径走行时间和路径选择概率呈现此消彼长并持续震荡的状态；事故持续期和事故清除后,事故路段上的排队位置发生转移。
(3)为分析事故影响下出行者在途路径选择行为,引入混合Logit模型描述出行者在节点处的路径转变概率。采用Logit模型描述出发时刻的路径选择概率,结合路段传输模型加载流量,得到影响路径转变概率的影响因素值,改进了路段传输模型节点模型中选择概率的计算,分析了考虑在途路径选择情况下路径选择概率和路径走行时间的变化规律。结果表明：事故持续期间,路径上的选择概率及走行时间均呈现震荡状态,与只考虑出发时刻路径选择的区别在于,路径选择概率及路径走行时间的震幅相对较小,表明了出行者对出发前选择路径的依赖性。
(4)为计算事故持续期内路网可靠性,定义了动态行程时间可靠性,将交通事故持续时间离散化,建立以路段传输模型和Logit路径选择模型为基础的拟动态模型,得到时段内到达车辆数及其走行时间,计算车辆平均走行时间；将事故持续时间,事故严重程度及事故发生位置看作随机变量,基于蒙特卡洛技术计算路网行程时间可靠性。结果表明：出行需求越大,可靠度越低；时间阈值越大,可靠度越高；持续时间均值越大,可靠度越低；可靠度随着持续时间方差的变化则根据不同时间阈值的大小有递减和递增两种趋势。事故越严重,可靠性越低,可靠性值随着事故发生位置的改变而改变。
(5)为计算事故影响期内路网可靠性,定义了动态容量可靠性,将交通事故影响时间离散化,建立了以路段传输模型和Logit路径选择模型为基础的拟动态模型,得到各时段内允许驶入路网的总车辆数作为路网容量指标;将事故持续时间,事故严重程度及事故发生位置看作随机变量,基于概率解析法计算路网容量可靠性。结果表明：容量需求阈值越大,容量可靠度越低；持续时间均值越大,容量可靠度越低；可靠度随着持续时间方差的变化则有递增和递减两种趋势。
Road network's performance presents stochastic under the influence of external factors. These factors include recurrent factors and non-recurrent factors. Recurrent factors include capacity degradation and traffic demand variation due to day-to-day congestion, the character is continuous for a long time; Non-recurrent factors include capacity degradation due to accidents and significant meetings, the character is that it only influences network during duration time.
Non-recurrent congestion caused by traffic incident and other emergencies has great impacts on the network service level. Incident often brings random capacity degradation for partial links, breaks the original equilibrium of network, leads to sharp fluctuations in network performance and declines network efficiency. From the perspective of travelers, non-recurrent congestion caused by incident and other emergencies has weak predictability and is difficult to respond before trip, which may lead to travel time increase greatly. Therefore, analyzing interaction between network travel time and route choice probability under incident condition has important significance in real-time traffic management and control. Definitions of dynamic travel time reliability and capacity reliability are presented to describe responsive ability to incident based on travel behavior. One hand, reliability data can be issued to travelers for basis of route choice before departure, and route choice behavior considering reliability can decrease travel time variation due to incident; the other hand, for network managers, reliability can be used to analyze vulnerable links, assess network performance and provide guidance for traffic manage and incident prevention.
In order to compute network reliability under incident condition, analysis of road network condition and route choice behavior is necessary component. Therefore, Travel behavior model under incident and definitions of dynamic reliability are constructed in this dissertation in order to provide theoretical support for network performance analysis during incident. The main contributions of this dissertation include:
(1) The existing models on dynamic network loading are reviewed including speed-density loading model, cell transmission model and link transmission model. Speed-density loading model is based on the function of speed and density; the other models are based on the function of flow and density. All the three models include two parts——link model and node model. Speed-density loading model has easier computation and larger errors due to the assumption that vehicles distribute homogeneous on the whole link. Cell transmission model has computational complexity due to calculation for every cell and link transmission has storage complexity.
(2) Improvement for dynamic network loading is presented and a quasi-dynamic model is established based on Link Transmission Model and logit principle. For speed-density loading model, diverge-merge model and speed-density function are adopted to calculate link accommodated vehicles and travel time of non-incident links firstly. Then through analyzing traffic flow evolution of incident link, Kinematic Wave Theory is used to compute queue length and travel time of incident links.
Methods of computing travel time and flow density based on cell transmission model and link transmission model are presented, interaction and evolution rule between network travel time and route choice probability is presented. The results indicate that:route travel time and route choice probability present concussed during incident period and queue dispersing period; the queue spot transfers during incident duration period and clearing period.
(3) In order to analyze en-route behavior, mixed-logit model is adopted to compute route conversion probability at nodes. Combined with link transmission model, influence factor value is calculated and route conversion probability value is computed by improving node model of LTM. Through analyzing interaction and evolution rule between network travel time and route choice probability under en-route condition, the results indicate that:route travel time and route choice probability present concussed, the differences exist that:The amplitudes of route travel time and route choice probability reveal smaller due to compliance of original routes before departure.
(4) In order to compute network reliability under incident, a definition of dynamic travel time reliability is presented. Distributing incident duration time, through establishing a quasi-dynamic model based on Link Transmission Model and logit principle, the number of arrived vehicles and travel time is obtained; Setting incident duration time, incident impact and incident spots as stochastic variables, Monte-Carlo method is adopted to calculate travel time reliability. The results indicate that:larger travel demand corresponds to lower reliability; larger time threshold corresponds to higher reliability; larger mean value of incident duration time corresponds to lower reliability; change trend of reliability corresponding to duration time variance presents ascent and descent with different time threshold. Lower reliability corresponds to more serious incident and reliability changes with different incident spots.
(5) In order to compute network reliability under incident, a definition of dynamic capacity reliability is presented. Through establishing a quasi-dynamic model based on Link Transmission Model and logit principle, the vehicle number of entering into network is obtained; setting incident duration time, incident impact and incident spots as stochastic variables, probability analysis method is adopted to calculate capacity reliability. The results indicate that:Larger capacity demand corresponds to lower reliability; larger mean value of incident duration time corresponds to lower reliability; change trend of reliability corresponding to duration time variance presents ascent and descent with different time threshold.
引文
[1]Abdel-Aty M. A.,Kitamura R.,Jovanisp P.. Investigating effect of travel time variability on route choice using repeated measurement stated preference data. Transportation Research Record,1996,1493:39-45.
