基于混合交通流的信号交叉口机动车车头时距研究
|
摘要
混合交通是中国城市交通的基本特征。在混合交通环境下,机动车、非机动车与行人的特性各异,相互干扰严重,导致道路设施利用率与交通运行质量下降,为城市道路交通管理与控制带来巨大挑战。城市道路信号交叉口是城市路网中最基本的节点,是城市道路网络中通行能力和交通安全的瓶颈,交叉口的通行能力影响着整个交通网络的运输能力。
信号交叉口排队车辆车头时距是城市微观交通分析中的一个关键参数,直接反映了司机驾驶行为特性、交叉口通行能力以及服务水平。本文在综述国内外相关研究的基础上,结合混合交通调查数据,从混合交通环境下信号交叉口排队车辆队列消散特征、首车车头时距、排队车辆车头时距分析及通行能力研究等方面进行了一些有益探讨,主要研究内容及结论如下：
①根据摄像采集数据,对混合交通流环境下城市道路信号交叉口排队车辆队列消散过程及车头时距进行了深入分析,提出了车辆消散的四个阶段,即初始加速阶段、致密性行驶队列阶段、队列消散阶段和自由流阶段,并指出受机非干扰等因素的影响,车流消散的四个阶段不一定全部出现。对混合交通流消散特性进行了分析,明确指出信号交叉口首车车头时距从大到小依次为专右车道、专左车道、直外车道和直内车道,并定量分析了机非干扰和车辆类型对车头时距的影响。
②在适应性分析的基础上,将风险分析方法引入交通流研究领域,首次提出了基于风险分析方法的信号交叉排队车辆首车车头时距分析模型,并对偏似然函数的构建和模型参数估计方法做了详细探讨,以北京典型信号控制交叉口首车车头时距数据为对象进行了案例分析,取得了良好效果。分析结果表明北京信号交叉口排队车辆首车车头时距达到4.54s,明显大于国外同类城市；模型影响因素分析结果还揭示了启动干扰和行驶干扰对分别导致首车平均车头时距增长了37.09%和60.24%。
③系统分析了既有三大车头时距模型,即Briggs确定性模型、Bonneson模型和神经网络模型的缺陷,阐述了统计学习理论和支持向量机方法的优点,首次构建了基于支持向量机方法的信号交叉口排队车辆车头时距分析模型,提出了分析框架和步骤,将本车类型、前车类型、车道功能、前车车头时距、排队位置和有无机非干扰这六个影响因素作为模型输入,排队车辆车头时距作为输出,通过相对误差、平均绝对误差和均方根绝对误差三个统计指标对模型性能进行评价。以北京市知春路口排队车辆车头时距为研究对象,应用本文提出的支持向量机模型、与确定性模型和神经网络模型进行混合交通流环境下信号交叉口排队车辆车头时距分析,结果表明,本研究提出的支持向量机模型明显优于确定性模型和神经网络模型。
④通过对信号交叉口排队车辆队列消散的稳定性分析,明确指出混合交通流环境下,排队车辆队列消散存在波动性,HCM定义的饱和流率很难出现,导致HCM提出的基于饱和流率的通行能力计算模型存在不适应性。通过引入信号转换时间内平均能通过的车辆数Nvki和显示绿灯时间内平均能通过的车辆数Ngki,构建了修正通行能力模型,并利用累计曲线法对相关参数进行了标定,以北京市知春路口东进口为研究对象进行了实证分析。结果表明,本模型在准确性、简洁性和适应性方面均优于HCM模型。
ABSTRACT:Most cities in China are characteristiced by heterogeneous traffic. Under the heterogeneous traffic condition, motorized vehicles, non-motorized vehicles and pedestrians all have their own characteristics, and interfere with each other, which cause the decrease of road facilities utilization and operation quality, and challenge the road transport management and control system. The intersection is the basic node of the road network in a city, and also the bottleneck of network capacity and traffic safety. Therefore, the intersection capacity has a great influence on the whole transport network.
As a key parameter for traffic microscopic analysis, discharge headway of queuing vehicle in signalized intersection dircectly reflect driving behavior characteristic, as well as intersection capacity and service level. Based on the review of relavent research home and abroad, combined with heterogeneous traffic investigation data, several subjects, like discharge characteristics of queuing vehicles, the first discharge headway, successive discharge headway are analysed. Main works and conclusions of this dissertation are summarized as below:
①According to the video record data, we elaborately analyses the discharging process of queuing vehicles and its discharge headway. On this foundation, the four stage of discharging are proposed, i.e. the initial speedup phase, compaction platoon phase, platoon dispersion phase, and free flow phase. However, the four stages may not appear under some situation owing to non-motorized vehicles disturbance. By analyzing the discharge characteristic of heterogeneous traffic, this dissertation expressly instructs that the discharge headways on lane type are in the following descending order:the right-turn-only-lane, the left-turn-only-lane, the off-side straight lane, and the kerb-side straight lane. And then, we also make a quantative analysis on the effects on discharge headway for non-motorized vehicles disturbance and vehicle type.
②Hazard-based analysis method is introduced into traffic flow field after adaptability analysis, and for the first time first discharge headway is modeled basing on hazard-based analysis method. The partial likelihood function and parameters' evaluation method is explored deeply next. Then a case study of typical signalized intersections at Beijing is conducted. The results provided that in Beijing city the first discharge headway arrives at 4.54s, which is obviously longer than in other equivalent foreign cities. And the factor analysis results also reveal the influence degree of the starting disturbance and the moving disturbance, which causes an increase in the average first discharge headway to 37.09 percent and 60.24 percent, respectively.
③The defects of existing top-three widly-used headway models, which are Deterministic Model (DM), Bonneson Model (BM), and Neural Network Model of discharge headway (NNM), are analysed, and the principle of Statistical Learing Theory and Support Vector Machine (SVM) is represented next. In addition, a SVM-based discharge headway model at signalized intersection is constructed for the first time, and the related analytical frameworks and process are presented. In the model, subject vehicle type, preceding vehicle type, lane type, discharging headway of the preceding vehicle, queuing position, and disturbance are input, and discharge headway of the subject vehicle is output. Besides, three indexes, i.e. relative percentage error (RPE), mean absolute percentage error (MAPE), and root mean square error (RMSE), are introduced to evaluate the model perfoemance. At last, performances of three models, namely SVM-based discharge headway model (SVMM), DM, and NNM, are evaluated by case studies. The results indicate that the SVMM is better than the others.
④Based on the discharge stability analysis of queuing vehicles at signalized intersections, the saturation flow rate defined in Highway Capacity Manual is pointed out that can hardly happen under the heterogeneous traffic condition at Beijing. This means that the HCM capacity model based on the saturation flow rate is not adaptive. After introduction of two variables, i.e. the expected number of vehicles that can be discharged during green interval of usable phase,, and the expected number of vehicles that can be discharged during green time of usable phase,, a modified intersection capacity model is presented and the related parameters is calibrated by cumulative curve method. Finally, the eastbound of Zhichunlukou is conducted as an empirical analysis. The results show that the modified model is better than HCM model in accuracy, simplicity and universality.
