路网环境下高速公路网容量提升及关键技术研究
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摘要
高速公路作为经济社会发展的重要基础设施之一，是覆盖度广、机动性灵活、服务质量高的交通运输方式。在基本现代化新征程中，需要高速公路更好地发挥先行军作用，为产业转型和结构调整增进提供先导性、基础性支撑。特别是随着高速公路基础设施由大规模建设阶段向经营管理阶段逐步转移，以及高速公路路网管理和服务水平全面提升的大背景下，给高速公路交通建设、养护和管理提出了更高的要求。因此，需要高速公路在现代化建设进程中，能够更好地顺应人民群众安全便捷舒适出行的新期待，以信息化智能化为引领，进一步提升公路交通行业创新能力，适应产业结构调整、新型城镇建设和生态文明建设对高速公路发展的新要求。综上，在完善高速公路网络基础设施网络的同时，系统地研究高速公路网路网容量提升关键技术和方法，制定合理高效的路网容量提升方案，将有助于改善路网环境下的运输能力和效率，发挥其可达性、安全性、高效性、衔接性和舒适性等五大交通功能，提升高速公路网系统运行效率和服务质量。
本文以高速公路路网容量为研究对象，在既有高速公路理论研究的基础上，重新诠释高速公路交通功能，提出了以可达性、安全性、高效性、衔接性和舒适性为五大功能特征的交通功能基本特征。在此基础上，结合高速公路网交通功能的内涵和基本特征，在分析高速公路交通功能影响因素的基础上，重点研究了路网环境下高速公路路网容量提升的关键技术和方法。论文的主要工作包括以下几方面：
（1）首先对高速公路网交通流特性进行了详细分析。研究车辆行驶在高速公路基本路段时的车道变换、车速离散性等微观交通现象，在对称双车道元胞自动机模型的基础上，提出了一种引入了车辆速度控制规则、车道宽度控制规则和弹性安全换道间距规则的高速公路基本路段交通流元胞自动机模型。该模型通过控制速度变量和车道宽度变量，分析了大车比例及所制定的车辆换道规则等条件对高速公路路段交通流特性的影响，并利用MATLAB软件对不同条件下高速公路路段基本特性进行仿真。分析结果表明：所建模型能够模拟车辆在高速公路基本路段区域的时空变化特征；高速公路基本路段的基本特性受车速离散性、车道宽度和车道变换三者共同的影响较大，同时呈现出不同的交通拥挤等现象；提出的元胞自动机模型将可改善道路的通行能力、提高道路资源的利用效率。
（2）在分析整个高速公路路网容量影响因素的基础上，考虑高速公路交通出行特性，从不同的交通出行需求形态出发，基于双层数学规划模型，构建了路网容量基本模型、路网容量极限模型和路网容量扩张模型。针对三种路网容量模型，设计了基于多智能系统的蚁群算法求解路网容量基本模型，基于局部线性优化的遗传模拟退火算法求解路网容量极限模型，基于相继平均法的遗传算法求解路网容量扩张模型。最后以山东省高速公路为例对模型进行了实例验证。分析结果表明：通过路网容量基本模型可以计算出路网的实际高速网容量；极限模型可以进一步挖掘路网容量的潜力，得到更为充分利用下的路网容量；扩张模型通过确定路网中的瓶颈路段并进行改造，进一步的提升网容量。三种情形下的路网容量模型为最优的路网规划、路网改造和研究路网容量提供了有效的理论依据。
（3）以复杂网络理论为基础，对高速公路交通网络容量的可靠性进行了分析。首先，构建了高速公路网络的拓扑结构，分析了高速公路网拓扑结构的统计特性。其次，阐释了网络连通可靠性、时间可靠性和容量可靠性。针对容量可靠性，构建了高速公路网容量可靠性双层规划模型，以满足路段容量的约束条件作为双层规划模型中的上层规划，以随机用户均衡模型作为双层规划模型中的下层规划，采用灵敏度分析法和蒙特卡洛仿真模拟相结合的求解方法对其进行求解。最后，对网络进行随机和蓄意两种攻击方式，考察在两种攻击下网络的抗毁性与鲁棒性特性，并给出了实例分析。分析结果表明：服务水平越高，OD对的容量可靠性越大，当服务水平不断降低时，容量可靠性降低速度变快。路网容量可靠性模型能够较好地体现不同路网容量要求下的路网容量可靠性，可为高速公路网规划、后期运营管理及路网扩容提供理论借鉴。
（4）在分析高速公路区域降级路网供需平衡的基础上，基于高速公路区域降级路网交通流平衡理论，提出了路网环境下高速公路网容量提升控制技术。通过分析高速公路网结构的脆弱性与鲁棒性，建立瓶颈路段、重要节点（枢纽）影响范围的路网拓扑结构（区域降级路网）。在此基础上，提出了路网环境下高速公路主线控制方法和匝道控制方法。根据驾驶员对出行路径判断的不确定性，引入后悔理论视角下的出行选择行为，建立了出行路径选择的随机后悔最小化模型，与随机效用最大化模型进行对比，分析出行路径选择结果的差异性。最后，以山东省高速公路为例对其容量提升控制技术进行了仿真验证。分析结果表明：对于出行者在不确定条件下对其所选择的出行路判断的不确定性，采取容错技术下的出行路径诱导是有效的，在路网环境下采取路径诱导、主线控制和匝道控制能够使得高速公路网容量提高。
As one of the important infrastructure, highway is a transport mode of a cover wide,mobility, quality of service and high flexible and also the foundation of moderncomprehensive transportation system. In the new journey of basic modernization, needhighway to play the leading role better, to provide a basic support of the industry transition,structure adjustment and development of agglomeration. Especially with the highwayinfrastructure from large-scale construction stage to transfer management stage, as well asagainst this background which highway network management and service levels improve,significantly enhance the ability of innovation of science and technology, highway and naturalenvironment, humanistic environment, the rapid development of regional urban, highway notonly carries road traffic, but also carries the city traffic which put forward higher requirementsof the highway traffic construction, maintenance and management. Therefore, the highway inthe modernization process can look forward better to adapt the new people safe andconvenient travel in comfort, make the intelligent information technology as the guide, tofurther improve the highway traffic industry innovation ability, the new requirements for thedevelopment of highway industry structure adjustment, the new town construction andecological civilization construction. In conclusion, at the same time with the improvement ofhighway network infrastructure network, study related the technology and method to improvethe traffic function of highway network systematically, draw up reasonable and efficienttraffic function promotion plan, will help to improve the road network transport capacity andefficiency, develop its accessibility, safety, efficiency, convergence and comfort of five trafficfunction, to improve the highway network system operation efficiency and service quality.
The capacity of highway network was studied in the paper, the traffic function ofhighway was reinterpreted based on the existing theoretical research of highway. Thetransport capacity and efficiency of a given traffic conditions were also described in the paper.Five functional characteristics of safety, efficiency, connectivity and comfort were proposedas the basic characterstics of the traffic function. Therefore, on the basis of the analysis ofhighway traffic function and the connotation&features of highway network traffic capability, the key techniques and methods of enhancing the capacity of the road network under thehighway network environment were in-depth studied. The paper‘s main work includes thefollowing aspects:
(1) The traffic flow characteristics of highway network were analyzed in detail. Themicroscopic traffic phenomena of lane changing, speed discrete for vehicle traveling onhighway was researched in the paper. On the basis of a symmetric double-lane cellularautomaton model, the rules of vehicle speed controlling, lane width controlling, Flexiblesecurity lane change spacing, a basic freeway traffic flow cellular automaton model waspresented. By controlling the variable of speed and lane width, the impact on highwaycapacity of the proportion of carts and formulated rules, lane changing conditions wasanalysized, and MATLAB was used in the paper to simulate the highway capacity underdifferent conditions. The results showed that: the model can simulate the temporal and spatialof vehicles on the basic section of highway, Basic freeway capacity by discrete speed, lanewidth and lane change influenced the three common, while showing a different phenomenasuch as traffic congestion, Cellular automata model proposed will improve road capacity,improve resource utilization efficiency of road.
(2) On the basic of analysis the factors affecting the highway network capacity,considering the traffic features, the different forms of transportation demand and the bi-levelmathematical programming model, the paper build the general road network capacity model,the limit road network capacity model and the network capacity expansion model. For thethree kinds of road network capacity, the paper design the limit model to solve the networkcapacity based on local linear optimization genetic simulated annealing algorithm, and theexpansion model to solve the network capacity based on the successive average method ofgenetic algorithm model. Shandong provincial highway was as an example to validate themodel. The results show that, the capacity of the road network through the basic model cancalculate the actual speed network capacity Way network; limits of the model can be furthertap the potential of the road network capacity, the capacity of the road network to get moreleverage under; expansion of road network model by determining the bottlenecks and roadreconstruction, further improved network capacity. Road network capacity model under threescenarios for the optimal planning of road network, road network reconstruction and research capacity of the road network provides an effective theory.