[2]Lam T C.. The Effect of Variability of Travel Time on Route an Time-of-day Choice[D]. Department of Economics, University of California,2006.
[3]Ghosh A.. Heterogeneity in Value of Time:Revealed and Stated Preference Estimates from the 1-15 Congestion Pricing Project[C]//Proceedings of the 80th Annual Meeting of the Transportation Research Board. Washington D.C,2001.
[4]Chen A, Zhou Z. The a-reliable mean-excess traffic equilibrium model with stochastic travel times. Transportation Research Part B,2010,44(4):493-513.
[5]Small K. A., Noland R B, Koskenoja P. Socio-economic attributes and impacts of travel reliability:a stated preference approach[R]. California PATH Research Report,1995:95-36.
[6]Polak J.. Travel time variability and departure time choice:a utility theoretic approach.Polytechnic of Central London, Transport Studies Group, Discussion paper No.15,1987.
[7]Kahneman D., Tversky A. Prospect Theory:An Analysis Of Decision Under Risk[J].Econometrica,1979,47(2):263-291.
[8]Kahneman D., Tversky A. Choices, Values, And Frames[J]. American Psychologist,1984,39(4):341-350.
[9]Gaver D P. Headstart strategies for combating congestion[J].Transportation Science,1968,3(2):172-181.
[10]Jackson B W, Jucker J V. An emperical study of travel time variability and travel choice behavior[J].Transportation Science,1981,16(4):460-475.
[11]Noland R B, Small K A. Travel-time uncertainty, departure time choice, and the cost of the morning commutes[J].Transportation Research Record,1995(1493):150-158.
[12]Siu B W Y. Travel time budget and schedule delay costs under uncertainty[D].The Hague:The Netherlands,2007.
[13]Li H. Reliability-based Dynamic Network Design with Stochastic Networks[D].Delft University of Technology,2009.
[14]Lo H K, Luo X W, Siu B W Y. Degradable transport network:Travel time budget of travelers with heterogeneous risk aversion[J].Transportation Research Part B,2006,40(9):792-806.
[15]Ettema D, Timmermans H. Costs of travel time uncertainty and benefits of travel time information:Conceptual model and numerical examples [J].Transportation Research C,2006,14(5):335-350.
[16]Lotan T. Effects of familiarity on route choice behavior in the presence of information[J].Transportation Research Part C,1997,5(3-4):225-243.
[17]Jha M, Madanat S, Peeta S. Perception updating and day-to-day travel choice dynamics in traffic networks with information provision[J].Transportation Research Part C,1998,6(3):189-212.
[18]Wahle J, Bazzan A L C,Kliigl F. The impact of real-time information in a two-route scenario using agent-based simulation [J].Transportation Research Part C,2002,10(5):399-417.
[19]Jou R C, Kitamura R. Commuter departure time choice:a reference-point approach[J].Mimeograph,2002:149-155.
[20]Avineri E. A Cumulative Prospect Theory Approach to PassengersBehavior Modeling:Waiting Time Paradox Revisited[J].Journal of Intelligent Transportation Systems,2004,8(4):195-204.
[21]Fujii S, Kitamura R. Drivers' Mental Representation of Travel Time and Departure Time Choice in Uncertain Traffic Network Conditions [J].Networks and Spatial Economics,2004,4(3):243-256.
[22]石小法.ATIS环境下动态选择模型的研究[J].系统工程学报,2002,17(3)：271-276.
[23]石小法,王炜.高度信息化条件下的动态配流模型[J].东南大学学报(自然科学版),2001,31(2)：91-93.
[24]赵凛.基于”前景理论”的出行决策模型及ATIS仿真实验研究[D].北京交通大学,2006.
[25]张星臣赵凛.基于“前景理论“的先验信息下出行者路径选择模型[J].交通运输系统工程与信息,2006,6(2)：42-46.
[26]刘玉印,刘伟铭,吴建伟.基于累积前景理论的出行者路径选择模型[J].华南理工大学学报(自然科学版),2010,38(7)：84-100.
[27]杨志勇.基于前景理论的出发时刻和出行路径选择模型研究[D].哈尔滨工业大学,2007.
[28]蒲琪,杨晓光,吕杰.交通信息对驾驶员路径选择行为影响的初步分析[J].公路 交通科技,1999,16(3)：53-56.
[29]黄海军,吴文祥.交通信息对交通行为影响的评价模型[J].系统工程理论与实践,2002,22(10)：81-83.
[30]石小法,王炜,卢林等.交通信息影响下的动态路径选择模型研究[J].公路交通科技,2000,17(4)：35-37.
[31]李志纯,黄海军.先进的旅行者信息系统对出行者选择行为的影响研究[J].公路交通科技,2005,22(2)：95-99.
[32]Boyles S, Fajardo D, Waller T. A Nave Bayesian Classifier for Incident Duration Prediction[C]//Proceedings of the 86th Annual Meeting of the Transportation Research Board,Washington D.C,2007.
[33]Garib A, Radwan E, and Al-Deek H. Estimating magnitude and duration of incident delays [J].Journal of Transportation Engineering,1997,123(6):459-466.
[34]Tampere C M J, Stada J, Immers B.etc. Methodology for Identifying Vulnerable Sections in a National Road Network [J].Transportation Research Record,2007(2012):1-19.
[35]Murray-Tuite P, Mahmassani,H. Methodology for Determining Vulnerable Links in a Transportation Network [J].Transportation Research Record,2004, 1882:88-96.
[36]尚华艳,黄海军,高自友.基于元胞传输模型的实时交通信息设计[J].北京航空航天大学学报,2008,34(2)：234-238.
[37]Knoop V. Road Incidents and Network Dynamics Effects on driving behavior and traffic congestion[D].Delft University of Technology,2009.
[38]Li M W. Robustness Analysis for Road Networks:A framework with combined DTA models[D].Delft University of Technology,2008.
[39]Burghout W, Koutsopoulos H, Andreasson J. Incident Management and Traffic Information:Tools and Methods for Simulation-Based Traffic Prediction[J]. Journal of Transportation Research Record,2010,2161.:20-28.
[40]Menendez M, Daganzo C. Assessment of the Impact of Incidents near Bottlenecks: Strategies to Reduce Delay[J]. Journal of Transportation Research Record,2004, 1867.:53-59.
[41]Iida Y, Wakabayashi H. An approximation method of tenninal reliability of a road network using partial minimal Path and cut set[C]//Proceedings of the 5th WCTR,Yokohama,1989:367-380.