信号交叉口排队车辆车头时距是城市微观交通分析中的一个关键参数,直接反映了司机驾驶行为特性、交叉口通行能力以及服务水平。本文在综述国内外相关研究的基础上,结合混合交通调查数据,从混合交通环境下信号交叉口排队车辆队列消散特征、首车车头时距、排队车辆车头时距分析及通行能力研究等方面进行了一些有益探讨,主要研究内容及结论如下：
①根据摄像采集数据,对混合交通流环境下城市道路信号交叉口排队车辆队列消散过程及车头时距进行了深入分析,提出了车辆消散的四个阶段,即初始加速阶段、致密性行驶队列阶段、队列消散阶段和自由流阶段,并指出受机非干扰等因素的影响,车流消散的四个阶段不一定全部出现。对混合交通流消散特性进行了分析,明确指出信号交叉口首车车头时距从大到小依次为专右车道、专左车道、直外车道和直内车道,并定量分析了机非干扰和车辆类型对车头时距的影响。
②在适应性分析的基础上,将风险分析方法引入交通流研究领域,首次提出了基于风险分析方法的信号交叉排队车辆首车车头时距分析模型,并对偏似然函数的构建和模型参数估计方法做了详细探讨,以北京典型信号控制交叉口首车车头时距数据为对象进行了案例分析,取得了良好效果。分析结果表明北京信号交叉口排队车辆首车车头时距达到4.54s,明显大于国外同类城市；模型影响因素分析结果还揭示了启动干扰和行驶干扰对分别导致首车平均车头时距增长了37.09%和60.24%。
③系统分析了既有三大车头时距模型,即Briggs确定性模型、Bonneson模型和神经网络模型的缺陷,阐述了统计学习理论和支持向量机方法的优点,首次构建了基于支持向量机方法的信号交叉口排队车辆车头时距分析模型,提出了分析框架和步骤,将本车类型、前车类型、车道功能、前车车头时距、排队位置和有无机非干扰这六个影响因素作为模型输入,排队车辆车头时距作为输出,通过相对误差、平均绝对误差和均方根绝对误差三个统计指标对模型性能进行评价。以北京市知春路口排队车辆车头时距为研究对象,应用本文提出的支持向量机模型、与确定性模型和神经网络模型进行混合交通流环境下信号交叉口排队车辆车头时距分析,结果表明,本研究提出的支持向量机模型明显优于确定性模型和神经网络模型。
④通过对信号交叉口排队车辆队列消散的稳定性分析,明确指出混合交通流环境下,排队车辆队列消散存在波动性,HCM定义的饱和流率很难出现,导致HCM提出的基于饱和流率的通行能力计算模型存在不适应性。通过引入信号转换时间内平均能通过的车辆数Nvki和显示绿灯时间内平均能通过的车辆数Ngki,构建了修正通行能力模型,并利用累计曲线法对相关参数进行了标定,以北京市知春路口东进口为研究对象进行了实证分析。结果表明,本模型在准确性、简洁性和适应性方面均优于HCM模型。
ABSTRACT:Most cities in China are characteristiced by heterogeneous traffic. Under the heterogeneous traffic condition, motorized vehicles, non-motorized vehicles and pedestrians all have their own characteristics, and interfere with each other, which cause the decrease of road facilities utilization and operation quality, and challenge the road transport management and control system. The intersection is the basic node of the road network in a city, and also the bottleneck of network capacity and traffic safety. Therefore, the intersection capacity has a great influence on the whole transport network.
As a key parameter for traffic microscopic analysis, discharge headway of queuing vehicle in signalized intersection dircectly reflect driving behavior characteristic, as well as intersection capacity and service level. Based on the review of relavent research home and abroad, combined with heterogeneous traffic investigation data, several subjects, like discharge characteristics of queuing vehicles, the first discharge headway, successive discharge headway are analysed. Main works and conclusions of this dissertation are summarized as below:
①According to the video record data, we elaborately analyses the discharging process of queuing vehicles and its discharge headway. On this foundation, the four stage of discharging are proposed, i.e. the initial speedup phase, compaction platoon phase, platoon dispersion phase, and free flow phase. However, the four stages may not appear under some situation owing to non-motorized vehicles disturbance. By analyzing the discharge characteristic of heterogeneous traffic, this dissertation expressly instructs that the discharge headways on lane type are in the following descending order:the right-turn-only-lane, the left-turn-only-lane, the off-side straight lane, and the kerb-side straight lane. And then, we also make a quantative analysis on the effects on discharge headway for non-motorized vehicles disturbance and vehicle type.
②Hazard-based analysis method is introduced into traffic flow field after adaptability analysis, and for the first time first discharge headway is modeled basing on hazard-based analysis method. The partial likelihood function and parameters' evaluation method is explored deeply next. Then a case study of typical signalized intersections at Beijing is conducted. The results provided that in Beijing city the first discharge headway arrives at 4.54s, which is obviously longer than in other equivalent foreign cities. And the factor analysis results also reveal the influence degree of the starting disturbance and the moving disturbance, which causes an increase in the average first discharge headway to 37.09 percent and 60.24 percent, respectively.
③The defects of existing top-three widly-used headway models, which are Deterministic Model (DM), Bonneson Model (BM), and Neural Network Model of discharge headway (NNM), are analysed, and the principle of Statistical Learing Theory and Support Vector Machine (SVM) is represented next. In addition, a SVM-based discharge headway model at signalized intersection is constructed for the first time, and the related analytical frameworks and process are presented. In the model, subject vehicle type, preceding vehicle type, lane type, discharging headway of the preceding vehicle, queuing position, and disturbance are input, and discharge headway of the subject vehicle is output. Besides, three indexes, i.e. relative percentage error (RPE), mean absolute percentage error (MAPE), and root mean square error (RMSE), are introduced to evaluate the model perfoemance. At last, performances of three models, namely SVM-based discharge headway model (SVMM), DM, and NNM, are evaluated by case studies. The results indicate that the SVMM is better than the others.
④Based on the discharge stability analysis of queuing vehicles at signalized intersections, the saturation flow rate defined in Highway Capacity Manual is pointed out that can hardly happen under the heterogeneous traffic condition at Beijing. This means that the HCM capacity model based on the saturation flow rate is not adaptive. After introduction of two variables, i.e. the expected number of vehicles that can be discharged during green interval of usable phase,, and the expected number of vehicles that can be discharged during green time of usable phase,, a modified intersection capacity model is presented and the related parameters is calibrated by cumulative curve method. Finally, the eastbound of Zhichunlukou is conducted as an empirical analysis. The results show that the modified model is better than HCM model in accuracy, simplicity and universality.
引文
[1]Abdennour A., Al-Ghamdi A.S. Artificial neural networks application to the estimation of vehicle headways in freeway sections. Proceedings of the 81st Annual Meeting, Transportation Research Board, Washington, D.C. January 13-17,2002.
[2]Akcelik R., Chung E. Calibration of the bunched exponential distribution of arrival headways. Road and Transport Research,1994,3(1),42-59.
[3]Al-Ghamdi A.S. Entering headway for through movements at urban signalized intersections. Transportation Research Record,1999, No.1678,42-47.
[4]Bester C.J., Varndell P.J. The effect of a leading green phase on the startup lost time of opposing vehicles.21st Annual South African Transport Conference, South Africa,15-18 July, 2002.
[5]Bhat C.R., Frusti T., Zhao H.M., Schonfelder S., Axhausen K.W. Intershopping duration:An analysis using multiweek data. Transportation Research Part B,2004,38(1),39-60.
[6]Bonneson J.A. Lane volume and saturation flow rate for multilane intersection approach. Journal of Transportation Engineering,1998,124(3),240-245.
[7]Bonneson J.A. Study of headway and lost time at single-point urban interchanges. Transportation Research Record, Transportation Research Board of the National Academies, Washington, D.C.,1992, No.1365,30-39.
[8]Botha J.L., Kruse T.R. Flow rates at signalized intersections under cold winter conditions. Journal of Transportation Engineering,1992,118(3),439-450.
[9]Boumediene A., Brahimi K., Belguesmia N., Bouakkaz K. Saturation flow versus green time at two-stage signal controlled intersections. Transport,2009,24(4),288-295.