(3) Based on complex network theory the reliability of highway network capacity can beanalyzed. Firstly, the highway network topology structure is constructed and the statisticalproperties of the highway network topology are analyzed. Secondly, the connectivityreliability, travel time reliability as well as capacity reliability is elucidated. For the capacityreliability we establish a bi-level programming model, in which road segment capacityconstraint is chose as the upper planning and stochastic user equilibrium model as the lowerplanning. A combination of sensitivity analysis and Monte Carlo simulation can solve thebi-level programming model. Finally, the network invulnerability and robustness charactersare studied under the random attack and deliberate attack. Through a case study we canachieve that, the higher level of service, the greater capacity reliability of OD. Also it can befound that when level of service decrease, the reducing speed of capacity reliability increaserapidly. Road network capacity reliability model can give a better reflection of road networkcapacity reliability in different road network capacity requirements. At the same time it cangive a theoretical reference for highway network planning, management, operation, togetherwith network expansion.
(4) After analyzing the balance between supply and demand of regional downgradehighway network, on the basis of regional downgrade highway network traffic flowequilibrium theory, this paper proposed key technologies and methods to enhance trafficfunction under highway network environment. By analyzing the vulnerability and robustnessof highway network structure, highway network topologies (regional downgrade highwaynetwork) are established which can reflect the affected area of bottleneck sections andimportant nodes (hubs) of the road. And then, highway mainline control and ramp controlmethods under highway network environment are proposed. Taking the uncertainty whendrivers choose their travel into account, using travel choice behavior under regret theoryperspective, route choice random regret minimization model are established and differences inroute choice results are analyzed comparing with random utility maximization model. Theresults show that, route guidance using fault-tolerant technology is effective dealing with theuncertainty when travelers choose their travel paths under uncertain conditions.
本文以高速公路路网容量为研究对象，在既有高速公路理论研究的基础上，重新诠释高速公路交通功能，提出了以可达性、安全性、高效性、衔接性和舒适性为五大功能特征的交通功能基本特征。在此基础上，结合高速公路网交通功能的内涵和基本特征，在分析高速公路交通功能影响因素的基础上，重点研究了路网环境下高速公路路网容量提升的关键技术和方法。论文的主要工作包括以下几方面：
（1）首先对高速公路网交通流特性进行了详细分析。研究车辆行驶在高速公路基本路段时的车道变换、车速离散性等微观交通现象，在对称双车道元胞自动机模型的基础上，提出了一种引入了车辆速度控制规则、车道宽度控制规则和弹性安全换道间距规则的高速公路基本路段交通流元胞自动机模型。该模型通过控制速度变量和车道宽度变量，分析了大车比例及所制定的车辆换道规则等条件对高速公路路段交通流特性的影响，并利用MATLAB软件对不同条件下高速公路路段基本特性进行仿真。分析结果表明：所建模型能够模拟车辆在高速公路基本路段区域的时空变化特征；高速公路基本路段的基本特性受车速离散性、车道宽度和车道变换三者共同的影响较大，同时呈现出不同的交通拥挤等现象；提出的元胞自动机模型将可改善道路的通行能力、提高道路资源的利用效率。
（2）在分析整个高速公路路网容量影响因素的基础上，考虑高速公路交通出行特性，从不同的交通出行需求形态出发，基于双层数学规划模型，构建了路网容量基本模型、路网容量极限模型和路网容量扩张模型。针对三种路网容量模型，设计了基于多智能系统的蚁群算法求解路网容量基本模型，基于局部线性优化的遗传模拟退火算法求解路网容量极限模型，基于相继平均法的遗传算法求解路网容量扩张模型。最后以山东省高速公路为例对模型进行了实例验证。分析结果表明：通过路网容量基本模型可以计算出路网的实际高速网容量；极限模型可以进一步挖掘路网容量的潜力，得到更为充分利用下的路网容量；扩张模型通过确定路网中的瓶颈路段并进行改造，进一步的提升网容量。三种情形下的路网容量模型为最优的路网规划、路网改造和研究路网容量提供了有效的理论依据。
（3）以复杂网络理论为基础，对高速公路交通网络容量的可靠性进行了分析。首先，构建了高速公路网络的拓扑结构，分析了高速公路网拓扑结构的统计特性。其次，阐释了网络连通可靠性、时间可靠性和容量可靠性。针对容量可靠性，构建了高速公路网容量可靠性双层规划模型，以满足路段容量的约束条件作为双层规划模型中的上层规划，以随机用户均衡模型作为双层规划模型中的下层规划，采用灵敏度分析法和蒙特卡洛仿真模拟相结合的求解方法对其进行求解。最后，对网络进行随机和蓄意两种攻击方式，考察在两种攻击下网络的抗毁性与鲁棒性特性，并给出了实例分析。分析结果表明：服务水平越高，OD对的容量可靠性越大，当服务水平不断降低时，容量可靠性降低速度变快。路网容量可靠性模型能够较好地体现不同路网容量要求下的路网容量可靠性，可为高速公路网规划、后期运营管理及路网扩容提供理论借鉴。
（4）在分析高速公路区域降级路网供需平衡的基础上，基于高速公路区域降级路网交通流平衡理论，提出了路网环境下高速公路网容量提升控制技术。通过分析高速公路网结构的脆弱性与鲁棒性，建立瓶颈路段、重要节点（枢纽）影响范围的路网拓扑结构（区域降级路网）。在此基础上，提出了路网环境下高速公路主线控制方法和匝道控制方法。根据驾驶员对出行路径判断的不确定性，引入后悔理论视角下的出行选择行为，建立了出行路径选择的随机后悔最小化模型，与随机效用最大化模型进行对比，分析出行路径选择结果的差异性。最后，以山东省高速公路为例对其容量提升控制技术进行了仿真验证。分析结果表明：对于出行者在不确定条件下对其所选择的出行路判断的不确定性，采取容错技术下的出行路径诱导是有效的，在路网环境下采取路径诱导、主线控制和匝道控制能够使得高速公路网容量提高。
As one of the important infrastructure, highway is a transport mode of a cover wide,mobility, quality of service and high flexible and also the foundation of moderncomprehensive transportation system. In the new journey of basic modernization, needhighway to play the leading role better, to provide a basic support of the industry transition,structure adjustment and development of agglomeration. Especially with the highwayinfrastructure from large-scale construction stage to transfer management stage, as well asagainst this background which highway network management and service levels improve,significantly enhance the ability of innovation of science and technology, highway and naturalenvironment, humanistic environment, the rapid development of regional urban, highway notonly carries road traffic, but also carries the city traffic which put forward higher requirementsof the highway traffic construction, maintenance and management. Therefore, the highway inthe modernization process can look forward better to adapt the new people safe andconvenient travel in comfort, make the intelligent information technology as the guide, tofurther improve the highway traffic industry innovation ability, the new requirements for thedevelopment of highway industry structure adjustment, the new town construction andecological civilization construction. In conclusion, at the same time with the improvement ofhighway network infrastructure network, study related the technology and method to improvethe traffic function of highway network systematically, draw up reasonable and efficienttraffic function promotion plan, will help to improve the road network transport capacity andefficiency, develop its accessibility, safety, efficiency, convergence and comfort of five trafficfunction, to improve the highway network system operation efficiency and service quality.
The capacity of highway network was studied in the paper, the traffic function ofhighway was reinterpreted based on the existing theoretical research of highway. Thetransport capacity and efficiency of a given traffic conditions were also described in the paper.Five functional characteristics of safety, efficiency, connectivity and comfort were proposedas the basic characterstics of the traffic function. Therefore, on the basis of the analysis ofhighway traffic function and the connotation&features of highway network traffic capability, the key techniques and methods of enhancing the capacity of the road network under thehighway network environment were in-depth studied. The paper‘s main work includes thefollowing aspects:
(1) The traffic flow characteristics of highway network were analyzed in detail. Themicroscopic traffic phenomena of lane changing, speed discrete for vehicle traveling onhighway was researched in the paper. On the basis of a symmetric double-lane cellularautomaton model, the rules of vehicle speed controlling, lane width controlling, Flexiblesecurity lane change spacing, a basic freeway traffic flow cellular automaton model waspresented. By controlling the variable of speed and lane width, the impact on highwaycapacity of the proportion of carts and formulated rules, lane changing conditions wasanalysized, and MATLAB was used in the paper to simulate the highway capacity underdifferent conditions. The results showed that: the model can simulate the temporal and spatialof vehicles on the basic section of highway, Basic freeway capacity by discrete speed, lanewidth and lane change influenced the three common, while showing a different phenomenasuch as traffic congestion, Cellular automata model proposed will improve road capacity,improve resource utilization efficiency of road.