[42]Bell M. G. H, Iida Y. Transportation network analysis[M].John Wiley and Sons, 1997:179-192.
[43]Wakabayashi H, Iida Y. Upper and lower bounds of terminal reliability of road networks:an efficient method with Boolean algebra [J]. Journal of Natural Disaster Science,1992,14(1):29-44.
[44]BellM G H, Iida Y. Estimation the Terminal Reliability of Degradable Transport Network [C]//Triennial Symposium on Transportation Analysis IV, Sao Miguel, Azores Islands, Portugal,2001.
[45]王明文,王江平,姜彩良.高烈度地震区公路网连通可靠性评价模型[J].公路,2011,56(6)：121-124.
[46]高永,温慧敏,郭继孚等.基于关键路段的路网连通可靠性评价方法[C]//第四届中国智能交通年会,中国山东青岛,2008：575-579.
[47]许良,高自友.基于连通可靠性的城市道路交通离散网络设计问题[J].燕山大学学报,2007,31(2)：159-163.
[48]翟京,冷军强,王天逸等.基于替代路径的道路系统连通可靠性分析[J].昆明理工大学学报(理工版),2010,35(4)：57-60.
[49]郭继孚,高永,温慧敏.基于替代路径的路网连通可靠性评价方法研究[J].公路交通科技,2007,24(7)：91-94.
[50]Asakura Y, Kashiwadani M. Road Network reliability caused by daily flunctation of traffic flow[C]//19th PTRC Summer Annual Meeting,1991:73-84.
[51]Lam W.H.K, Xu G. A traffic flow simulator for network reliability assessment [J].Journal of Advanced Transportation,1999,33(2):159-182.
[52]Clark S, Watling D. Modeling network travel time reliability under stochastic demand [J].Transportation Research B,2005,39(2):119-140.
[53]Asakura Y. Reliability Measures of an Origin and Destination Pair in a Deteriorated Road Network with Variable Flows[C]//Selected Proceedings of the 4th EURO Transportation Meeting, Newcastle,1999,273-287.
[54]Chen A, Recker W. Considering Risk Taking Behavior in Travel Time Reliability[C]//Proceedings of the 79th Annual Meeting of the Transportation Research Board, Washington D.C,2000.
[55]Chen A, Lo H. K. Yang H,etc. A capacity related reliability for transportation networks [J].Journal of Advanced Transportation,1999,33(2):183-200.
[56]Lomax T,Turner S, Margiotta R. Monitoring Urban Roadways in 2002:Using Archived Operations Data for March 2004 Reliability and Mobility Measurement [R]. Washington, DC:Department of Transportation Federal Highway Administration Office of Operations,2004.
[57]A1-Deek H, Emam E.B. New Methodology for Estimating Reliability in Transportation Networks with Degraded Link Capacities [J].Journal of Intelligent Transportation Systems,2006,10(3):117-129.
[58]Noland R.B, Small K. A, Koskenoja P. M,etc. Simulating travel reliability[J].Regional Science and Urban Economics,1998,28(5):535-564.
[59]冷军强.冰雪条件下城市路网行程时间可靠性研究[D].哈尔滨工业大学,,2010.
[60]张建.城市交通网络行程时间可靠性研究[D].兰州交通大学,2009.
[61]张勇,白玉,杨晓光.城市道路网络的行程时间可靠性[J].系统工程理论与实践,2009,29(8)：171-176.
[62]冷军强,张亚平,赵兴奎.基于广义出行费用的城市路网行程时间可靠性[J].公路交通科技,2010,27(7)：133-137.
[63]Chen A, Yang H, Lo H. K. Capacity Reliability of a Road Network:An Assessment Methodology and Numerical Results.[J].Transportation Research B,2002,36(3):225-252.
[64]Chen H. K, Hsu M. C, Hsieh C. F. Some issues in network capacity reliability [C]//IEEE International Conference on Networking-Sensing and Control, 2004:293-298.
[65]Yang H, Lo H.K., Tang W H. Travel time versus capacity reliability of a road network[C]//Proceedings of the 79th Annual Meeting of the Transportation Research Board, Washington D.C,2000.
[66]Chen A, Tatineni M, Lee D.H. Effect of Route Choice Models on Estimating Network Capacity Reliability [J]. Transportation Research Reeord,2000(1733):493-513.
[67]Lo H. K., Tung Y. K.. Network with degradable links:capacity analysis and design [J].Transportation Research B,2003,37(4):345-363.
[68]陈艳艳,梁颖,杜华兵.可靠度在路网运营状态评价中的应用[J].土木工程学报,2003,36(1)：36-40.
[69]刘海旭,蒲云.弹性需求随机路网的可靠性[J].公路交通科技,2005,,22(7)：97-100.
[70]侯立文,蒋馥.基于路网可靠性的路网服务水平[J].系统工程理论方法应用,2003,12(3)：248-252.
[71]朱顺应,王炜,邓卫.交通网络可靠度及其道路算法研究[J].中国公路学报,2000,13(1)：91-94.
[72]程琳,李强,王京元.城市道路网络容量可靠性(英文)[J].Journal of Southeast University (English Edition),2004,20(2):235-239.
[73]李志纯,朱道立.随机动态交通网络可靠度分析与评价[J].交通运输工程学报,2008,8(1)：106-112.
[74]黎茂盛,王炜,史峰.降级路网的认知及交通流平衡分析模型[J].中国公路学报,2006,19(6)：87-91.
[75]许良,高自友.基于路段能力可靠性的城市交通网络设计[J].中国公路学报,2006,19(2)：86-90.
[76]Nicholson A, Du Z P. Degradable transportation systems:An integrated equilibrium model [J].Transportation Research Part B,1997,31(3):209-223.
[77]Bell M. G. H. A game theory approach to measuring the performance reliability of transport networks [J].Transportation Research B,2000,34(6):533-546.
[78]Sanso B, Soumis F. Communication & transportation network reliability using routing models[J].IEEE Transactions on Reliability,1991,40(1):29-38.
[79]Lee S, Moon B, Asakura Y. Reliability analysis and calculation on large scale transport network [C]//Reliability of Transport Networks. Hertfordshire:RSP Ltd., 2000:173-189.
[80]Sumalee A, Watling D. P, Nakayama S. Reliable network design Problem:the case with uncertain demand and total travel time reliability [J].Transportation Research Record,2006(1964):81-90.
[81]Chootinan P, Wong S. C, Chen A. A reliability-based network design problem[J].Journal of Advanced Transportation,2005,39(3):247-270.