[10]Brackstone M., Waterson B., McDonald M. Determinants of following headway in.congested traffic. Transportation Research Part F,2009,12(2),131-142.
[11]Briggs T. Headways on crossing the stop line after queuing at traffic lights. Traffic Engineering and Control,1977,18(5),264-265.
[12]Buckley D.J. A semi-Poisson model of traffic flow. Transportation Science,1968,2(2), 107-133.
[13]Cao X.B., Xu Y.W., Chen D., Qiao H. Associated evolution of a support vector machine-based classifier for pedestrian detection. Information Sciences,2009,179(8),1070-1077.
[14]Carstens R.L. Some traffic parameters at signalized intersections. Traffic Engineering,1971, 41(11),33-36.
[15]Chung U. Development of an accident duration prediction model on the Korean freeway systems. Accident Analysis and Prevention,2010,42(2),282-289.
[16]Conforti D., Guido R. Kernel based support vector machine via semidefinite programming: Application to medical diagnosis. Computers and Operations Research,2010,37(8), 1389-1394.
[17]Cowan R.J. Useful headway models. Transportation Research,1975,9(6),371-375.
[18]Cox D.R. Regression models and life tables. Journal of the Royal Statistical Society. Series B, 1972,34(2),187-220.
[19]Evgeniou T., Poggio T., Pontil M., Verri A. Regularization and statistical learning theory for data analysis. Computational Statistics & Data Analysis,2002,38(4),421-432.
[20]Gao L.P., Liu M.J., Sun Z.Z., Mao B.H. Simulation on impact of information guidance on regional traffic flow. Journal of Transportation Systems Engineering and Information Technology,2008,8(4),63-69.
[21]Gerlough D.L., Wagner F.A. NCHRP report 32:Improved criteria for traffic signals at individual intersections. TRB, National Research Council, Washington, D.C.,1967,38-42.
[22]Gonzalez L.F.C. Discharge headway at signalized intersections in Monterrey, Nuevo Leon, Mexico. Doctoral Dissertation of University of Texas, USA,2006.
[23]Greenshields B.D., Schapiro D., Ericksen E.L. Traffic performance at urban intersections. Eno Foundation for Highway Traffic Control,1947,23-30.
[24]Greenshields B.D., Weida F.M. Statistics with application to highway traffic analyses. Eno Foundation for Highway Traffic Control,1952,164-169.
[25]Griffiths J.D., Hunt J.G Vehicle headway in urban areas. Traffic Engineering and Control, 1991,32(10),458-462.
[26]Hagring O. Estimation of parameters in distribution of headways in roundabouts. Journal of Transportation Engineering,2002,128(5),403-411.
[27]Hatzopoulou M., Miller E.J. Institutional integration for sustainable transportation policy in Canada. Transport Policy,2008,15(3),149-162.
[28]He M., Liu L., Wei H., Dong R. Characteristics of signalized U-turn movements in headway distribution, capacity, and control delay:Case study of intersections in Kunning, China. Proceedings of the 88th Annual Meeting Transportation Research Board, Washington D.C. Paper #09-2550, Jan 11-14 2009.
[29]Helbing D. Traffic and related self-driven many-particle systems. Reviews of Modern Physics,2001,73(4),1067-1141.
[30]Hewage K.N., Ruwanpura J.Y. Optimization of traffic signal light timeing using simulation. Proceedings of the 2004 Winter Simulation Conference,2004,1428-1433.
[31]Highway Research Board. Highway Capacity Manual:Practical applications for research. Department of Traffic and Operations, Committee on Highway Capacity, United States Government Printing Office, Washington, DC,1950,39-41.
[32]Hung W.T., Tian F., Tong H.Y. Discharge headway at signalized intersections in Hong Kong. Journal of Advance Transportation,2003,37(1),105-113.
[33]Jin X.X., Zhang Y., Wang F., Li L., Yao D.Y., Su Y.L., Wei Z. Departure headways at signalized intersections:A log-normal distribution model approach. Transportation Research Part C,2009,17(3),318-327.
[34]Joseph J., Chang G.L. Saturation flow rates and maximum critical lane volumes for planning applications in Maryland. Journal of Transportation Engineering,2005,131(12),946-952.
[35]Jovanis P.P., Chang H.L. Disaggregate model of highway accident occurrence using survival theory. Accident Analysis and Prevention,1989,21(5),445-458.
[36]Jr May A.D. Gap availability studies. Highway Research Record, HRB, Washington D.C., 1965, No.72,105-136.
[37]Jr May A.D., Wagner F.A. Headway characteristics and interrelationships of fundamental characteristics of traffic flow. Highway Research Board Proceedings,1960, Vol.39,524-547.
[38]Jr. Denney R.W., Curtis E., Head L. Long green times and cycles at congested traffic signals. TRB 2009 Annual Meeting CD-ROM, Jan 11-14 2009.
[39]Lee H.S., Do T.W. Saturation headway of through movement at signalized intersections in urban area. Journal of Transportation Research Society of Korea,2002,20(5),23-31.
[40]Lee J., Chen R.L. Entering headway at signalized intersections in a small metropolitan area. Transportation Research Record,1986, No 1091,117-126.
[41]Lee T.H. Saturation headways and lost times at signalized intersections. Doctoral dissertation of University of Kansa,1995.
[42]Li H.L., Prevedouros P.D. Detailed observations of saturation headways and start-up lost times. Transportation Research Record,2002, No.1802,44-53.
[43]Li X.G., Lord D., Zhang Y.L., Xie Y.C. Predicting motor vehicle crashes using Support Vector Machine models. Accident Analysis & Prevention,2008,40(4),1611-1618.
[44]Limanond T., Chookerd S., Roubtonglang N. Effects of countdown timers on queue discharge characteristics of through movement at a signalized intersection. Transportation Research Part C,2009,17(6),662-671.
[45]Lin F.B., Thomas D.R. Headway compression during queue discharge at signalized intersections. Transportation Research Record,2005, No.1920,81-85.
[46]Lin F.B., Tseng F.B., Su C.W. Variations in queue discharge patterns and their implications in analysis of signalized intersections. Transportation Research Record,2004, No.1883, 192-197.
[47]Liu H.C., Balke K.N., Lin W.H. A reverse causal-effect modeling approach for signal control of an oversaturated intersection. Transportation Research Part C,2008,16(6),742-754.
[48]Liu M.J., Mao B.H., Huang Y., Zhang J.P., Chen, S.K. Comparison of pre-& post-Olympic traffic:A case study of several roads in Beijing. Journal of Transportation Systems Engineering and Information,2008,8(6),67-72.
[49]Liu P., Lu J.J., Fan J.J., Pernia J.C., Sokolow G. Effects of U-turns on capacities of signalized intersections. Transportation Research Record,2005, No.1920,74-80.
[50]Lu J.J., Pernia J.C. The differences of drivering behavior among different driver age groups at signalized intersections. IATSS Research,2000,24(2),75-84.
[51]Lu Y.J. A study of left-turning maneuver time for signalized intersections. ITE Journal,1984, 54(10),42-47.
[52]Makishita H., Matsunaga K. Differences of drivers' reaction times according to age and mental workload. Accident Analysis and Prevention,2008,40(2),567-575.
[53]Mao B.H., Chen H.B., Chen S.L. Sustainability assessment of speed regulation of urban traffic. Journal of International Association of Traffic and Safety Sciences, Japan,2002,26(2),18-24.
[54]McDonald M., Hounsell F.O. Variability of flow parameters at traffic signal installations. Proceedings of 2nd International Conference on Road Traffic Control, London, UK.1986, 106-109.