(2) On the basic of analysis the factors affecting the highway network capacity,considering the traffic features, the different forms of transportation demand and the bi-levelmathematical programming model, the paper build the general road network capacity model,the limit road network capacity model and the network capacity expansion model. For thethree kinds of road network capacity, the paper design the limit model to solve the networkcapacity based on local linear optimization genetic simulated annealing algorithm, and theexpansion model to solve the network capacity based on the successive average method ofgenetic algorithm model. Shandong provincial highway was as an example to validate themodel. The results show that, the capacity of the road network through the basic model cancalculate the actual speed network capacity Way network; limits of the model can be furthertap the potential of the road network capacity, the capacity of the road network to get moreleverage under; expansion of road network model by determining the bottlenecks and roadreconstruction, further improved network capacity. Road network capacity model under threescenarios for the optimal planning of road network, road network reconstruction and research capacity of the road network provides an effective theory.
(3) Based on complex network theory the reliability of highway network capacity can beanalyzed. Firstly, the highway network topology structure is constructed and the statisticalproperties of the highway network topology are analyzed. Secondly, the connectivityreliability, travel time reliability as well as capacity reliability is elucidated. For the capacityreliability we establish a bi-level programming model, in which road segment capacityconstraint is chose as the upper planning and stochastic user equilibrium model as the lowerplanning. A combination of sensitivity analysis and Monte Carlo simulation can solve thebi-level programming model. Finally, the network invulnerability and robustness charactersare studied under the random attack and deliberate attack. Through a case study we canachieve that, the higher level of service, the greater capacity reliability of OD. Also it can befound that when level of service decrease, the reducing speed of capacity reliability increaserapidly. Road network capacity reliability model can give a better reflection of road networkcapacity reliability in different road network capacity requirements. At the same time it cangive a theoretical reference for highway network planning, management, operation, togetherwith network expansion.
(4) After analyzing the balance between supply and demand of regional downgradehighway network, on the basis of regional downgrade highway network traffic flowequilibrium theory, this paper proposed key technologies and methods to enhance trafficfunction under highway network environment. By analyzing the vulnerability and robustnessof highway network structure, highway network topologies (regional downgrade highwaynetwork) are established which can reflect the affected area of bottleneck sections andimportant nodes (hubs) of the road. And then, highway mainline control and ramp controlmethods under highway network environment are proposed. Taking the uncertainty whendrivers choose their travel into account, using travel choice behavior under regret theoryperspective, route choice random regret minimization model are established and differences inroute choice results are analyzed comparing with random utility maximization model. Theresults show that, route guidance using fault-tolerant technology is effective dealing with theuncertainty when travelers choose their travel paths under uncertain conditions.
引文
[1] Lighthill M J, Whitham G B. On kinematic waves. II. A theory of traffic flow on long crowdedroads[J]. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences,1955,229(1178):317-345
[2] Richards P I. Shock waves on the highway[J]. Operations research,1956,4(1):42-51
[3] Payne H J. Models of freeway traffic and control[J]. Mathematical models of public systems,1971:74-82
[4] Gerlough D L, Huber M J. Traffic flow theory[R].1976:39-51
[5] Reuschel A. Vehicle movements in a platoon[J]. Oesterreichisches Ingenieur-Archir,1950,4:193-215.
[6] Pipes L A. An operational analysis of traffic dynamics[J]. Journal of applied physics,2004,24(3):274-281
[7] Chandler R E, Herman R, Montroll E W. Traffic dynamics: studies in car following[J]. Operationsresearch,1958,6(2):165-184
[8] Herman R, Montroll E W, Potts R B, et al. Traffic dynamics: analysis of stability in car following[J].Operations research,1959,7(1):86-106
[9] Newell G F. Nonlinear effects in the dynamics of car following[J]. Operations Research,1961,9(2):209-229
[10] Bando M, Hasebe K, Nakayama A, et al. Dynamical model of traffic congestion and numericalsimulation[J]. Physical Review E,1995,51:1035-1042
[11] Bando M, Hasebe K, Nakanishi K, et al. Analysis of optimal velocity model with explicit delay[J].arXiv preprint patt-sol/9805002,1998:129-133
[12] Helbing D, Tilch B. Generalized force model of traffic dynamics[J]. Physical Review E,1998,58(1):133
[13] Treiber M, Hennecke A, Helbing D. Derivation, properties, and simulation of a gas-kinetic-based,nonlocal traffic model[J]. Physical Review E,1999,59(1):239
[14] Jiang R, Wu Q, Zhu Z. Full velocity difference model for a car-following theory[J]. Physical Review E,2001,64(1):017101
[15] Jiang R, Wu Q S. First-and second-order phase transitions from free flow to synchronized flow.Physics A,2003,322:676-684
[16] Jiang R, Wu Q S. Traffic patterns induced by the merging of two moving car platoonswith differentdensities. J. Phys. A,2002,35:2145-2157
[17] Konishi K, Kokame H, Hirata K. Coupled map car-following model and its delayed-feedbackcontrol[J]. Physical Review E,1999,60(4):4000
[18] Zhao X, Gao Z. A control method for congested traffic induced by bottlenecks in the coupled mapcar-following model[J]. Physica A: Statistical Mechanics and its Applications,2006,366:513-522
[19] Ge H X, Dai S Q, Dong L Y, et al. Stabilization effect of traffic flow in an extended car-followingmodel based on an intelligent transportation system application[J]. Physical Review E,2004,70(6):066134
[20] Ge H X, Dai S Q, Dong L Y. An extended car-following model based on intelligent transportationsystem application[J]. Physica A: Statistical Mechanics and its Applications,2006,365(2):543-548
[21] Ge H X, Cheng R J, Dai S Q. KdV and kink–antikink solitons in car-following models[J]. Physica A:Statistical Mechanics and its Applications,2005,357(3):466-476
[22] Wolfram S. Statistical mechanics of cellular automata[J]. Reviews of modern physics,1983,55(3):601
[23] Wolfram S. Theory and applications of cellular automata[J].1986:28-45
[24] Wolfram S. Cellular automaton fluids1: Basic theory[J]. Journal of Statistical Physics,1986,45(3-4):471-526
[25] Wolfram S. Cellular automata and complexity: collected papers[M]. Reading: Addison-Wesley,1994.