[82]Sanchez-Silva M, Daniels M, Llerasa G, etc. A transport network reliability model for the efficient assignment of resources [J].Transportation Research B,2005, 39(1):47-63.
[83]许宏科,慕巍,焦家华.高速公路动态交通流模型及其参数的分段辨识[J].武汉理工大学学报(交通科学与工程版),2005,29(1)：91-93.
[84]苏岳龙,路鹭,姚丹亚.经典交通流模型在城市车路不均衡发展评价中的应用[J].公路交通科技,2010,27(11)：108-112.
[85]张鹏,刘儒勋.交通流模型的分析评价与模型的改进[J].昆明理工大学学报,2000,25(4)：118-123.
[86]王勇.物流园区动态交通配流研究[D].北京交通大学,2008.
[87]王赛政.动态交通条件下车辆导航系统的最优路径规划方法研究[D].长沙理工大学,2010.
[88]李清泉,李汉武,谢智颖.面向动态路径选择的路段行程时间的分析研究[J].武汉大学学报(信息科学版),2006,31(6)：519-522.
[89]Nie Y.Equilibrium analysis of macroscopic traffic oscillations [J].Transportation Research Part B,2010,44(1):62-72.
[90]Liu Y, Chang G.L. An arterial signal optimization model for intersections experiencing queue spillback and lane blockage [J].Transportation Research Part C,2011,19(1):130-144.
[91]安维胜.混合交通流动力学建模研究[D].西南交通大学,2010.
[92]Greenshields B. D. A study in highway capacity[J]. Highway Research Board Proceedings,1935,14(1):448-477.
[93]Greenberg H. An analysis of traffic flow [J].Operations Research,1959, 7(1):79-85.
[94]Underwood R. T. Speed, volume and density relationship [J].In Quality and theory of traffic flow,1961:141-188.
[95]Newell G. F. Nonlinear effects in the dynamics of car following [J].Operations Research,1961,9(2):209-299.
[96]Pipes L. A. Car-following models and the fundamental diagram of road traffic [J].Transportation Research Part B,1967, 1(1):21-29.
[97]Castillo J.M.D, Benitez F.G. On the functional form of the speed-density relationship-Ⅱ:Empirical investigation [J].Transportation Research Part B,1995, 29(5):391-406.
[98]Ben-Akiva M. Development of a Deployable Real-time Dynamic Traffic Assignment System[R].MIT, Cambridge, MA:Task D Interim Report:Analytical Developments for DTA System,1996.
[99]Daganzo C. F. The cell transmission model:A dynamic representation of highway traffic consistent with the hydrodynamic theory [J].Transportation Research B, 1994,28(4):269-287.
[100]Daganzo C. F. The cell transmission model, part Ⅱ:network traffic [J].Transportation Research B,1995,29(2):79-93.
[101]Szeto W Y. Enhanced Lagged Cell-Transmission Model for Dynamic Traffic Assignment [J].Transportation Research Record,2008(2085):76-85.
[102]Ishak S, Alecsandru C, Seedah D. Improvement and Evaluation of Cell-Transmission Model for Operational Analysis of Traffic Networks:Freeway Case Study [J].Transportation Research Record,2006(1965):171-182.
[103]Daganzo C. F. The lagged cell-transmission model[C]//Proeeedings of the 14th International Symposium on Transportation and Traffic Theory,New York,1999:81-106.
[104]Szeto W. Y. Enhanced Lagged Cell-Transmission Model for Dynamic Traffic Assignment [J].Transportation Research Record,2009(2085):76-85.
[105]Boela R, Mihaylovab L. A compositional stochastic model for real time freeway traffic simulation [J].Transportation Research B,2006,40(4):319-334.
[106]Kim Y, Keller H. On-line traffic flow model applying the dynamic flow-density relation[C]//Proceeding of the IEE Road Transport Information and Control Conference, London:2002:141-145.
[107]Gomes G, Horowitz R. A study of two onramp metering schemed for congested freeways[C]// Proeeedings of the Ameriean Control Conferene, Denver, Colorado.2003:3756-3761.
[108]Gomes G, Horowitz R. Globally optimal solution to the on-ramp metering problem-Part 1[C]//IEEE Intelligent Transportation Systems Conference,WashingtonD.C,2012:509-514.
[109]Muiioz L, Sun X, Horowitz R. Traffic Density Estimation with the Cell Transmission Model [C]//Proceedings of the American Control Conference, Denver, Colorado,2003:3750-3755.
[110]Muiioz L, Sun X, Sun D. Methodological Calibration of the Cell Transmission Model [C]//In 2004 American Control Conference Proceedings, Boston. Massachusetts,2004:798-803.
[111]尚华艳,黄海军,高自友.基于元胞传输模型的可变信息标志选址问题研究[J].物理学报,2007,56(8)：4342-4347.
[112]尚华艳.基于元胞传输模型的实时交通信息研究[D].北京航空航天大学,2008：1.
[113]Ziliaskopoulos A K. A Linear Programming Model for the Single Destination System Optimum Dynamic Traffic Assignment Problem [J]. Transportation Science,2000,34(1):37-49.
[114]Lo H K, Szeto W Y. A cell-based variational inequality formulation of the dynamic user optimal assignment problem [J].Transportation Research B,2002, 36(5):421-443.
[115]Szeto W Y, Lo H K. A cell-based simultaneous route and departure time choice model with elastic demand[J].Transportation Research B,2004,38(7):593-612.
[116]Szeto W Y, Lo H K. Assignment, Non-Equilibrium Dynamic Traffic[C]// Transportation and Traffic Theory. Flow, Dynamics and Human Interaction.16th International Symposium on Transportation and Traffic Theory, USA,2005: 427-445.
[117]Waller S T, Ziliaskopoulos A K. Stochastic Dynamic Network Design Problem [J].Transportation Research Record,2001 (1771):106-113.
[118]Karoonsoontawong A, Waller S T. Dynamic Continuous Network Design Problem[J].Transportation Research Record,2006(1964):104-117.
[119]Karoonsoontawong A, Waller S T. Robust Dynamic Continuous Network Design Problem [J].Transportation Research Record,2007(1981):58-71.
[120]Lo H K. A novel traffic signal control formulation [J].Transportation Research Part A,1999,33(6):433-448.
[121]Lo H K. A Cell-Based Traffic Control Formulation:Strategies and Benefits of Dynamic Timing Plans [J].Transportation Science,2001,35(2):148-164.