[55]Mehmood A., Easa S.M. Modeling reaction time in car-following behaviour based on human factors. International Journal of Applied Science, Engineering and Technology,2009,5(2), 93-101.
[56]Mei M., Bullen A.G.R. The log-normal distribution for high traffic flows. Transportation Research Record,1993, No.1398,125-128.
[57]Messelodi S., Modena C.M., Cattoni G Vision-based bicycle/motorcycle classification. Pattern Recognition Letters,2007,28(13),1719-1726.
[58]Michael P.G., Leeming F.C., Dwyer W.O. Headway on urban streets:observational data and an intervention to decrease tailgating. Transportation Research Part F,2000,3(2),55-64.
[59]Minh C.C., Sano K. Analysis of motorcycle effects to saturation flow rate at signalized intersections in developing countries. Proceedings of the Eastern Asia Society for Transportation Studies,2003, Vol 5,1211-1222.
[60]Moavenian M., Khorrami H. A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification. Expert Systems with Applications, 2010,37(4),3088-3093.
[61]Mood A.M., Graybill F.A., Boes D.C. Introduction to the theory of statistics. McGraw-Hill Inc., USA,1974.
[62]Moskowitz K. Waiting for a gap in a traffic stream. Highway Research Board Proceedings, 1954, No.33,385-395.
[63]Moussavi M., Tarawneh M. Variability of departure headways at signalized intersections. Compendium of Technical Papers, Institute of Transportation Engineers,60th Annual Meeting, Orlando, Florida,1990,313-317.
[64]Park B., Won J., Yun I. Application of microscopic simulation model calibration and validation procedure:Case study of coordinated actuated signal system. Transportation Research Record,2006, No.1978,113-122.
[65]Parker M.T. The effect of heavy goods vehicles and following behavior on capacity at motorway roadwork sites. Traffic Engineering and Control,1996,37(9),524-531.
[66]Pelc公里 ans K., Suykens J.A.K., Van Gestel T., De Brabanter J., Lukas L., Hamers B., De Moor B., Vandewalle J. LS-SVMlab toolbox guide. ESAT-SCD-SISTA Technical Report 02-145, Katholieke Universiteit Leuven, Belgium,2002.
[67]Philippe F.L., Vallerand R.J., Richer I., Vallieres E., Bergeron J. Passion for driving and aggressive driving behavior:A look at their relationship. Journal of Applied Social Psychology, 2009,39(2),3020-3043.
[68]Rahman M.M., Ahmed S.N., Hassan T. Comparison of saturation flow rate at signalized intersections in Yokohamaand and Dhaka. Proceedings of the Eastern Asia Society for Transportation Studies,2005, Vol 5,959-966.
[69]Rama P., Kulmala R. Effects of variable message signs for slippery road conditions on driving speed and headways. Transportation Research Part F,2000,3(2),85-94.
[70]Ramsay E., Bunker J., Troutbeck R. Signalized intersection capacity reduction of trucks. Proceedings of the Fourth International Conference on Traffic and Transportation Studies, Science Press, Beijing, China,2004,793-802.
[71]Reid D.A. Traffic control by design. Proceedings of 2nd International Conference on Road Traffic Control, London, UK.1986,64-67.
[72]Rotwannasin P., Choocharukul K. Transferability of HCM to Asian countries:Exploratory evidence from Bangkok's multilane highways. Proceedings of the 3rd International SIIV Congress, September 2005.
[73]Sadeghhosseini S. Headway and platooning characteristics of vehicles on interstate highways. Doctoral dissertation of University of Illinois,2002,100-156.
[74]Sadoun B. An efficient simulation methodology for the design of traffic lights at intersections in urban areas. Simulation,2003,79(4),243-251.
[75]Sato T., Hino S. A spatial CGE analysis of road pricing in the Tokyo Metropolitan Area. Journal of the Eastern Asia Society for Transportation Studies,2005,6,608-623.
[76]Schrank D., Lomax T. The 2007 urban mobility report. Texas Transportation Institute, the Texas A&M University System, September 2007,33-36.
[77]Shin C.H., Choi K. Staturation flow rate estimation under rainy weather conditions for on-line traffic control purpose. KSCE Journal of Civil Engineering,1998,2(3),211-222.
[78]Stathopoulos A., Karlaftis M.G. Modeling duration of urban traffic congestion. Journal of Transportation Engineering,2002,128(6),587-590.
[79]Steuart G.N., Shin B.Y. The effect of small cars on the capacity of signalized urban intersections. Transportation Science,1978,12(3),250-263.
[80]Sullivan D.P., Troutbeck R. The use of Cowan's M3 distribution for modeling urban traffic flow. Traffic Engineering and Control,1994,35(7/8),445-450.
[81]Tableman M., Kim J.S. Survival analysis using S:Analysis of time-to-event data. Chapman & Hall/CRC, Texts in Statistical Science,2003.
[82]Tarko A.P., Tracz M. Uncertainty in saturation flow predictions. Transportation Research Circular E-C018:4th International Symposium on Highway Capacity,2000,310-321.
[83]Teply S., Jones A.M. Saturation flow:Do we speak the same language? Transportation Research Record,1991,No.1320,144-153.
[84]Teply S., Richardson D., Schnablegger J., Stephenson B. New features in the Canadian capacity guide for signalized intersections. ITE Journal,1985,55(10):40-46.
[85]Tiwari G., Mohan D., Fazio J. Conflict analysis for pedestrian of fatal crash locations in mixed traffic streams. Accident Analysis and Prevention,1998,30(2),207-215.
[86]Tolle J.E. The lognormal headway distribution model. Traffic Engineering and Control,1971, 13(1),22-24.
[87]Tong H.Y., Hung W.T. Neural network modeling of vehicle discharge headway at signalized intersection:model descriptions and results. Transportation Research Part A,2002,36(1), 17-40.
[88]Transportation Research Board. Highway Capacity Manual, fourth edition. National Research Council, Washington, DC,2000.
[89]Transportation Research Board. Highway Capacity Manual, third edition. National Research Council, Washington, DC,1985.
[90]Troutbeck R.J., Kako S. Limited priority merge at unsignalized intersections. Transportation Research Part A,1999,33(3-4),291-304.
[91]Tsao S.M., Chu S.W. Adjustment factors for heavy vehicles at signalized intersections. Journal of Transportation Engineering,1995,121(2),150-157.
[92]Vapnik V.N. The nature of statistical learning theory. Springer,1995.
[93]Wang W., Chen S.Y., Qu G.F. Incident detection algorithm based on partial least squares regression. Transportation Research Part C,2008,16(1),54-70.
[94]Wasielewski P. Car-following headways on freeways interpreted by the semi-Poisson headway distribution model. Transportation Sciences,1979,13(1),36-55.
[95]Wasielewski P. The effect of car size on headways in freely flowing freeway traffic. Transportation Sciences,1981,15(4),364-378.
[96]Xie D.F., Gao Z.Y., Zhao X.M., Li K.P. Characteristics of mixed traffic flow with non-motorized vehicles and motorized vehicles at an unsignalized intersection. Physica A, 2009,388(10),2041-2050.
[97]Yagar S. Capacity of a signalized road junction:Critique and extensions. Transportation Research,1974,8(2),137-147.
[98]Yamamoto T., Kitamura R. An analysis of household vehicle holding durations considering intended holding durations. Transportation Research Part A,2000,34(5),339-351.
[99]Yang S-I., Frangopol D.M., Neves, L.C. Service life prediction of structural systems using lifetime functions with emphasis on bridges. Reliability Engineering & System Safety,2004, 86(1),39-51.