[26] Cremer M, Ludwig J. A fast simulation model for traffic flow on the basis of Boolean operations[J].Mathematics and Computers in Simulation,1986,28(4):297-303
[27] Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic[J]. Journal de physique I,1992,2(12):2221-2229
[28] Biham O, Middleton A A, Levine D. Self organization and a dynamical transition in traffic flowmodels[J]. arXiv preprint cond-mat/9206001,1992:58-89
[29] Benjamin S C, Johnson N F, Hui P M. Cellular automata models of traffic flow along a highwaycontaining a junction[J]. Journal of Physics A: Mathematical and General,1996,29(12):3119
[30] Nishinari K, Takahashi D. Multi-value cellular automaton models and metastable states in a congestedphase[J]. Journal of Physics A: Mathematical and general,2000,33(43):7709
[31] Barlovic R, Santen L, Schadschneider A, et al. Metastable states in cellular automata for traffic flow[J].The European Physical Journal B-Condensed Matter and Complex Systems,1998,5(3):793-800
[32] K. Nagel, M. Schreckenberg. A Cellular Automaton Model for Freeway Traffic. Journal de Physique I.1992,2(12):2221-2229
[33] D.E. Wolf. Cellular Automata for Traffic Simulations. Physica A: Statistical Mechanics and itsApplications.1999,263(1):438-451
[34]薛郁,董力耘,戴世强.一种改进的一维元胞自动机交通流模型及减速概率的影响.物理学报.2001,50(3):445-449
[35] Y. Liu. A Cellular Automaton Traffic Flow Model with Advanced Decelerations. MathematicalProblems in Engineering.2012,40(1):2012-2026
[36] A. Clarridge, K. Salomaa. Analysis of a Cellular Automaton Model for Car Traffic with a Slow-to-StopRule. Theoretical Computer Science.2010,411(38):3507-3515
[37] Y.-S. Qian, W.-J. Li, J.-W. Zeng, M. Wang, J.-W. Du, X.-P. Guang. Cellular Automaton Models ofHighway Traffic Flow Considering Lane-Control and Speed-Control. Communications in TheoreticalPhysics.2011,56:785-790
[38] M. Takayasu, H. Takayasu.1/F Noise in a Traffic Model. Fractals.1993,1(04):860-866
[39] S.C. Benjamin, N.F. Johnson, P. Hui. Cellular Automata Models of Traffic Flow Along a HighwayContaining a Junction. Journal of Physics A: Mathematical and General.1996,29(12):3119-3129
[40] R. Barlovic, T. Huisinga, A. Schadschneider, M. Schreckenberg. Open Boundaries in a CellularAutomaton Model for Traffic Flow with Metastable States. Physical Review E.2002,66(4):46-58
[41] M. Fukui, Y. Ishibashi. Traffic Flow in1d Cellular Automaton Model Including Cars Moving withHigh Speed. Journal of the Physical Society of Japan.1996,65(6):1868-1870
[42] D. Chowdhury, D.E. Wolf, M. Schreckenberg. Particle Hopping Models for Two-Lane Traffic withTwo Kinds of Vehicles: Effects of Lane-Changing Rules. Physica A: Statistical Mechanics and itsApplications.1997,235(3):417-439
[43]郑亮,马寿峰,贾宁.基于驾驶员行为的元胞自动机模型研究.物理学报.2010,59(7):4490-4498
[44]雷丽,薛郁,戴世强.交通流的一维元胞自动机敏感驾驶模型.物理学报.2003,52(9):2121-2126
[45]郑华荣,吴超仲,马晓凤.考虑驾驶愤怒的元胞自动机交通流模型.武汉理工大学学报(交通科学与工程版).2013,37(3):617-621
[46]华雪东,王炜,王昊.考虑驾驶心理的城市双车道交通流元胞自动机模型.物理学报.2011,60(8):398-405
[47]赵韩涛,毛宏燕.有应急车辆影响的多车道交通流元胞自动机模型.物理学报.2013(006):45-52
[48] K. Nagel, D.E. Wolf, P. Wagner, P. Simon. Two-Lane Traffic Rules for Cellular Automata: ASystematic Approach. Physical Review E.1998,58(2):1425-1435
[49] K. Rawat, V.K. Katiyar, P. Gupta. Two-Lane Traffic Flow Simulation Model Via Cellular Automaton.International Journal of vehicular technology.2011,2012(1):130-136
[50] J. Luo, Y. Qian, J. Zeng, X. Shao, W. Guo. Study on Security Features of Freeway Traffic Flow withCellular Automata Model-Taking the Number of Overtake as a Example. Measurement.2013,46(6):2035-2042
[51]王永明,周磊山,吕永波.基于元胞自动机交通流模型的车辆换道规则.中国公路学报.2008,21(1):89-93
[52] A. Schadschneider, D. Chowdhury, E. Brockfeld, K. Klauck, L. Santen, J. Zittartz. A New CellularAutomaton Model for City Traffic. In: Traffic and Granular Flow: Springer,2000:437-442
[53] Q. Meng, J. Weng. An Improved Cellular Automata Model for Heterogeneous Work Zone Traffic.Transportation research part C: emerging technologies.2011,19(6):1263-1275
[54] I. Spyropoulou. Modelling a Signal Controlled Traffic Stream Using Cellular Automata. Transportationresearch part C: emerging technologies.2007,15(3):175-190
[55]周奚召,刘灿齐,杨佩昆.高峰时段城市道路网时空资源和交通空间容量[J].同济大学学报,1996,24(4):392-397
[56]杨涛,程万里.城市交通网络广义容量应用研究—以南京市为例[J].东南大学学报,1992,22(5):82-84
[57]苗拴明,赵英.对时空消耗概念下路网广义容量计算方法的修正.城市规划汇刊1994(3):98-102
[58]陈春妹.路网容量研究[D].北京:北京工业大学博士论文,2002
[59]杨涛,徐吉谦.运输网络极大流的一种新算法[J].土木工程学报.1991,24(1):12-15
[60] Masao Fukushima. On the dual approach to the traffic assignment problem. Transpn Res, B Vol.18B,No3.1984,245-255
[61] Hai Yang, Michael, G.H.Bell, Qiang Meng. Modeling the capacity and level of urban transportationnetworks. Transportation Research Part B34(2000):132-146
[62]高自友,张好智,孙会君.城市交通网络设计问题中双层规划模型、方法及应用[J].土交通运输系统工程与信息,2004.4(1):33-36
[63] Forian M. and Nguyen S. a method for computing network equilibrium with elastic demands. TranspnRes.10.1976:37-57
[64] Florian M. An improved liners approximation algorithm for the network equilibrium (packet switching)problem. Proc, of the1977IEEE Conf. On Decision&Control,1977:812-818
[65] Masao Fukushima, On the Dual Approach to the Traffic Assignment Problem [J], Transpn Res, B Vol.18B.No.3.1984,P245-255
[66] J. H. Wu, Y. Chen and M. Florian, The Continues Dynamic Network Loading Problem: AMathematical Formulation and Solution Method[J], Transportation research PartB,1998,32(1):173-187
[67] Alfredo Garcia, Daniel Resume, Robert L. Smith, Fictitious play for finding system optimal routingsin dynamic traffic networks [J], Transportation research PartB,2000,34(1):147-156
[68] Byung-wook Wie, Dynamic Congestion Pricing Models for General Traffic Networks, Transportationresearch PartB,1998,32(1):313-327
[69] Ford.L.R.Jr, Fulkerson.D.R. Maximal flow through a network [J]. Canadian journal of mathematics,1956:399-404
[70]杨涛.运输网络极大流动态诊断模型[J].中国公路学报,1991,4(1):72-77
[71] FukushimaM.A modified Frank-Wolfe algorithm for solving the traffic assignment problem.Department of Appl. Math. And Physics, Kyoto University, Kyoto, Japan. To appear in Transpn Trs.1983:21-39
[72] Gartner M.H. Optimal traffic assignment with elastic demands: A review[J]. Transpn Sci.,1980,14(1):174-208
[73] William H.K..Lam, Member, ASCE, and Yangping Zhang, Capacity-Constrained Traffic Assignmentin Networks with Residual Queues[J], Journal of Transportation Engineering,2000(1):121-128
[74] Y.W.XU, J.H.WU, M.Florian, P.Marcotte, and D.L.Zhu, Advances in the Continuous DynamicNetwork Loading Problem, Transportation Science,1999.11, Vol.33,No.4:321-323
[75]杨晓光,褚浩然.错峰出行对城市交通的影响分析[J].同济大学学报:自然科学版,2006,34(7):899-903
[76]徐寅峰,余海燕,苏兵等.基于时间和路径偏好的交通流分配模型与诱导策略[J].系统工程理论与实践,2012,32(10):2306-2314
[77]丁以中,交通运输网络规划[M],大连海事大学出版社,1999
[78]李旭宏,田峰,顾正华.城市道路网供应分析技术[J].交通运输工程学报.2002.6:12-16
[79]李硕,黎莉.公路网狭义总容量理论及模型[J].湖南大学学报(自然科学版),1999.26(1):08-12
[80] Paguette RL. Transportation Engineering, planning and Design,2ndedu. New York: John Wiiley Press,1982:66-68
[81] Blunden WR,Black JA. The land use-transport System. Oxford, England:Pergem on Press,1984
[82]李炳林.基于路网服务水平的路网容量研究[D].长沙：长沙理工大学,2008
[83]张华.区域高速公路网优化布局与调整策略研究[D].武汉：华中科技大学,2009
[84] H. Chen, J.B. Zhou, Y. Wu, et al. Modeling of Road Network Capacity Research in Urban CentralArea[J]. Applied Mechanics and Materials.2011,40:778-784
[85] Watts, D.J., Strogatz, S.H.. Collective dynamics of small-world‘net-works[J]. Nature,1998,393(6684):409-10
[86] Barabasi A.-L. and Albert R.. Emergence of scaling in random networks[J]. Science,1999,208:509-512
[87] Albert R. and Barabasi A.-L.. Statistical mechanics of complex networks[J]. Rev. Mod. Phys.,2002,74:47-97
[88] Holme Petter, Beom Jun kim, Chang No Yoon and Seung Kee Han. Attack vulnerability of complexnetworks[J]. Physical Review E,2002,65(1):056-076
[89] He S, Li S, Ma H. Effect of edge removal on topological and functional robustness of complexnetworks[J]. Physica A: Statistical Mechanics and its Applications,2009,388(11):2243-2253.
[90] Crucitti Paolo, Latora Vito and Porta Centrality measures in spatial networks of urban streets[J].Physical Review E,2006,2:110-115.
[91] Wu J.J., Gao Z.Y., Sun H.J., and Huang H.J.. Congestion in different topologies of traffic networks[J].Europhysics Letters,2006,74(3):560-566.