[122]Lo H K, Chang E, Chan Y C. Dynamic network traffic control[J].Transportation Research A,2001,35(8):721-744.
[123]Chow A H F, Lo H K. Sensitivity analysis of signal control with physical queuing: Delay derivatives and an application [J].Transportation Research B,2007, 41(4):462-477.
[124]Asano M, Sumalee A, Kuwahara M. Dynamic Cell Transmission-Based Pedestrian Model with Multidirectional Flows and Strategic Route Choices[J].Transportation Research Record,2007(2039):42-49.
[125]Dixita V.V, Radwana E. Hurricane Evacuation:Origin, Route, and Destination[J].Journal of Transportation Safety & Security,2009,1(1):74-84.
[126]胡晓健,王炜,盛慧.基于可变元胞传输模型的城市道路交通流估计方法[J].交通运输系统工程与信息,2010,10(4)：73-78.
[127]曾建勤,王家捷,刘琨.基于细胞模型及多目标优化的交叉口信号控制(英文)[J].中国科学技术大学学报,2005,35(2)：284-290.
[128]姬杨蓓蓓,张小宁,孙立军.基于元胞传输模型的交通事件消散建模[J].重庆交通大学学报(自然科学版),2008,27(3)：442-445.
[129]崔建勋,安实,崔娜.基于元胞传输模型的区域疏散动态交通分配[J].哈尔滨工业大学学报,2010,42(1)：123-127.
[130]Yperman I. The link transmission model for dynamic network loading [D].Katholieke Universiteit Leuven,2007.
[131]龙建成,高自友,赵小梅.基于路段传输模型的道路出口渠化[J].吉林大学学报(工学版),2009,39(S2)：41-46.
[132]Lighthill M J,Whitham J B. On kinematic waves.I.flow modeling in long rivers. II. A theory of traffic flow on long crowded roads [J].Proceedings of the Royal Society A,1955(229):291-345.
[133]Richards P. I. Shock waves on the highway [J].Operation Research,1956, 4(1):42-51.
[134]Khattak A J, Kanafani A, Colletter E L. Stated And Reported Route Diversion Behavior:Implications On The Benefits Of ATIS[R]. UC Berkeley, California Partners for Advanced Transit and Highways (PATH), Institute of Transportation Studies.1994.
[135]Khattak A J, Schofer J L, Koppelman F S. Commuters' enroute diversion and return decisions:Analysis and implications for advanced traveler information systems[J].Transportation Research A,1993,27(2):101-111.
[136]Polydoropoulou A, Ben-Akiva M, Khattak A. Modeling Revealed and Stated En-Route Travel Response to Advanced Traveler Information Systems [J].Transportation Research Record,1996(1537):38-45.
[137]Madanat S, Yang C. Y, Yen Y. Analysis of Stated Route Diversion Intentions under ATIS Using Latent Variable Modeling[C]//Proceedings of the 74th Annual Meeting of the Transportation Research Board. Washington D.C,1995.
[138]刘海旭.城市交通网络可靠性研究[D].西南交通大学,2004.
[139]Watling D. User equilibrium traffic network assignment with stochastic travel times and late arrival penalty [J].European Journal of Operational Research,2006, 3(175):1539-1556.
[2]Lam T C.. The Effect of Variability of Travel Time on Route an Time-of-day Choice[D]. Department of Economics, University of California,2006.
[3]Ghosh A.. Heterogeneity in Value of Time:Revealed and Stated Preference Estimates from the 1-15 Congestion Pricing Project[C]//Proceedings of the 80th Annual Meeting of the Transportation Research Board. Washington D.C,2001.
[4]Chen A, Zhou Z. The a-reliable mean-excess traffic equilibrium model with stochastic travel times. Transportation Research Part B,2010,44(4):493-513.
[5]Small K. A., Noland R B, Koskenoja P. Socio-economic attributes and impacts of travel reliability:a stated preference approach[R]. California PATH Research Report,1995:95-36.
[6]Polak J.. Travel time variability and departure time choice:a utility theoretic approach.Polytechnic of Central London, Transport Studies Group, Discussion paper No.15,1987.
[7]Kahneman D., Tversky A. Prospect Theory:An Analysis Of Decision Under Risk[J].Econometrica,1979,47(2):263-291.
[8]Kahneman D., Tversky A. Choices, Values, And Frames[J]. American Psychologist,1984,39(4):341-350.
[9]Gaver D P. Headstart strategies for combating congestion[J].Transportation Science,1968,3(2):172-181.
[10]Jackson B W, Jucker J V. An emperical study of travel time variability and travel choice behavior[J].Transportation Science,1981,16(4):460-475.
[11]Noland R B, Small K A. Travel-time uncertainty, departure time choice, and the cost of the morning commutes[J].Transportation Research Record,1995(1493):150-158.
[12]Siu B W Y. Travel time budget and schedule delay costs under uncertainty[D].The Hague:The Netherlands,2007.
[13]Li H. Reliability-based Dynamic Network Design with Stochastic Networks[D].Delft University of Technology,2009.
[14]Lo H K, Luo X W, Siu B W Y. Degradable transport network:Travel time budget of travelers with heterogeneous risk aversion[J].Transportation Research Part B,2006,40(9):792-806.
[15]Ettema D, Timmermans H. Costs of travel time uncertainty and benefits of travel time information:Conceptual model and numerical examples [J].Transportation Research C,2006,14(5):335-350.
[16]Lotan T. Effects of familiarity on route choice behavior in the presence of information[J].Transportation Research Part C,1997,5(3-4):225-243.
[17]Jha M, Madanat S, Peeta S. Perception updating and day-to-day travel choice dynamics in traffic networks with information provision[J].Transportation Research Part C,1998,6(3):189-212.
[18]Wahle J, Bazzan A L C,Kliigl F. The impact of real-time information in a two-route scenario using agent-based simulation [J].Transportation Research Part C,2002,10(5):399-417.
[19]Jou R C, Kitamura R. Commuter departure time choice:a reference-point approach[J].Mimeograph,2002:149-155.
[20]Avineri E. A Cumulative Prospect Theory Approach to PassengersBehavior Modeling:Waiting Time Paradox Revisited[J].Journal of Intelligent Transportation Systems,2004,8(4):195-204.
[21]Fujii S, Kitamura R. Drivers' Mental Representation of Travel Time and Departure Time Choice in Uncertain Traffic Network Conditions [J].Networks and Spatial Economics,2004,4(3):243-256.