[100]Ye F., Zhang Y.L. Vehicle-type-specific headway analysis using freeway traffic data. Proceedings of the 88th Annual Meeting Transportation Research Board, Washington D.C. Paper #09-2997, Jan 11-14,2009.
[101]Yoshiyuki K., Shigenori S., Masahiko K., Takashi O. Effect on the left-turn saturation flow rate caused by turning-radius and turning-angle at signalized intersections. Infrastructure Planning Review,2002,19(4),739-744.
[102]北京交通发展研究中心(BJTRC).2009北京市交通发展年度报告.北京,2009.
[103]北京市发展和改革委员会,北京交通发展研究中心.“十二五”期间北京城市交通体系建设的目标、思路及措施研究.北京,2010.
[104]北京市交通委员会(BMCC),北京交通发展研究中心(BJTRC).北京市第三次交通综合调查简要报告.北京,2007.
[105]北京市交通委员会(BMCC).北京交通发展纲要2004-2020.北京,2005,5-7.
[106]陈绍宽,郭谨一,王璇,毛保华.信号交叉口延误计算方法的比较.北京交通大学学报,2005,29(3),77-80.
[107]陈团生.通勤者出行行为特征与分析方法研究.北京交通大学博士学位论文,2007,69-86.
[108]陈振起.混合交通交叉口直行机动车运行特性分析与建模.北京交通大学博士学位论文,2009.
[109]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机.北京：科学出版社,2004,235-244.
[110]高利平,孙全欣,刘明君,梁肖,毛保华.基于路边停车的机非混行延误模型与仿真研究.系统仿真学报,2010,22(3),808-812.
[111]高自友.大城市交通拥堵瓶颈的基础科学问题研究项目申请书.北京,2006.
[112]郭宏伟,高自友,赵小梅.基于持续模型的行人过街行为.吉林大学学报( 工学版),2009,39(sup2),35-40.
[113]郭谨一,毛保华,刘明君,高利平.单路口感应信号控制系统仿真研究.第20届中国控制与决策年会,烟台,2008,2177-2181.
[114]何民.混合交通流微观仿真关键技术研究.北京工业大学博士学位论文,2003.
[115]何民.交通流跟驰问题研究.北京工业大学硕士学位论文,2000.
[116]黄海军.城市交通网络动态建模与交通行为研究.管理学报,2005,2(1),18-22.
[117]黄荣,郭敏,陈绍宽,梁肖.拥挤状态下信号交义口通行能力计算方法.交通运输系统工程与信息,2008,8(3),58-65.
[118]贾鸿飞,隽志才,魏丽英.饱和流率的部分影响因素修正系数的标定.吉林工业大学自然科学学报,2000,30(2),99-102.
[119]贾顺平,彭宏勤,郭谨一,陈海波.城市混合交通中自行车队机动车道行驶影响的定量分析.交通运输系统工程与信息,2008,8(2),58-63.
[120]金莉.混合交通流下信号交叉口交通组织优化问题研究.陕西西安,长安大学硕士学位论文,2006,12-17.
[121]靳雪翔.数据驱动的城市交通系统建模分析若干问题研究.清华大学博士学位论文,北京,2009,40-58.
[122]李建新,毛保华.混合交通环境下有信号交叉口通行能力研究.交通运输系统工程与信息,2001,1(2),119-123.
[123]零点研究咨询集团,北汽福田汽车有限公司.2008福田指数——中国居民生活机动性指数研究报告.http://auto.sohu.com/20080612/n257459328.shtml, Accessed on 2008-12-7.
[124]刘江,吕津燕,荣建,任福田.车头时距分布模型及其在山区双车道公路的应用.交通运输工程与信息学报,2004,2(4),16-22.
[125]罗霞,杜进有,霍娅敏.车头间距分布规律研究.西南交通大学学报,2001,36(2),113-116.
[126]马驰,阮秋琦.基于离散微粒群优化算法的SVM参数选择.计算机技术与发展,2007,17(12),20-23.
[127]毛保华,贾顺平等.北京交通结构优化研究报告.北京：北京交通大学,北京交通发展研究中心,2008.
[128]毛保华,孙壮志,贾顺平等.区域交通组织优化方法及实践研究.北京：人民交通出版社,2007.
[129]裴玉龙,高晗.城市快速路匝道连接段车头时距分布模型.交通与计算机,2007,25(5),4-7.
[130]裴玉龙,李松龄,薛长龙.快速路交织区车头时距分布特征.公路交通科技,2008,25(6),111-114.
[131]邵长桥,杜晓辉,李光芹.信号交叉口排队离散车头时距统计分析.公路交通科技,2003,20(4),76-79.
[132]邵长桥.平面信号交叉口延误分析.北京工业大学博士学位论文,2002.
[133]苏岳龙,魏铮,姚丹亚,张毅.混合交通状态下交叉口排队机动车流启动车头时距研究和应用.公路交通科技,2008,25(7),113-117.
[134]孙剑,杨晓光,刘好德.微观交通仿真系统参数校正研究.系统仿真学报,2007,19(1),48-50.
[135]田丰,敖进涛,史其信.神经网络方法在车头时距特性分析中的应用.公路,2009,53(8),260-264.
[136]王殿海,杨少辉,景春光.累计曲线法计算饱和流率和相位损失时间.交通运输工程学报,2003,3(3),75-78.
[137]王文群,陈海波,玛格丽特·贝尔.交通事故持续时间分析综述.交通运输系统工程与信息,2005,5(3),127-140.
[138]杨晓光,庄斌,李克平.信号交叉口饱和流率和启动延误的影响分析.同济大学学报(自然科学版),2006,34(6),738-743.
[139]姚志廷.铺面养护决策支援分析模式之研究.中国台湾,国立中央大学博士学位论文,2005.
[140]姚智胜,邵春福,熊志华,岳吴.基于主成分和支持向量机的道路网短时交通流量预测.吉林大学学报(工学版),2008,38(1),48-52.
[141]于滨,杨忠振,林剑艺.应用支持向量机预测公交车运行时间.系统工程理论与实践,2007,27(4),160-164.
[142]袁晶矜,袁振洲.信号交叉口通行能力计算方法的比较分析.公路交通技术,2006,No 5,123-129.
[143]曾平毅,林丰博,张瓊文.市区号志化路口停等率疏解特性.中国土木水利工程学刊,2007,19(2),289-300.
[144]曾平毅,张瓊文,陈文斌.无冲突左转停等车之疏解特性.运输计划季刊,2006,35(2),233-260.
[145]张瓊文,林丰博,曾平毅.机车专用道车队扩散行为之探封.中国土木水利工程学刊,2009,21(1),51-60.
[146]赵林,邵长桥,荣建.交通组成对信号交叉口饱和流率影响研究.公路工程,2008,33(4),88-91.
[147]赵娜乐,于雷,耿彦斌,陈旭梅.基于SVM的数据层多元ITS数据融合方法初探.交通运输系统工程与信息,2007,7(2),32-38.
[148]中华人民共和国国家统计局.中国统计年鉴2009.北京：中国统计出版社,2009.
[2]Akcelik R., Chung E. Calibration of the bunched exponential distribution of arrival headways. Road and Transport Research,1994,3(1),42-59.
[3]Al-Ghamdi A.S. Entering headway for through movements at urban signalized intersections. Transportation Research Record,1999, No.1678,42-47.
[4]Bester C.J., Varndell P.J. The effect of a leading green phase on the startup lost time of opposing vehicles.21st Annual South African Transport Conference, South Africa,15-18 July, 2002.
[5]Bhat C.R., Frusti T., Zhao H.M., Schonfelder S., Axhausen K.W. Intershopping duration:An analysis using multiweek data. Transportation Research Part B,2004,38(1),39-60.