[92] Latora V, Marchiori M. Is the Boston subway a small-world network [J]. Physica A(50378-4371),2002,314(1):109-113
[93]李英，周伟，郭世进.上海公共交通网络复杂性分析[J].系统工程，2007，25(l):38-41
[94] Sienkiewicz J. Holyst J A.Public transport systems in Poland: from Bialystok to Zielona Gora by busand tram using universal statistics of complex networks[J]. Acta Physica PolonicaB,2005,36(5):1771-1778
[95]张晨，张宁.上海市公交网络拓扑性质研究[J].上海理工大学学报,2006,28(5):489-94.
[96]高自友，吴建军，毛保华，黄海军.交通运输网络复杂性及其相关问题的研究[J].交通运输系统工程与信息,2005,5(2):79-84
[97]任华玲,高自友.动态公交网络设计的双层规划模型及算法研究[J].系统工程理论与实践,2007,27(5):82-89
[98]吴建军.城市交通网络拓扑结构复杂性研究[D].北京:北京交通大学,2008
[99]胡一竑,吴勤旻,朱道立.城市道路网络的拓扑性质和脆弱性分析[J].复杂系统与复杂性科学,2009,6(3):69-76
[100]邓贵仕,武佩剑,田炜.全球航运网络鲁棒性和脆弱性研究[J].大连理工大学学报,2008,48(5):765-768
[101] Mine H, Kawai H. Mathematics for reliability analysis[J]. M]. Tokyo: Asakura-shoten,1982:12-19
[102] Nicholson A, Du Z P. Degradable transportation systems: an integrated equilibrium model[J].Transportation Research Part B: Methodological,1997,31(3):209-223
[103] Bell M G H, Iida Y. Transportation network analysis[M].1997
[104] Bell, M. G. H. and J. D. Schm cker. Public Transport Network Reliability: Topological Effects[C].Proceedings of the3rd International Conference on Traffic and Transportation Studies (ICTTS), Guilin,China,2002,1(1):453-460.
[105] Chen, A., Z.W. Ji, and W. Recker. Travel Time Reliability with Risk-Sensitive Travelers[C].Transportation Research Record: Journal of Transportation Research Board, No.1783, TransportationResearch Board of the National Academies, Washington, D.C.,2002:27-33
[106] Chen A, Yang H, Lo H K, et al. A capacity related reliability for transportation networks[J]. Journal ofAdvanced Transportation,1999,33(2):183-200
[107]高自友,张好智,孙会君.城市交通网络设计问题中双层规划模型,方法及应用[J].交通运输系统工程与信息,2004,4(1):35-44
[108]任华玲,高自友.考虑出发时间选择的动态用户最优模型[J].交通运输系统工程与信息,2007,7(3):83-89
[109]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
[110]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,2006,2:22-26.
[111] LIU Z, LIU J. Seismic-controlled nonlinear extrapolation of well parameters using neural networks [J].Geophysics,1998,63(6):2035-41
[112] ALLABY P, HELLINGA B, BULLOCK M. Variable speed limits: Safety and operational impacts of acandidate control strategy for freeway applications [J]. Intelligent Transportation Systems, IEEETransactions on,2007,8(4):671-80
[113] VAN NES N, BRANDENBURG S, TWISK D. Improving homogeneity by dynamic speed limitsystems [J]. Accident Analysis&Prevention,2010,42(3):944-52.
[114]李宝家,黄小原.高速公路交通的变速限模糊控制[J].控制工程,2002,9(3):45-7
[115]张乐飞.高速公路瓶颈路段可变限速控制方法研究[J].北京交通大学学报,2012.5:13
[116]梁新荣,刘智勇,毛宗源.高速公路模糊神经网络限速控制与仿真研究[J].公路交通科技,2005,22(11):123-5
[117]任沙沙,庞明宝,王彦虎, et al.基于减法聚类的高速公路主线可变速度FNN混沌控制[J].公路交通科技,2012,29(007):124-31
[118] BOGENBERGER K, MAY A D. Advanced coordinated traffic responsive ramp metering strategies [J].1999:169-175
[119] PAPAGEORGIOU M, HADJ-SALEM H, BLOSSEVILLE J-M. ALINEA: A local feedback controllaw for on-ramp metering [J]. Transportation Research Record,1991:1320
[120] PAPAGEORGIOU M, HADJ-SALEM H, MIDDELHAM F. ALINEA local ramp metering: Summaryof field results [J]. Transportation Research Record: Journal of the Transportation Research Board,1997,1603(1):90-8
[121] MORI Y, YAMADA M, FUJIEDA A, et al. Traffic Simulation of On-Ramp Merging Behavior [J].TECHNOLOGY REPORTS-OSAKA UNIVERSITY,1994,44(101):289-293
[122]陈德望,王飞跃,陈龙.基于模糊神经网络的城市高速公路入口匝道控制算法[J].交通运输工程学报,2003,3(2):100-5
[123]梁新荣,刘智勇,毛宗源.高速公路匝道非线性反馈控制器的设计与仿真[J].计算机工程与应用,2005,41(20):111-3
[124]温凯歌,曲仕茹,张玉梅.基于模糊逻辑的高速公路入口匝道控制方法[J].中国公路学报,2007,20(6):100-4
[125]张生瑞,唐国玺.高速公路人口匝道的模糊逻辑控制及辅助方案设计[J].交通运输工程学报,2006,6(4):101-104
[126] Apostolos Kotsialos, Markos Papageorgiou, Morgan Mangeas, Habib Haj-Salem, coordinated andintegrated control of motorway networks via non-linear optimal control, Transportation Research PartC,2002,10(1):65-84
[127] Messmer and M.Papageorgiou, METANEA: A macroscopic simulation program for motorwaynetworks, Traffic engineering and control,1990,131(32):466-470
[128] Alessandri, A.Di Febbrara, A.Ferram, optimal control of freeways via speed signaling and rampmetering, Control Engineering practice Vol.6,771-780,1998
[129] Papageorgiou, M., Application of automatic control concepts to traffic flow modeling and control.Springer-Verlag, Berlin, Heidelberg, New York, Tokyo:231-236
[130] Stephanedes, Y., and Chang, K.-K., optimal control of freeway corridors, ASCE Journal ofTransportation Engineering,119,504-514
[131] Van Aerde, M., and Yagar, S., Dynamic integrated freewayltrafic signal networks: Problems andproposed solutions. Transportation Research22A,1993, p:435:443
[132] Zhou J, Chen H, Yan B, et al. Risk Assessment of Operation Safety in Freeway Tunnels: AnEvaluation Approach Using Multiple Safety Indices[J]. Journal of Transportation Safety&Security,2014,6(2):93-116
[133] Ji-biao Zhou, Hong Chen, Jing Yang, et al. Pedestrian Evacuation Time Model for Urban Metro Hubsbased on Multiple Video Sequences Data[J]. Mathematical Problems in Engineering, Publisher：Hindawi Publishing Corporation,2014,2014(1):1-11
[134]杨晓光.城市高速道路交通系统动态控制方法的研究[D],同济大学.1996.10
[135] Hong Chen, Yue-sheng YOU, Ji-biao Zhou, et al．(2013) A simplified approach to estimate the urbanexpressway capacity in traffic accidents using micro-simulation modeling [J]． Advances inMechanical Engineering, Publisher：Hindawi Publishing Corporation,2013,2013(1):1-8
[136]周继彪,陈红,闫彬,等.综合交通枢纽安全应急疏散路径选择[J].中国安全科学学报.2014,24(2):164-170
[137] CHAKRABORTY B. Ga-based multiple route selection for car navigation [M]. Applied Computing.Springer.2004:76-83
[138] INOUE Y. Exploration method of various routes with genetic algorithm [D]; Master‘s Thesis,Information System Engineering, Kochi Institute of Technology,2001.(in Japanese),2001
[139] SEN S, PILLAI R, JOSHI S, et al. A mean-variance model for route guidance in advanced travelerinformation systems [J]. Transportation Science,2001,35(1):37-49
[140]徐丽丽,邵春福.路径信息诱导的双层规划模型[J].交通运输工程学报,2007,7(5):106-109
[141]赵慧,赵占山,窦利谦.基于鲁棒离散优化方法的ATIS路径诱导[J].系统工程理论与实践,2008,28(10):156-61
[142]苏海滨,李伟恒,侯朝桢.基于遗传算法多目标非重叠路径诱导算法[J].北京理工大学学报,2007,27(4):331-334
[143]张丰焰,周伟,王元庆,张佳.高速公路改扩建工程交通组织设计探讨[J].公路.2006(1):109-113
[144]周继彪,陈红,闫彬,等.地铁换乘枢纽行人交通特性分析[J].武汉理工大学学报.2014,36(4):1-8
[145]宋开亮.关于高速公路拓宽改造交通量预测工作中若干问题的研究[D].西安:长安大学.2007
[146]夏阳.改扩建高速公路交诵量预测方法研究[D].武汉:武汉理工大学.2006
[147]韩宝睿.高速公路改扩建工程方案研究的关键技术分析[D].南京:东南大学.2005
[148]李永义.高速公路施工路段交通组织方案设计与评价研究[D].南京:东南大学.2006
[149]陈瑜.高速公路作业区安全分析及交通组织管理方法研究[J].哈尔滨:哈尔滨工业大学.2006.6:52-55
[150]堪志强.高速公路改扩建工程交通安全研究[D].长沙:湖南大学.2007
[151]马昌喜.高速公路交通安全对策研究[J].智能交通.中国公共安全学术版.2007(9):7~60
[152]陈红,周继彪,王建军,等.公路隧道运行环境安全评价指标与方法[J].长安大学学报（自然科学版）.2013,33(4):54-61
[153]周继彪,陈红,甘佐贤,等.公路隧道运行环境体系敏感性的灰色关联研究[J].武汉理工大学学报.2012,34(12):71-77
[154]路峰,张伟.交通安全管理预案研究[J].公安大学学报.2000(4):19-23
[155]刘清,张存保,陶存新.高速公路应急管理系统总体设计[J].公路,2007(7):132-136
[156]熊烈强.交通流理论及其在高速公路中的应用研究[D].武汉:武汉理工大学,2003
[2] Richards P I. Shock waves on the highway[J]. Operations research,1956,4(1):42-51
[3] Payne H J. Models of freeway traffic and control[J]. Mathematical models of public systems,1971:74-82
[4] Gerlough D L, Huber M J. Traffic flow theory[R].1976:39-51
[5] Reuschel A. Vehicle movements in a platoon[J]. Oesterreichisches Ingenieur-Archir,1950,4:193-215.