[22]石小法.ATIS环境下动态选择模型的研究[J].系统工程学报,2002,17(3)：271-276.
[23]石小法,王炜.高度信息化条件下的动态配流模型[J].东南大学学报(自然科学版),2001,31(2)：91-93.
[24]赵凛.基于”前景理论”的出行决策模型及ATIS仿真实验研究[D].北京交通大学,2006.
[25]张星臣赵凛.基于“前景理论“的先验信息下出行者路径选择模型[J].交通运输系统工程与信息,2006,6(2)：42-46.
[26]刘玉印,刘伟铭,吴建伟.基于累积前景理论的出行者路径选择模型[J].华南理工大学学报(自然科学版),2010,38(7)：84-100.
[27]杨志勇.基于前景理论的出发时刻和出行路径选择模型研究[D].哈尔滨工业大学,2007.
[28]蒲琪,杨晓光,吕杰.交通信息对驾驶员路径选择行为影响的初步分析[J].公路 交通科技,1999,16(3)：53-56.
[29]黄海军,吴文祥.交通信息对交通行为影响的评价模型[J].系统工程理论与实践,2002,22(10)：81-83.
[30]石小法,王炜,卢林等.交通信息影响下的动态路径选择模型研究[J].公路交通科技,2000,17(4)：35-37.
[31]李志纯,黄海军.先进的旅行者信息系统对出行者选择行为的影响研究[J].公路交通科技,2005,22(2)：95-99.
[32]Boyles S, Fajardo D, Waller T. A Nave Bayesian Classifier for Incident Duration Prediction[C]//Proceedings of the 86th Annual Meeting of the Transportation Research Board,Washington D.C,2007.
[33]Garib A, Radwan E, and Al-Deek H. Estimating magnitude and duration of incident delays [J].Journal of Transportation Engineering,1997,123(6):459-466.
[34]Tampere C M J, Stada J, Immers B.etc. Methodology for Identifying Vulnerable Sections in a National Road Network [J].Transportation Research Record,2007(2012):1-19.
[35]Murray-Tuite P, Mahmassani,H. Methodology for Determining Vulnerable Links in a Transportation Network [J].Transportation Research Record,2004, 1882:88-96.
[36]尚华艳,黄海军,高自友.基于元胞传输模型的实时交通信息设计[J].北京航空航天大学学报,2008,34(2)：234-238.
[37]Knoop V. Road Incidents and Network Dynamics Effects on driving behavior and traffic congestion[D].Delft University of Technology,2009.
[38]Li M W. Robustness Analysis for Road Networks:A framework with combined DTA models[D].Delft University of Technology,2008.
[39]Burghout W, Koutsopoulos H, Andreasson J. Incident Management and Traffic Information:Tools and Methods for Simulation-Based Traffic Prediction[J]. Journal of Transportation Research Record,2010,2161.:20-28.
[40]Menendez M, Daganzo C. Assessment of the Impact of Incidents near Bottlenecks: Strategies to Reduce Delay[J]. Journal of Transportation Research Record,2004, 1867.:53-59.
[41]Iida Y, Wakabayashi H. An approximation method of tenninal reliability of a road network using partial minimal Path and cut set[C]//Proceedings of the 5th WCTR,Yokohama,1989:367-380.
[42]Bell M. G. H, Iida Y. Transportation network analysis[M].John Wiley and Sons, 1997:179-192.
[43]Wakabayashi H, Iida Y. Upper and lower bounds of terminal reliability of road networks:an efficient method with Boolean algebra [J]. Journal of Natural Disaster Science,1992,14(1):29-44.
[44]BellM G H, Iida Y. Estimation the Terminal Reliability of Degradable Transport Network [C]//Triennial Symposium on Transportation Analysis IV, Sao Miguel, Azores Islands, Portugal,2001.
[45]王明文,王江平,姜彩良.高烈度地震区公路网连通可靠性评价模型[J].公路,2011,56(6)：121-124.
[46]高永,温慧敏,郭继孚等.基于关键路段的路网连通可靠性评价方法[C]//第四届中国智能交通年会,中国山东青岛,2008：575-579.
[47]许良,高自友.基于连通可靠性的城市道路交通离散网络设计问题[J].燕山大学学报,2007,31(2)：159-163.
[48]翟京,冷军强,王天逸等.基于替代路径的道路系统连通可靠性分析[J].昆明理工大学学报(理工版),2010,35(4)：57-60.
[49]郭继孚,高永,温慧敏.基于替代路径的路网连通可靠性评价方法研究[J].公路交通科技,2007,24(7)：91-94.
[50]Asakura Y, Kashiwadani M. Road Network reliability caused by daily flunctation of traffic flow[C]//19th PTRC Summer Annual Meeting,1991:73-84.
[51]Lam W.H.K, Xu G. A traffic flow simulator for network reliability assessment [J].Journal of Advanced Transportation,1999,33(2):159-182.
[52]Clark S, Watling D. Modeling network travel time reliability under stochastic demand [J].Transportation Research B,2005,39(2):119-140.
[53]Asakura Y. Reliability Measures of an Origin and Destination Pair in a Deteriorated Road Network with Variable Flows[C]//Selected Proceedings of the 4th EURO Transportation Meeting, Newcastle,1999,273-287.
[54]Chen A, Recker W. Considering Risk Taking Behavior in Travel Time Reliability[C]//Proceedings of the 79th Annual Meeting of the Transportation Research Board, Washington D.C,2000.
[55]Chen A, Lo H. K. Yang H,etc. A capacity related reliability for transportation networks [J].Journal of Advanced Transportation,1999,33(2):183-200.
[56]Lomax T,Turner S, Margiotta R. Monitoring Urban Roadways in 2002:Using Archived Operations Data for March 2004 Reliability and Mobility Measurement [R]. Washington, DC:Department of Transportation Federal Highway Administration Office of Operations,2004.
[57]A1-Deek H, Emam E.B. New Methodology for Estimating Reliability in Transportation Networks with Degraded Link Capacities [J].Journal of Intelligent Transportation Systems,2006,10(3):117-129.
[58]Noland R.B, Small K. A, Koskenoja P. M,etc. Simulating travel reliability[J].Regional Science and Urban Economics,1998,28(5):535-564.
[59]冷军强.冰雪条件下城市路网行程时间可靠性研究[D].哈尔滨工业大学,,2010.
[60]张建.城市交通网络行程时间可靠性研究[D].兰州交通大学,2009.