[6]Bonneson J.A. Lane volume and saturation flow rate for multilane intersection approach. Journal of Transportation Engineering,1998,124(3),240-245.
[7]Bonneson J.A. Study of headway and lost time at single-point urban interchanges. Transportation Research Record, Transportation Research Board of the National Academies, Washington, D.C.,1992, No.1365,30-39.
[8]Botha J.L., Kruse T.R. Flow rates at signalized intersections under cold winter conditions. Journal of Transportation Engineering,1992,118(3),439-450.
[9]Boumediene A., Brahimi K., Belguesmia N., Bouakkaz K. Saturation flow versus green time at two-stage signal controlled intersections. Transport,2009,24(4),288-295.
[10]Brackstone M., Waterson B., McDonald M. Determinants of following headway in.congested traffic. Transportation Research Part F,2009,12(2),131-142.
[11]Briggs T. Headways on crossing the stop line after queuing at traffic lights. Traffic Engineering and Control,1977,18(5),264-265.
[12]Buckley D.J. A semi-Poisson model of traffic flow. Transportation Science,1968,2(2), 107-133.
[13]Cao X.B., Xu Y.W., Chen D., Qiao H. Associated evolution of a support vector machine-based classifier for pedestrian detection. Information Sciences,2009,179(8),1070-1077.
[14]Carstens R.L. Some traffic parameters at signalized intersections. Traffic Engineering,1971, 41(11),33-36.
[15]Chung U. Development of an accident duration prediction model on the Korean freeway systems. Accident Analysis and Prevention,2010,42(2),282-289.
[16]Conforti D., Guido R. Kernel based support vector machine via semidefinite programming: Application to medical diagnosis. Computers and Operations Research,2010,37(8), 1389-1394.
[17]Cowan R.J. Useful headway models. Transportation Research,1975,9(6),371-375.
[18]Cox D.R. Regression models and life tables. Journal of the Royal Statistical Society. Series B, 1972,34(2),187-220.
[19]Evgeniou T., Poggio T., Pontil M., Verri A. Regularization and statistical learning theory for data analysis. Computational Statistics & Data Analysis,2002,38(4),421-432.
[20]Gao L.P., Liu M.J., Sun Z.Z., Mao B.H. Simulation on impact of information guidance on regional traffic flow. Journal of Transportation Systems Engineering and Information Technology,2008,8(4),63-69.
[21]Gerlough D.L., Wagner F.A. NCHRP report 32:Improved criteria for traffic signals at individual intersections. TRB, National Research Council, Washington, D.C.,1967,38-42.
[22]Gonzalez L.F.C. Discharge headway at signalized intersections in Monterrey, Nuevo Leon, Mexico. Doctoral Dissertation of University of Texas, USA,2006.
[23]Greenshields B.D., Schapiro D., Ericksen E.L. Traffic performance at urban intersections. Eno Foundation for Highway Traffic Control,1947,23-30.
[24]Greenshields B.D., Weida F.M. Statistics with application to highway traffic analyses. Eno Foundation for Highway Traffic Control,1952,164-169.
[25]Griffiths J.D., Hunt J.G Vehicle headway in urban areas. Traffic Engineering and Control, 1991,32(10),458-462.
[26]Hagring O. Estimation of parameters in distribution of headways in roundabouts. Journal of Transportation Engineering,2002,128(5),403-411.
[27]Hatzopoulou M., Miller E.J. Institutional integration for sustainable transportation policy in Canada. Transport Policy,2008,15(3),149-162.
[28]He M., Liu L., Wei H., Dong R. Characteristics of signalized U-turn movements in headway distribution, capacity, and control delay:Case study of intersections in Kunning, China. Proceedings of the 88th Annual Meeting Transportation Research Board, Washington D.C. Paper #09-2550, Jan 11-14 2009.
[29]Helbing D. Traffic and related self-driven many-particle systems. Reviews of Modern Physics,2001,73(4),1067-1141.
[30]Hewage K.N., Ruwanpura J.Y. Optimization of traffic signal light timeing using simulation. Proceedings of the 2004 Winter Simulation Conference,2004,1428-1433.
[31]Highway Research Board. Highway Capacity Manual:Practical applications for research. Department of Traffic and Operations, Committee on Highway Capacity, United States Government Printing Office, Washington, DC,1950,39-41.
[32]Hung W.T., Tian F., Tong H.Y. Discharge headway at signalized intersections in Hong Kong. Journal of Advance Transportation,2003,37(1),105-113.
[33]Jin X.X., Zhang Y., Wang F., Li L., Yao D.Y., Su Y.L., Wei Z. Departure headways at signalized intersections:A log-normal distribution model approach. Transportation Research Part C,2009,17(3),318-327.
[34]Joseph J., Chang G.L. Saturation flow rates and maximum critical lane volumes for planning applications in Maryland. Journal of Transportation Engineering,2005,131(12),946-952.
[35]Jovanis P.P., Chang H.L. Disaggregate model of highway accident occurrence using survival theory. Accident Analysis and Prevention,1989,21(5),445-458.
[36]Jr May A.D. Gap availability studies. Highway Research Record, HRB, Washington D.C., 1965, No.72,105-136.
[37]Jr May A.D., Wagner F.A. Headway characteristics and interrelationships of fundamental characteristics of traffic flow. Highway Research Board Proceedings,1960, Vol.39,524-547.
[38]Jr. Denney R.W., Curtis E., Head L. Long green times and cycles at congested traffic signals. TRB 2009 Annual Meeting CD-ROM, Jan 11-14 2009.
[39]Lee H.S., Do T.W. Saturation headway of through movement at signalized intersections in urban area. Journal of Transportation Research Society of Korea,2002,20(5),23-31.
[40]Lee J., Chen R.L. Entering headway at signalized intersections in a small metropolitan area. Transportation Research Record,1986, No 1091,117-126.
[41]Lee T.H. Saturation headways and lost times at signalized intersections. Doctoral dissertation of University of Kansa,1995.
[42]Li H.L., Prevedouros P.D. Detailed observations of saturation headways and start-up lost times. Transportation Research Record,2002, No.1802,44-53.
[43]Li X.G., Lord D., Zhang Y.L., Xie Y.C. Predicting motor vehicle crashes using Support Vector Machine models. Accident Analysis & Prevention,2008,40(4),1611-1618.
[44]Limanond T., Chookerd S., Roubtonglang N. Effects of countdown timers on queue discharge characteristics of through movement at a signalized intersection. Transportation Research Part C,2009,17(6),662-671.
[45]Lin F.B., Thomas D.R. Headway compression during queue discharge at signalized intersections. Transportation Research Record,2005, No.1920,81-85.
[46]Lin F.B., Tseng F.B., Su C.W. Variations in queue discharge patterns and their implications in analysis of signalized intersections. Transportation Research Record,2004, No.1883, 192-197.
[47]Liu H.C., Balke K.N., Lin W.H. A reverse causal-effect modeling approach for signal control of an oversaturated intersection. Transportation Research Part C,2008,16(6),742-754.
[48]Liu M.J., Mao B.H., Huang Y., Zhang J.P., Chen, S.K. Comparison of pre-& post-Olympic traffic:A case study of several roads in Beijing. Journal of Transportation Systems Engineering and Information,2008,8(6),67-72.
[49]Liu P., Lu J.J., Fan J.J., Pernia J.C., Sokolow G. Effects of U-turns on capacities of signalized intersections. Transportation Research Record,2005, No.1920,74-80.
[50]Lu J.J., Pernia J.C. The differences of drivering behavior among different driver age groups at signalized intersections. IATSS Research,2000,24(2),75-84.