[6] Pipes L A. An operational analysis of traffic dynamics[J]. Journal of applied physics,2004,24(3):274-281
[7] Chandler R E, Herman R, Montroll E W. Traffic dynamics: studies in car following[J]. Operationsresearch,1958,6(2):165-184
[8] Herman R, Montroll E W, Potts R B, et al. Traffic dynamics: analysis of stability in car following[J].Operations research,1959,7(1):86-106
[9] Newell G F. Nonlinear effects in the dynamics of car following[J]. Operations Research,1961,9(2):209-229
[10] Bando M, Hasebe K, Nakayama A, et al. Dynamical model of traffic congestion and numericalsimulation[J]. Physical Review E,1995,51:1035-1042
[11] Bando M, Hasebe K, Nakanishi K, et al. Analysis of optimal velocity model with explicit delay[J].arXiv preprint patt-sol/9805002,1998:129-133
[12] Helbing D, Tilch B. Generalized force model of traffic dynamics[J]. Physical Review E,1998,58(1):133
[13] Treiber M, Hennecke A, Helbing D. Derivation, properties, and simulation of a gas-kinetic-based,nonlocal traffic model[J]. Physical Review E,1999,59(1):239
[14] Jiang R, Wu Q, Zhu Z. Full velocity difference model for a car-following theory[J]. Physical Review E,2001,64(1):017101
[15] Jiang R, Wu Q S. First-and second-order phase transitions from free flow to synchronized flow.Physics A,2003,322:676-684
[16] Jiang R, Wu Q S. Traffic patterns induced by the merging of two moving car platoonswith differentdensities. J. Phys. A,2002,35:2145-2157
[17] Konishi K, Kokame H, Hirata K. Coupled map car-following model and its delayed-feedbackcontrol[J]. Physical Review E,1999,60(4):4000
[18] Zhao X, Gao Z. A control method for congested traffic induced by bottlenecks in the coupled mapcar-following model[J]. Physica A: Statistical Mechanics and its Applications,2006,366:513-522
[19] Ge H X, Dai S Q, Dong L Y, et al. Stabilization effect of traffic flow in an extended car-followingmodel based on an intelligent transportation system application[J]. Physical Review E,2004,70(6):066134
[20] Ge H X, Dai S Q, Dong L Y. An extended car-following model based on intelligent transportationsystem application[J]. Physica A: Statistical Mechanics and its Applications,2006,365(2):543-548
[21] Ge H X, Cheng R J, Dai S Q. KdV and kink–antikink solitons in car-following models[J]. Physica A:Statistical Mechanics and its Applications,2005,357(3):466-476
[22] Wolfram S. Statistical mechanics of cellular automata[J]. Reviews of modern physics,1983,55(3):601
[23] Wolfram S. Theory and applications of cellular automata[J].1986:28-45
[24] Wolfram S. Cellular automaton fluids1: Basic theory[J]. Journal of Statistical Physics,1986,45(3-4):471-526
[25] Wolfram S. Cellular automata and complexity: collected papers[M]. Reading: Addison-Wesley,1994.
[26] Cremer M, Ludwig J. A fast simulation model for traffic flow on the basis of Boolean operations[J].Mathematics and Computers in Simulation,1986,28(4):297-303
[27] Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic[J]. Journal de physique I,1992,2(12):2221-2229
[28] Biham O, Middleton A A, Levine D. Self organization and a dynamical transition in traffic flowmodels[J]. arXiv preprint cond-mat/9206001,1992:58-89
[29] Benjamin S C, Johnson N F, Hui P M. Cellular automata models of traffic flow along a highwaycontaining a junction[J]. Journal of Physics A: Mathematical and General,1996,29(12):3119
[30] Nishinari K, Takahashi D. Multi-value cellular automaton models and metastable states in a congestedphase[J]. Journal of Physics A: Mathematical and general,2000,33(43):7709
[31] Barlovic R, Santen L, Schadschneider A, et al. Metastable states in cellular automata for traffic flow[J].The European Physical Journal B-Condensed Matter and Complex Systems,1998,5(3):793-800
[32] K. Nagel, M. Schreckenberg. A Cellular Automaton Model for Freeway Traffic. Journal de Physique I.1992,2(12):2221-2229
[33] D.E. Wolf. Cellular Automata for Traffic Simulations. Physica A: Statistical Mechanics and itsApplications.1999,263(1):438-451
[34]薛郁,董力耘,戴世强.一种改进的一维元胞自动机交通流模型及减速概率的影响.物理学报.2001,50(3):445-449
[35] Y. Liu. A Cellular Automaton Traffic Flow Model with Advanced Decelerations. MathematicalProblems in Engineering.2012,40(1):2012-2026
[36] A. Clarridge, K. Salomaa. Analysis of a Cellular Automaton Model for Car Traffic with a Slow-to-StopRule. Theoretical Computer Science.2010,411(38):3507-3515
[37] Y.-S. Qian, W.-J. Li, J.-W. Zeng, M. Wang, J.-W. Du, X.-P. Guang. Cellular Automaton Models ofHighway Traffic Flow Considering Lane-Control and Speed-Control. Communications in TheoreticalPhysics.2011,56:785-790
[38] M. Takayasu, H. Takayasu.1/F Noise in a Traffic Model. Fractals.1993,1(04):860-866
[39] S.C. Benjamin, N.F. Johnson, P. Hui. Cellular Automata Models of Traffic Flow Along a HighwayContaining a Junction. Journal of Physics A: Mathematical and General.1996,29(12):3119-3129
[40] R. Barlovic, T. Huisinga, A. Schadschneider, M. Schreckenberg. Open Boundaries in a CellularAutomaton Model for Traffic Flow with Metastable States. Physical Review E.2002,66(4):46-58
[41] M. Fukui, Y. Ishibashi. Traffic Flow in1d Cellular Automaton Model Including Cars Moving withHigh Speed. Journal of the Physical Society of Japan.1996,65(6):1868-1870
[42] D. Chowdhury, D.E. Wolf, M. Schreckenberg. Particle Hopping Models for Two-Lane Traffic withTwo Kinds of Vehicles: Effects of Lane-Changing Rules. Physica A: Statistical Mechanics and itsApplications.1997,235(3):417-439
[43]郑亮,马寿峰,贾宁.基于驾驶员行为的元胞自动机模型研究.物理学报.2010,59(7):4490-4498
[44]雷丽,薛郁,戴世强.交通流的一维元胞自动机敏感驾驶模型.物理学报.2003,52(9):2121-2126
[45]郑华荣,吴超仲,马晓凤.考虑驾驶愤怒的元胞自动机交通流模型.武汉理工大学学报(交通科学与工程版).2013,37(3):617-621
[46]华雪东,王炜,王昊.考虑驾驶心理的城市双车道交通流元胞自动机模型.物理学报.2011,60(8):398-405
[47]赵韩涛,毛宏燕.有应急车辆影响的多车道交通流元胞自动机模型.物理学报.2013(006):45-52
[48] K. Nagel, D.E. Wolf, P. Wagner, P. Simon. Two-Lane Traffic Rules for Cellular Automata: ASystematic Approach. Physical Review E.1998,58(2):1425-1435
[49] K. Rawat, V.K. Katiyar, P. Gupta. Two-Lane Traffic Flow Simulation Model Via Cellular Automaton.International Journal of vehicular technology.2011,2012(1):130-136