[61]张勇,白玉,杨晓光.城市道路网络的行程时间可靠性[J].系统工程理论与实践,2009,29(8)：171-176.
[62]冷军强,张亚平,赵兴奎.基于广义出行费用的城市路网行程时间可靠性[J].公路交通科技,2010,27(7)：133-137.
[63]Chen A, Yang H, Lo H. K. Capacity Reliability of a Road Network:An Assessment Methodology and Numerical Results.[J].Transportation Research B,2002,36(3):225-252.
[64]Chen H. K, Hsu M. C, Hsieh C. F. Some issues in network capacity reliability [C]//IEEE International Conference on Networking-Sensing and Control, 2004:293-298.
[65]Yang H, Lo H.K., Tang W H. Travel time versus capacity reliability of a road network[C]//Proceedings of the 79th Annual Meeting of the Transportation Research Board, Washington D.C,2000.
[66]Chen A, Tatineni M, Lee D.H. Effect of Route Choice Models on Estimating Network Capacity Reliability [J]. Transportation Research Reeord,2000(1733):493-513.
[67]Lo H. K., Tung Y. K.. Network with degradable links:capacity analysis and design [J].Transportation Research B,2003,37(4):345-363.
[68]陈艳艳,梁颖,杜华兵.可靠度在路网运营状态评价中的应用[J].土木工程学报,2003,36(1)：36-40.
[69]刘海旭,蒲云.弹性需求随机路网的可靠性[J].公路交通科技,2005,,22(7)：97-100.
[70]侯立文,蒋馥.基于路网可靠性的路网服务水平[J].系统工程理论方法应用,2003,12(3)：248-252.
[71]朱顺应,王炜,邓卫.交通网络可靠度及其道路算法研究[J].中国公路学报,2000,13(1)：91-94.
[72]程琳,李强,王京元.城市道路网络容量可靠性(英文)[J].Journal of Southeast University (English Edition),2004,20(2):235-239.
[73]李志纯,朱道立.随机动态交通网络可靠度分析与评价[J].交通运输工程学报,2008,8(1)：106-112.
[74]黎茂盛,王炜,史峰.降级路网的认知及交通流平衡分析模型[J].中国公路学报,2006,19(6)：87-91.
[75]许良,高自友.基于路段能力可靠性的城市交通网络设计[J].中国公路学报,2006,19(2)：86-90.
[76]Nicholson A, Du Z P. Degradable transportation systems:An integrated equilibrium model [J].Transportation Research Part B,1997,31(3):209-223.
[77]Bell M. G. H. A game theory approach to measuring the performance reliability of transport networks [J].Transportation Research B,2000,34(6):533-546.
[78]Sanso B, Soumis F. Communication & transportation network reliability using routing models[J].IEEE Transactions on Reliability,1991,40(1):29-38.
[79]Lee S, Moon B, Asakura Y. Reliability analysis and calculation on large scale transport network [C]//Reliability of Transport Networks. Hertfordshire:RSP Ltd., 2000:173-189.
[80]Sumalee A, Watling D. P, Nakayama S. Reliable network design Problem:the case with uncertain demand and total travel time reliability [J].Transportation Research Record,2006(1964):81-90.
[81]Chootinan P, Wong S. C, Chen A. A reliability-based network design problem[J].Journal of Advanced Transportation,2005,39(3):247-270.
[82]Sanchez-Silva M, Daniels M, Llerasa G, etc. A transport network reliability model for the efficient assignment of resources [J].Transportation Research B,2005, 39(1):47-63.
[83]许宏科,慕巍,焦家华.高速公路动态交通流模型及其参数的分段辨识[J].武汉理工大学学报(交通科学与工程版),2005,29(1)：91-93.
[84]苏岳龙,路鹭,姚丹亚.经典交通流模型在城市车路不均衡发展评价中的应用[J].公路交通科技,2010,27(11)：108-112.
[85]张鹏,刘儒勋.交通流模型的分析评价与模型的改进[J].昆明理工大学学报,2000,25(4)：118-123.
[86]王勇.物流园区动态交通配流研究[D].北京交通大学,2008.
[87]王赛政.动态交通条件下车辆导航系统的最优路径规划方法研究[D].长沙理工大学,2010.
[88]李清泉,李汉武,谢智颖.面向动态路径选择的路段行程时间的分析研究[J].武汉大学学报(信息科学版),2006,31(6)：519-522.
[89]Nie Y.Equilibrium analysis of macroscopic traffic oscillations [J].Transportation Research Part B,2010,44(1):62-72.
[90]Liu Y, Chang G.L. An arterial signal optimization model for intersections experiencing queue spillback and lane blockage [J].Transportation Research Part C,2011,19(1):130-144.
[91]安维胜.混合交通流动力学建模研究[D].西南交通大学,2010.
[92]Greenshields B. D. A study in highway capacity[J]. Highway Research Board Proceedings,1935,14(1):448-477.
[93]Greenberg H. An analysis of traffic flow [J].Operations Research,1959, 7(1):79-85.
[94]Underwood R. T. Speed, volume and density relationship [J].In Quality and theory of traffic flow,1961:141-188.
[95]Newell G. F. Nonlinear effects in the dynamics of car following [J].Operations Research,1961,9(2):209-299.
[96]Pipes L. A. Car-following models and the fundamental diagram of road traffic [J].Transportation Research Part B,1967, 1(1):21-29.
[97]Castillo J.M.D, Benitez F.G. On the functional form of the speed-density relationship-Ⅱ:Empirical investigation [J].Transportation Research Part B,1995, 29(5):391-406.
[98]Ben-Akiva M. Development of a Deployable Real-time Dynamic Traffic Assignment System[R].MIT, Cambridge, MA:Task D Interim Report:Analytical Developments for DTA System,1996.
[99]Daganzo C. F. The cell transmission model:A dynamic representation of highway traffic consistent with the hydrodynamic theory [J].Transportation Research B, 1994,28(4):269-287.
[100]Daganzo C. F. The cell transmission model, part Ⅱ:network traffic [J].Transportation Research B,1995,29(2):79-93.
[101]Szeto W Y. Enhanced Lagged Cell-Transmission Model for Dynamic Traffic Assignment [J].Transportation Research Record,2008(2085):76-85.
[102]Ishak S, Alecsandru C, Seedah D. Improvement and Evaluation of Cell-Transmission Model for Operational Analysis of Traffic Networks:Freeway Case Study [J].Transportation Research Record,2006(1965):171-182.