[51]Lu Y.J. A study of left-turning maneuver time for signalized intersections. ITE Journal,1984, 54(10),42-47.
[52]Makishita H., Matsunaga K. Differences of drivers' reaction times according to age and mental workload. Accident Analysis and Prevention,2008,40(2),567-575.
[53]Mao B.H., Chen H.B., Chen S.L. Sustainability assessment of speed regulation of urban traffic. Journal of International Association of Traffic and Safety Sciences, Japan,2002,26(2),18-24.
[54]McDonald M., Hounsell F.O. Variability of flow parameters at traffic signal installations. Proceedings of 2nd International Conference on Road Traffic Control, London, UK.1986, 106-109.
[55]Mehmood A., Easa S.M. Modeling reaction time in car-following behaviour based on human factors. International Journal of Applied Science, Engineering and Technology,2009,5(2), 93-101.
[56]Mei M., Bullen A.G.R. The log-normal distribution for high traffic flows. Transportation Research Record,1993, No.1398,125-128.
[57]Messelodi S., Modena C.M., Cattoni G Vision-based bicycle/motorcycle classification. Pattern Recognition Letters,2007,28(13),1719-1726.
[58]Michael P.G., Leeming F.C., Dwyer W.O. Headway on urban streets:observational data and an intervention to decrease tailgating. Transportation Research Part F,2000,3(2),55-64.
[59]Minh C.C., Sano K. Analysis of motorcycle effects to saturation flow rate at signalized intersections in developing countries. Proceedings of the Eastern Asia Society for Transportation Studies,2003, Vol 5,1211-1222.
[60]Moavenian M., Khorrami H. A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification. Expert Systems with Applications, 2010,37(4),3088-3093.
[61]Mood A.M., Graybill F.A., Boes D.C. Introduction to the theory of statistics. McGraw-Hill Inc., USA,1974.
[62]Moskowitz K. Waiting for a gap in a traffic stream. Highway Research Board Proceedings, 1954, No.33,385-395.
[63]Moussavi M., Tarawneh M. Variability of departure headways at signalized intersections. Compendium of Technical Papers, Institute of Transportation Engineers,60th Annual Meeting, Orlando, Florida,1990,313-317.
[64]Park B., Won J., Yun I. Application of microscopic simulation model calibration and validation procedure:Case study of coordinated actuated signal system. Transportation Research Record,2006, No.1978,113-122.
[65]Parker M.T. The effect of heavy goods vehicles and following behavior on capacity at motorway roadwork sites. Traffic Engineering and Control,1996,37(9),524-531.
[66]Pelc公里 ans K., Suykens J.A.K., Van Gestel T., De Brabanter J., Lukas L., Hamers B., De Moor B., Vandewalle J. LS-SVMlab toolbox guide. ESAT-SCD-SISTA Technical Report 02-145, Katholieke Universiteit Leuven, Belgium,2002.
[67]Philippe F.L., Vallerand R.J., Richer I., Vallieres E., Bergeron J. Passion for driving and aggressive driving behavior:A look at their relationship. Journal of Applied Social Psychology, 2009,39(2),3020-3043.
[68]Rahman M.M., Ahmed S.N., Hassan T. Comparison of saturation flow rate at signalized intersections in Yokohamaand and Dhaka. Proceedings of the Eastern Asia Society for Transportation Studies,2005, Vol 5,959-966.
[69]Rama P., Kulmala R. Effects of variable message signs for slippery road conditions on driving speed and headways. Transportation Research Part F,2000,3(2),85-94.
[70]Ramsay E., Bunker J., Troutbeck R. Signalized intersection capacity reduction of trucks. Proceedings of the Fourth International Conference on Traffic and Transportation Studies, Science Press, Beijing, China,2004,793-802.
[71]Reid D.A. Traffic control by design. Proceedings of 2nd International Conference on Road Traffic Control, London, UK.1986,64-67.
[72]Rotwannasin P., Choocharukul K. Transferability of HCM to Asian countries:Exploratory evidence from Bangkok's multilane highways. Proceedings of the 3rd International SIIV Congress, September 2005.
[73]Sadeghhosseini S. Headway and platooning characteristics of vehicles on interstate highways. Doctoral dissertation of University of Illinois,2002,100-156.
[74]Sadoun B. An efficient simulation methodology for the design of traffic lights at intersections in urban areas. Simulation,2003,79(4),243-251.
[75]Sato T., Hino S. A spatial CGE analysis of road pricing in the Tokyo Metropolitan Area. Journal of the Eastern Asia Society for Transportation Studies,2005,6,608-623.
[76]Schrank D., Lomax T. The 2007 urban mobility report. Texas Transportation Institute, the Texas A&M University System, September 2007,33-36.
[77]Shin C.H., Choi K. Staturation flow rate estimation under rainy weather conditions for on-line traffic control purpose. KSCE Journal of Civil Engineering,1998,2(3),211-222.
[78]Stathopoulos A., Karlaftis M.G. Modeling duration of urban traffic congestion. Journal of Transportation Engineering,2002,128(6),587-590.
[79]Steuart G.N., Shin B.Y. The effect of small cars on the capacity of signalized urban intersections. Transportation Science,1978,12(3),250-263.
[80]Sullivan D.P., Troutbeck R. The use of Cowan's M3 distribution for modeling urban traffic flow. Traffic Engineering and Control,1994,35(7/8),445-450.
[81]Tableman M., Kim J.S. Survival analysis using S:Analysis of time-to-event data. Chapman & Hall/CRC, Texts in Statistical Science,2003.
[82]Tarko A.P., Tracz M. Uncertainty in saturation flow predictions. Transportation Research Circular E-C018:4th International Symposium on Highway Capacity,2000,310-321.
[83]Teply S., Jones A.M. Saturation flow:Do we speak the same language? Transportation Research Record,1991,No.1320,144-153.
[84]Teply S., Richardson D., Schnablegger J., Stephenson B. New features in the Canadian capacity guide for signalized intersections. ITE Journal,1985,55(10):40-46.
[85]Tiwari G., Mohan D., Fazio J. Conflict analysis for pedestrian of fatal crash locations in mixed traffic streams. Accident Analysis and Prevention,1998,30(2),207-215.
[86]Tolle J.E. The lognormal headway distribution model. Traffic Engineering and Control,1971, 13(1),22-24.
[87]Tong H.Y., Hung W.T. Neural network modeling of vehicle discharge headway at signalized intersection:model descriptions and results. Transportation Research Part A,2002,36(1), 17-40.
[88]Transportation Research Board. Highway Capacity Manual, fourth edition. National Research Council, Washington, DC,2000.
[89]Transportation Research Board. Highway Capacity Manual, third edition. National Research Council, Washington, DC,1985.
[90]Troutbeck R.J., Kako S. Limited priority merge at unsignalized intersections. Transportation Research Part A,1999,33(3-4),291-304.
[91]Tsao S.M., Chu S.W. Adjustment factors for heavy vehicles at signalized intersections. Journal of Transportation Engineering,1995,121(2),150-157.
[92]Vapnik V.N. The nature of statistical learning theory. Springer,1995.
[93]Wang W., Chen S.Y., Qu G.F. Incident detection algorithm based on partial least squares regression. Transportation Research Part C,2008,16(1),54-70.
[94]Wasielewski P. Car-following headways on freeways interpreted by the semi-Poisson headway distribution model. Transportation Sciences,1979,13(1),36-55.
[95]Wasielewski P. The effect of car size on headways in freely flowing freeway traffic. Transportation Sciences,1981,15(4),364-378.
[96]Xie D.F., Gao Z.Y., Zhao X.M., Li K.P. Characteristics of mixed traffic flow with non-motorized vehicles and motorized vehicles at an unsignalized intersection. Physica A, 2009,388(10),2041-2050.