[50] J. Luo, Y. Qian, J. Zeng, X. Shao, W. Guo. Study on Security Features of Freeway Traffic Flow withCellular Automata Model-Taking the Number of Overtake as a Example. Measurement.2013,46(6):2035-2042
[51]王永明,周磊山,吕永波.基于元胞自动机交通流模型的车辆换道规则.中国公路学报.2008,21(1):89-93
[52] A. Schadschneider, D. Chowdhury, E. Brockfeld, K. Klauck, L. Santen, J. Zittartz. A New CellularAutomaton Model for City Traffic. In: Traffic and Granular Flow: Springer,2000:437-442
[53] Q. Meng, J. Weng. An Improved Cellular Automata Model for Heterogeneous Work Zone Traffic.Transportation research part C: emerging technologies.2011,19(6):1263-1275
[54] I. Spyropoulou. Modelling a Signal Controlled Traffic Stream Using Cellular Automata. Transportationresearch part C: emerging technologies.2007,15(3):175-190
[55]周奚召,刘灿齐,杨佩昆.高峰时段城市道路网时空资源和交通空间容量[J].同济大学学报,1996,24(4):392-397
[56]杨涛,程万里.城市交通网络广义容量应用研究—以南京市为例[J].东南大学学报,1992,22(5):82-84
[57]苗拴明,赵英.对时空消耗概念下路网广义容量计算方法的修正.城市规划汇刊1994(3):98-102
[58]陈春妹.路网容量研究[D].北京:北京工业大学博士论文,2002
[59]杨涛,徐吉谦.运输网络极大流的一种新算法[J].土木工程学报.1991,24(1):12-15
[60] Masao Fukushima. On the dual approach to the traffic assignment problem. Transpn Res, B Vol.18B,No3.1984,245-255
[61] Hai Yang, Michael, G.H.Bell, Qiang Meng. Modeling the capacity and level of urban transportationnetworks. Transportation Research Part B34(2000):132-146
[62]高自友,张好智,孙会君.城市交通网络设计问题中双层规划模型、方法及应用[J].土交通运输系统工程与信息,2004.4(1):33-36
[63] Forian M. and Nguyen S. a method for computing network equilibrium with elastic demands. TranspnRes.10.1976:37-57
[64] Florian M. An improved liners approximation algorithm for the network equilibrium (packet switching)problem. Proc, of the1977IEEE Conf. On Decision&Control,1977:812-818
[65] Masao Fukushima, On the Dual Approach to the Traffic Assignment Problem [J], Transpn Res, B Vol.18B.No.3.1984,P245-255
[66] J. H. Wu, Y. Chen and M. Florian, The Continues Dynamic Network Loading Problem: AMathematical Formulation and Solution Method[J], Transportation research PartB,1998,32(1):173-187
[67] Alfredo Garcia, Daniel Resume, Robert L. Smith, Fictitious play for finding system optimal routingsin dynamic traffic networks [J], Transportation research PartB,2000,34(1):147-156
[68] Byung-wook Wie, Dynamic Congestion Pricing Models for General Traffic Networks, Transportationresearch PartB,1998,32(1):313-327
[69] Ford.L.R.Jr, Fulkerson.D.R. Maximal flow through a network [J]. Canadian journal of mathematics,1956:399-404
[70]杨涛.运输网络极大流动态诊断模型[J].中国公路学报,1991,4(1):72-77
[71] FukushimaM.A modified Frank-Wolfe algorithm for solving the traffic assignment problem.Department of Appl. Math. And Physics, Kyoto University, Kyoto, Japan. To appear in Transpn Trs.1983:21-39
[72] Gartner M.H. Optimal traffic assignment with elastic demands: A review[J]. Transpn Sci.,1980,14(1):174-208
[73] William H.K..Lam, Member, ASCE, and Yangping Zhang, Capacity-Constrained Traffic Assignmentin Networks with Residual Queues[J], Journal of Transportation Engineering,2000(1):121-128
[74] Y.W.XU, J.H.WU, M.Florian, P.Marcotte, and D.L.Zhu, Advances in the Continuous DynamicNetwork Loading Problem, Transportation Science,1999.11, Vol.33,No.4:321-323
[75]杨晓光,褚浩然.错峰出行对城市交通的影响分析[J].同济大学学报:自然科学版,2006,34(7):899-903
[76]徐寅峰,余海燕,苏兵等.基于时间和路径偏好的交通流分配模型与诱导策略[J].系统工程理论与实践,2012,32(10):2306-2314
[77]丁以中,交通运输网络规划[M],大连海事大学出版社,1999
[78]李旭宏,田峰,顾正华.城市道路网供应分析技术[J].交通运输工程学报.2002.6:12-16
[79]李硕,黎莉.公路网狭义总容量理论及模型[J].湖南大学学报(自然科学版),1999.26(1):08-12
[80] Paguette RL. Transportation Engineering, planning and Design,2ndedu. New York: John Wiiley Press,1982:66-68
[81] Blunden WR,Black JA. The land use-transport System. Oxford, England:Pergem on Press,1984
[82]李炳林.基于路网服务水平的路网容量研究[D].长沙：长沙理工大学,2008
[83]张华.区域高速公路网优化布局与调整策略研究[D].武汉：华中科技大学,2009
[84] H. Chen, J.B. Zhou, Y. Wu, et al. Modeling of Road Network Capacity Research in Urban CentralArea[J]. Applied Mechanics and Materials.2011,40:778-784
[85] Watts, D.J., Strogatz, S.H.. Collective dynamics of small-world‘net-works[J]. Nature,1998,393(6684):409-10
[86] Barabasi A.-L. and Albert R.. Emergence of scaling in random networks[J]. Science,1999,208:509-512
[87] Albert R. and Barabasi A.-L.. Statistical mechanics of complex networks[J]. Rev. Mod. Phys.,2002,74:47-97
[88] Holme Petter, Beom Jun kim, Chang No Yoon and Seung Kee Han. Attack vulnerability of complexnetworks[J]. Physical Review E,2002,65(1):056-076
[89] He S, Li S, Ma H. Effect of edge removal on topological and functional robustness of complexnetworks[J]. Physica A: Statistical Mechanics and its Applications,2009,388(11):2243-2253.
[90] Crucitti Paolo, Latora Vito and Porta Centrality measures in spatial networks of urban streets[J].Physical Review E,2006,2:110-115.
[91] Wu J.J., Gao Z.Y., Sun H.J., and Huang H.J.. Congestion in different topologies of traffic networks[J].Europhysics Letters,2006,74(3):560-566.
[92] Latora V, Marchiori M. Is the Boston subway a small-world network [J]. Physica A(50378-4371),2002,314(1):109-113
[93]李英，周伟，郭世进.上海公共交通网络复杂性分析[J].系统工程，2007，25(l):38-41
[94] Sienkiewicz J. Holyst J A.Public transport systems in Poland: from Bialystok to Zielona Gora by busand tram using universal statistics of complex networks[J]. Acta Physica PolonicaB,2005,36(5):1771-1778
[95]张晨，张宁.上海市公交网络拓扑性质研究[J].上海理工大学学报,2006,28(5):489-94.
[96]高自友，吴建军，毛保华，黄海军.交通运输网络复杂性及其相关问题的研究[J].交通运输系统工程与信息,2005,5(2):79-84
[97]任华玲,高自友.动态公交网络设计的双层规划模型及算法研究[J].系统工程理论与实践,2007,27(5):82-89
[98]吴建军.城市交通网络拓扑结构复杂性研究[D].北京:北京交通大学,2008
[99]胡一竑,吴勤旻,朱道立.城市道路网络的拓扑性质和脆弱性分析[J].复杂系统与复杂性科学,2009,6(3):69-76
[100]邓贵仕,武佩剑,田炜.全球航运网络鲁棒性和脆弱性研究[J].大连理工大学学报,2008,48(5):765-768
[101] Mine H, Kawai H. Mathematics for reliability analysis[J]. M]. Tokyo: Asakura-shoten,1982:12-19
[102] Nicholson A, Du Z P. Degradable transportation systems: an integrated equilibrium model[J].Transportation Research Part B: Methodological,1997,31(3):209-223
[103] Bell M G H, Iida Y. Transportation network analysis[M].1997
[104] Bell, M. G. H. and J. D. Schm cker. Public Transport Network Reliability: Topological Effects[C].Proceedings of the3rd International Conference on Traffic and Transportation Studies (ICTTS), Guilin,China,2002,1(1):453-460.