[103]Daganzo C. F. The lagged cell-transmission model[C]//Proeeedings of the 14th International Symposium on Transportation and Traffic Theory,New York,1999:81-106.
[104]Szeto W. Y. Enhanced Lagged Cell-Transmission Model for Dynamic Traffic Assignment [J].Transportation Research Record,2009(2085):76-85.
[105]Boela R, Mihaylovab L. A compositional stochastic model for real time freeway traffic simulation [J].Transportation Research B,2006,40(4):319-334.
[106]Kim Y, Keller H. On-line traffic flow model applying the dynamic flow-density relation[C]//Proceeding of the IEE Road Transport Information and Control Conference, London:2002:141-145.
[107]Gomes G, Horowitz R. A study of two onramp metering schemed for congested freeways[C]// Proeeedings of the Ameriean Control Conferene, Denver, Colorado.2003:3756-3761.
[108]Gomes G, Horowitz R. Globally optimal solution to the on-ramp metering problem-Part 1[C]//IEEE Intelligent Transportation Systems Conference,WashingtonD.C,2012:509-514.
[109]Muiioz L, Sun X, Horowitz R. Traffic Density Estimation with the Cell Transmission Model [C]//Proceedings of the American Control Conference, Denver, Colorado,2003:3750-3755.
[110]Muiioz L, Sun X, Sun D. Methodological Calibration of the Cell Transmission Model [C]//In 2004 American Control Conference Proceedings, Boston. Massachusetts,2004:798-803.
[111]尚华艳,黄海军,高自友.基于元胞传输模型的可变信息标志选址问题研究[J].物理学报,2007,56(8)：4342-4347.
[112]尚华艳.基于元胞传输模型的实时交通信息研究[D].北京航空航天大学,2008：1.
[113]Ziliaskopoulos A K. A Linear Programming Model for the Single Destination System Optimum Dynamic Traffic Assignment Problem [J]. Transportation Science,2000,34(1):37-49.
[114]Lo H K, Szeto W Y. A cell-based variational inequality formulation of the dynamic user optimal assignment problem [J].Transportation Research B,2002, 36(5):421-443.
[115]Szeto W Y, Lo H K. A cell-based simultaneous route and departure time choice model with elastic demand[J].Transportation Research B,2004,38(7):593-612.
[116]Szeto W Y, Lo H K. Assignment, Non-Equilibrium Dynamic Traffic[C]// Transportation and Traffic Theory. Flow, Dynamics and Human Interaction.16th International Symposium on Transportation and Traffic Theory, USA,2005: 427-445.
[117]Waller S T, Ziliaskopoulos A K. Stochastic Dynamic Network Design Problem [J].Transportation Research Record,2001 (1771):106-113.
[118]Karoonsoontawong A, Waller S T. Dynamic Continuous Network Design Problem[J].Transportation Research Record,2006(1964):104-117.
[119]Karoonsoontawong A, Waller S T. Robust Dynamic Continuous Network Design Problem [J].Transportation Research Record,2007(1981):58-71.
[120]Lo H K. A novel traffic signal control formulation [J].Transportation Research Part A,1999,33(6):433-448.
[121]Lo H K. A Cell-Based Traffic Control Formulation:Strategies and Benefits of Dynamic Timing Plans [J].Transportation Science,2001,35(2):148-164.
[122]Lo H K, Chang E, Chan Y C. Dynamic network traffic control[J].Transportation Research A,2001,35(8):721-744.
[123]Chow A H F, Lo H K. Sensitivity analysis of signal control with physical queuing: Delay derivatives and an application [J].Transportation Research B,2007, 41(4):462-477.
[124]Asano M, Sumalee A, Kuwahara M. Dynamic Cell Transmission-Based Pedestrian Model with Multidirectional Flows and Strategic Route Choices[J].Transportation Research Record,2007(2039):42-49.
[125]Dixita V.V, Radwana E. Hurricane Evacuation:Origin, Route, and Destination[J].Journal of Transportation Safety & Security,2009,1(1):74-84.
[126]胡晓健,王炜,盛慧.基于可变元胞传输模型的城市道路交通流估计方法[J].交通运输系统工程与信息,2010,10(4)：73-78.
[127]曾建勤,王家捷,刘琨.基于细胞模型及多目标优化的交叉口信号控制(英文)[J].中国科学技术大学学报,2005,35(2)：284-290.
[128]姬杨蓓蓓,张小宁,孙立军.基于元胞传输模型的交通事件消散建模[J].重庆交通大学学报(自然科学版),2008,27(3)：442-445.
[129]崔建勋,安实,崔娜.基于元胞传输模型的区域疏散动态交通分配[J].哈尔滨工业大学学报,2010,42(1)：123-127.
[130]Yperman I. The link transmission model for dynamic network loading [D].Katholieke Universiteit Leuven,2007.
[131]龙建成,高自友,赵小梅.基于路段传输模型的道路出口渠化[J].吉林大学学报(工学版),2009,39(S2)：41-46.
[132]Lighthill M J,Whitham J B. On kinematic waves.I.flow modeling in long rivers. II. A theory of traffic flow on long crowded roads [J].Proceedings of the Royal Society A,1955(229):291-345.
[133]Richards P. I. Shock waves on the highway [J].Operation Research,1956, 4(1):42-51.
[134]Khattak A J, Kanafani A, Colletter E L. Stated And Reported Route Diversion Behavior:Implications On The Benefits Of ATIS[R]. UC Berkeley, California Partners for Advanced Transit and Highways (PATH), Institute of Transportation Studies.1994.
[135]Khattak A J, Schofer J L, Koppelman F S. Commuters' enroute diversion and return decisions:Analysis and implications for advanced traveler information systems[J].Transportation Research A,1993,27(2):101-111.
[136]Polydoropoulou A, Ben-Akiva M, Khattak A. Modeling Revealed and Stated En-Route Travel Response to Advanced Traveler Information Systems [J].Transportation Research Record,1996(1537):38-45.
[137]Madanat S, Yang C. Y, Yen Y. Analysis of Stated Route Diversion Intentions under ATIS Using Latent Variable Modeling[C]//Proceedings of the 74th Annual Meeting of the Transportation Research Board. Washington D.C,1995.
[138]刘海旭.城市交通网络可靠性研究[D].西南交通大学,2004.
[139]Watling D. User equilibrium traffic network assignment with stochastic travel times and late arrival penalty [J].European Journal of Operational Research,2006, 3(175):1539-1556.