[97]Yagar S. Capacity of a signalized road junction:Critique and extensions. Transportation Research,1974,8(2),137-147.
[98]Yamamoto T., Kitamura R. An analysis of household vehicle holding durations considering intended holding durations. Transportation Research Part A,2000,34(5),339-351.
[99]Yang S-I., Frangopol D.M., Neves, L.C. Service life prediction of structural systems using lifetime functions with emphasis on bridges. Reliability Engineering & System Safety,2004, 86(1),39-51.
[100]Ye F., Zhang Y.L. Vehicle-type-specific headway analysis using freeway traffic data. Proceedings of the 88th Annual Meeting Transportation Research Board, Washington D.C. Paper #09-2997, Jan 11-14,2009.
[101]Yoshiyuki K., Shigenori S., Masahiko K., Takashi O. Effect on the left-turn saturation flow rate caused by turning-radius and turning-angle at signalized intersections. Infrastructure Planning Review,2002,19(4),739-744.
[102]北京交通发展研究中心(BJTRC).2009北京市交通发展年度报告.北京,2009.
[103]北京市发展和改革委员会,北京交通发展研究中心.“十二五”期间北京城市交通体系建设的目标、思路及措施研究.北京,2010.
[104]北京市交通委员会(BMCC),北京交通发展研究中心(BJTRC).北京市第三次交通综合调查简要报告.北京,2007.
[105]北京市交通委员会(BMCC).北京交通发展纲要2004-2020.北京,2005,5-7.
[106]陈绍宽,郭谨一,王璇,毛保华.信号交叉口延误计算方法的比较.北京交通大学学报,2005,29(3),77-80.
[107]陈团生.通勤者出行行为特征与分析方法研究.北京交通大学博士学位论文,2007,69-86.
[108]陈振起.混合交通交叉口直行机动车运行特性分析与建模.北京交通大学博士学位论文,2009.
[109]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机.北京：科学出版社,2004,235-244.
[110]高利平,孙全欣,刘明君,梁肖,毛保华.基于路边停车的机非混行延误模型与仿真研究.系统仿真学报,2010,22(3),808-812.
[111]高自友.大城市交通拥堵瓶颈的基础科学问题研究项目申请书.北京,2006.
[112]郭宏伟,高自友,赵小梅.基于持续模型的行人过街行为.吉林大学学报( 工学版),2009,39(sup2),35-40.
[113]郭谨一,毛保华,刘明君,高利平.单路口感应信号控制系统仿真研究.第20届中国控制与决策年会,烟台,2008,2177-2181.
[114]何民.混合交通流微观仿真关键技术研究.北京工业大学博士学位论文,2003.
[115]何民.交通流跟驰问题研究.北京工业大学硕士学位论文,2000.
[116]黄海军.城市交通网络动态建模与交通行为研究.管理学报,2005,2(1),18-22.
[117]黄荣,郭敏,陈绍宽,梁肖.拥挤状态下信号交义口通行能力计算方法.交通运输系统工程与信息,2008,8(3),58-65.
[118]贾鸿飞,隽志才,魏丽英.饱和流率的部分影响因素修正系数的标定.吉林工业大学自然科学学报,2000,30(2),99-102.
[119]贾顺平,彭宏勤,郭谨一,陈海波.城市混合交通中自行车队机动车道行驶影响的定量分析.交通运输系统工程与信息,2008,8(2),58-63.
[120]金莉.混合交通流下信号交叉口交通组织优化问题研究.陕西西安,长安大学硕士学位论文,2006,12-17.
[121]靳雪翔.数据驱动的城市交通系统建模分析若干问题研究.清华大学博士学位论文,北京,2009,40-58.
[122]李建新,毛保华.混合交通环境下有信号交叉口通行能力研究.交通运输系统工程与信息,2001,1(2),119-123.
[123]零点研究咨询集团,北汽福田汽车有限公司.2008福田指数——中国居民生活机动性指数研究报告.http://auto.sohu.com/20080612/n257459328.shtml, Accessed on 2008-12-7.
[124]刘江,吕津燕,荣建,任福田.车头时距分布模型及其在山区双车道公路的应用.交通运输工程与信息学报,2004,2(4),16-22.
[125]罗霞,杜进有,霍娅敏.车头间距分布规律研究.西南交通大学学报,2001,36(2),113-116.
[126]马驰,阮秋琦.基于离散微粒群优化算法的SVM参数选择.计算机技术与发展,2007,17(12),20-23.
[127]毛保华,贾顺平等.北京交通结构优化研究报告.北京：北京交通大学,北京交通发展研究中心,2008.
[128]毛保华,孙壮志,贾顺平等.区域交通组织优化方法及实践研究.北京：人民交通出版社,2007.
[129]裴玉龙,高晗.城市快速路匝道连接段车头时距分布模型.交通与计算机,2007,25(5),4-7.
[130]裴玉龙,李松龄,薛长龙.快速路交织区车头时距分布特征.公路交通科技,2008,25(6),111-114.
[131]邵长桥,杜晓辉,李光芹.信号交叉口排队离散车头时距统计分析.公路交通科技,2003,20(4),76-79.
[132]邵长桥.平面信号交叉口延误分析.北京工业大学博士学位论文,2002.
[133]苏岳龙,魏铮,姚丹亚,张毅.混合交通状态下交叉口排队机动车流启动车头时距研究和应用.公路交通科技,2008,25(7),113-117.
[134]孙剑,杨晓光,刘好德.微观交通仿真系统参数校正研究.系统仿真学报,2007,19(1),48-50.
[135]田丰,敖进涛,史其信.神经网络方法在车头时距特性分析中的应用.公路,2009,53(8),260-264.
[136]王殿海,杨少辉,景春光.累计曲线法计算饱和流率和相位损失时间.交通运输工程学报,2003,3(3),75-78.
[137]王文群,陈海波,玛格丽特·贝尔.交通事故持续时间分析综述.交通运输系统工程与信息,2005,5(3),127-140.
[138]杨晓光,庄斌,李克平.信号交叉口饱和流率和启动延误的影响分析.同济大学学报(自然科学版),2006,34(6),738-743.
[139]姚志廷.铺面养护决策支援分析模式之研究.中国台湾,国立中央大学博士学位论文,2005.
[140]姚智胜,邵春福,熊志华,岳吴.基于主成分和支持向量机的道路网短时交通流量预测.吉林大学学报(工学版),2008,38(1),48-52.
[141]于滨,杨忠振,林剑艺.应用支持向量机预测公交车运行时间.系统工程理论与实践,2007,27(4),160-164.
[142]袁晶矜,袁振洲.信号交叉口通行能力计算方法的比较分析.公路交通技术,2006,No 5,123-129.
[143]曾平毅,林丰博,张瓊文.市区号志化路口停等率疏解特性.中国土木水利工程学刊,2007,19(2),289-300.
[144]曾平毅,张瓊文,陈文斌.无冲突左转停等车之疏解特性.运输计划季刊,2006,35(2),233-260.
[145]张瓊文,林丰博,曾平毅.机车专用道车队扩散行为之探封.中国土木水利工程学刊,2009,21(1),51-60.
[146]赵林,邵长桥,荣建.交通组成对信号交叉口饱和流率影响研究.公路工程,2008,33(4),88-91.
[147]赵娜乐,于雷,耿彦斌,陈旭梅.基于SVM的数据层多元ITS数据融合方法初探.交通运输系统工程与信息,2007,7(2),32-38.
[148]中华人民共和国国家统计局.中国统计年鉴2009.北京：中国统计出版社,2009.