[105] Chen, A., Z.W. Ji, and W. Recker. Travel Time Reliability with Risk-Sensitive Travelers[C].Transportation Research Record: Journal of Transportation Research Board, No.1783, TransportationResearch Board of the National Academies, Washington, D.C.,2002:27-33
[106] Chen A, Yang H, Lo H K, et al. A capacity related reliability for transportation networks[J]. Journal ofAdvanced Transportation,1999,33(2):183-200
[107]高自友,张好智,孙会君.城市交通网络设计问题中双层规划模型,方法及应用[J].交通运输系统工程与信息,2004,4(1):35-44
[108]任华玲,高自友.考虑出发时间选择的动态用户最优模型[J].交通运输系统工程与信息,2007,7(3):83-89
[109]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
[110]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,2006,2:22-26.
[111] LIU Z, LIU J. Seismic-controlled nonlinear extrapolation of well parameters using neural networks [J].Geophysics,1998,63(6):2035-41
[112] ALLABY P, HELLINGA B, BULLOCK M. Variable speed limits: Safety and operational impacts of acandidate control strategy for freeway applications [J]. Intelligent Transportation Systems, IEEETransactions on,2007,8(4):671-80
[113] VAN NES N, BRANDENBURG S, TWISK D. Improving homogeneity by dynamic speed limitsystems [J]. Accident Analysis&Prevention,2010,42(3):944-52.
[114]李宝家,黄小原.高速公路交通的变速限模糊控制[J].控制工程,2002,9(3):45-7
[115]张乐飞.高速公路瓶颈路段可变限速控制方法研究[J].北京交通大学学报,2012.5:13
[116]梁新荣,刘智勇,毛宗源.高速公路模糊神经网络限速控制与仿真研究[J].公路交通科技,2005,22(11):123-5
[117]任沙沙,庞明宝,王彦虎, et al.基于减法聚类的高速公路主线可变速度FNN混沌控制[J].公路交通科技,2012,29(007):124-31
[118] BOGENBERGER K, MAY A D. Advanced coordinated traffic responsive ramp metering strategies [J].1999:169-175
[119] PAPAGEORGIOU M, HADJ-SALEM H, BLOSSEVILLE J-M. ALINEA: A local feedback controllaw for on-ramp metering [J]. Transportation Research Record,1991:1320
[120] PAPAGEORGIOU M, HADJ-SALEM H, MIDDELHAM F. ALINEA local ramp metering: Summaryof field results [J]. Transportation Research Record: Journal of the Transportation Research Board,1997,1603(1):90-8
[121] MORI Y, YAMADA M, FUJIEDA A, et al. Traffic Simulation of On-Ramp Merging Behavior [J].TECHNOLOGY REPORTS-OSAKA UNIVERSITY,1994,44(101):289-293
[122]陈德望,王飞跃,陈龙.基于模糊神经网络的城市高速公路入口匝道控制算法[J].交通运输工程学报,2003,3(2):100-5
[123]梁新荣,刘智勇,毛宗源.高速公路匝道非线性反馈控制器的设计与仿真[J].计算机工程与应用,2005,41(20):111-3
[124]温凯歌,曲仕茹,张玉梅.基于模糊逻辑的高速公路入口匝道控制方法[J].中国公路学报,2007,20(6):100-4
[125]张生瑞,唐国玺.高速公路人口匝道的模糊逻辑控制及辅助方案设计[J].交通运输工程学报,2006,6(4):101-104
[126] Apostolos Kotsialos, Markos Papageorgiou, Morgan Mangeas, Habib Haj-Salem, coordinated andintegrated control of motorway networks via non-linear optimal control, Transportation Research PartC,2002,10(1):65-84
[127] Messmer and M.Papageorgiou, METANEA: A macroscopic simulation program for motorwaynetworks, Traffic engineering and control,1990,131(32):466-470
[128] Alessandri, A.Di Febbrara, A.Ferram, optimal control of freeways via speed signaling and rampmetering, Control Engineering practice Vol.6,771-780,1998
[129] Papageorgiou, M., Application of automatic control concepts to traffic flow modeling and control.Springer-Verlag, Berlin, Heidelberg, New York, Tokyo:231-236
[130] Stephanedes, Y., and Chang, K.-K., optimal control of freeway corridors, ASCE Journal ofTransportation Engineering,119,504-514
[131] Van Aerde, M., and Yagar, S., Dynamic integrated freewayltrafic signal networks: Problems andproposed solutions. Transportation Research22A,1993, p:435:443
[132] Zhou J, Chen H, Yan B, et al. Risk Assessment of Operation Safety in Freeway Tunnels: AnEvaluation Approach Using Multiple Safety Indices[J]. Journal of Transportation Safety&Security,2014,6(2):93-116
[133] Ji-biao Zhou, Hong Chen, Jing Yang, et al. Pedestrian Evacuation Time Model for Urban Metro Hubsbased on Multiple Video Sequences Data[J]. Mathematical Problems in Engineering, Publisher：Hindawi Publishing Corporation,2014,2014(1):1-11
[134]杨晓光.城市高速道路交通系统动态控制方法的研究[D],同济大学.1996.10
[135] Hong Chen, Yue-sheng YOU, Ji-biao Zhou, et al．(2013) A simplified approach to estimate the urbanexpressway capacity in traffic accidents using micro-simulation modeling [J]． Advances inMechanical Engineering, Publisher：Hindawi Publishing Corporation,2013,2013(1):1-8
[136]周继彪,陈红,闫彬,等.综合交通枢纽安全应急疏散路径选择[J].中国安全科学学报.2014,24(2):164-170
[137] CHAKRABORTY B. Ga-based multiple route selection for car navigation [M]. Applied Computing.Springer.2004:76-83
[138] INOUE Y. Exploration method of various routes with genetic algorithm [D]; Master‘s Thesis,Information System Engineering, Kochi Institute of Technology,2001.(in Japanese),2001
[139] SEN S, PILLAI R, JOSHI S, et al. A mean-variance model for route guidance in advanced travelerinformation systems [J]. Transportation Science,2001,35(1):37-49
[140]徐丽丽,邵春福.路径信息诱导的双层规划模型[J].交通运输工程学报,2007,7(5):106-109
[141]赵慧,赵占山,窦利谦.基于鲁棒离散优化方法的ATIS路径诱导[J].系统工程理论与实践,2008,28(10):156-61
[142]苏海滨,李伟恒,侯朝桢.基于遗传算法多目标非重叠路径诱导算法[J].北京理工大学学报,2007,27(4):331-334
[143]张丰焰,周伟,王元庆,张佳.高速公路改扩建工程交通组织设计探讨[J].公路.2006(1):109-113
[144]周继彪,陈红,闫彬,等.地铁换乘枢纽行人交通特性分析[J].武汉理工大学学报.2014,36(4):1-8
[145]宋开亮.关于高速公路拓宽改造交通量预测工作中若干问题的研究[D].西安:长安大学.2007
[146]夏阳.改扩建高速公路交诵量预测方法研究[D].武汉:武汉理工大学.2006
[147]韩宝睿.高速公路改扩建工程方案研究的关键技术分析[D].南京:东南大学.2005
[148]李永义.高速公路施工路段交通组织方案设计与评价研究[D].南京:东南大学.2006
[149]陈瑜.高速公路作业区安全分析及交通组织管理方法研究[J].哈尔滨:哈尔滨工业大学.2006.6:52-55
[150]堪志强.高速公路改扩建工程交通安全研究[D].长沙:湖南大学.2007
[151]马昌喜.高速公路交通安全对策研究[J].智能交通.中国公共安全学术版.2007(9):7~60
[152]陈红,周继彪,王建军,等.公路隧道运行环境安全评价指标与方法[J].长安大学学报（自然科学版）.2013,33(4):54-61
[153]周继彪,陈红,甘佐贤,等.公路隧道运行环境体系敏感性的灰色关联研究[J].武汉理工大学学报.2012,34(12):71-77
[154]路峰,张伟.交通安全管理预案研究[J].公安大学学报.2000(4):19-23
[155]刘清,张存保,陶存新.高速公路应急管理系统总体设计[J].公路,2007(7):132-136
[156]熊烈强.交通流理论及其在高速公路中的应用研究[D].武汉:武汉理工大学,2003