大范围战略交通协调控制系统关键技术研究
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摘要
依托国家“863”发展计划课题,本文对大范围战略交通协调控制系统的关键技术进行了研究,文中介绍了大范围战略交通协调控制系统研究的目的及意义,给出系统的研究思路和方法;然后,给出了大范围战略交通协调控制系统的系统构成、逻辑框架、物理框架;其次提出了基于手机经纬度信息的路段行程时间提取技术,包括基于手机定位的地图匹配模型、路段行程时间分配模型、样本量充足情况下和不足情况下的路段平均行程时间/速度的统计模型等。同时,给出了基于线圈检测器的路段平均行程时间提取方法,提出了基于自适应加权平均的融合技术、基于小波分析的融合技术和基于BP神经网络的非线性组合融合技术;并且,提出了基于手机浮动车的先验OD矩阵获取技术,包括基于标签算法的浮动车状态判别和出行OD的扩样方法,设计了基于卡尔曼滤波的估计模型及算法,给利用手机数据给出了相关的分配矩阵的计算方法;提出了区域战略交通控制策略优化方法、区域间交通协调控制策略及模型及区域边界交通协调控制策略及基于模糊控制的协调控制模型。并用实例进行了所提出模型及算法的验证。结果表明,本文所提出的模型、算法具有较好的精确性和实用性。
With the rapid development and urbanization of cities, traffic congestion become heavier and have inverse influence on the function of cities.With the deeper research of traffic subject, the mode of allieviate traffic congestion is changed from infrastruce construction to management, for example:traffic signal control system and traffic guidance system, to inhance the operation potential of infrastructure.Worldwide experts ,scholars and engineering technicians are paying more attention to the two system.After several decades of research, some achievement are obtained,such as TRANSYT of USA and UK,SCATS of Austrilia,SCOOT of UK, RHODES and OPEC of USA, KYOSAN of Japan and ITACA of Spain.Several tens of big cities in China imported and applied these advanced systems.These system have some effect on the allievating traffic congestion to some extent, however,it could not efficiently avoid traffic congestion and allieviate traffic congestion.Meanwhile, countries all around the world have construted traffic guidance sytem, such as VICS and DRGS of Japan, TRAVTEK of USA,ALI-SCOUT of Europe. In our country, the guidance by broadcast and VMS on freeway, expressway and some urban road are constructed, but there isn’t a shaping and complete traffic guidance system.
     Acoordig to the above analysis, within the research of traffic signal control and traffic guidance system, some questions still exist, which are as follows:
     (1)The two systems are constructed isolately, it is difficult for the information to share with each other and the strategies to coordinate with each other;
     (2)The existed traffic signal control system is designed without considering the saturated traffic flow. Facing current saturated traffic flow, for the congestion caused by saturated flow, the existing signal control system is unable to solve.
     (3)The existed traffic signal control system only have the function of adaption, they do not have intelligence and self-organization. The signal timing is ajusted within small step as the traffic flow changes, they do not have the ability to coordinate the traffiic flow of all node within the roadnetwork macroscopicly;
     (4)In the existed traffic signal control system, the regional control strategies are fixed, they could not adapte to the traffic flow flexiblely;what’more, the control strategies could coordinate among regions, so it could not adjust traffic flow by space resource and time resource, which aggravate traffic congestion and could not rapidly evacuate traffic congestion;
     (5)The existed traffic information infrastructure could not meet the needs, which are variety of information, high accuracy and low cost, of traffic signal control and traffic guidance.
     Under this backgroud, the national science and technology department establish“863 plan”project.Jilin University and Shanghai Baokang cooperated to develop Novel Intelligent Traffic Control System(NITCS).Large-scale Strategic Traffic Coordination System(LSTCS) is the central subsystem of NITCS.It is also the subsystem with higher intelligence and function of macrosopic decision The research on the key technology of LSTCS offers technical guarantee for NITCS. It has strong theory value and big significance. Therefore, depending on the national“863 plan”project( 2006AA11Z228, 2007AA11Z245 and 2007AA12Z242), this disseration aims to research on LSTCS. The dissertation is divided into 7 chapters. The 2nd,3rd,4th,5th,6th chapter is the focuse. The 1stand 7th chapter introduces the backgourd, significance, research clues and the whole disseration summery and prospects.The main key technologies of this disseration involves:Achitecture desgin of LSTCS, Travel time extraction tehnology based on cellular phone probe car, Traffic information fusion technology based on fixed detector and probe car,Dyanmic OD estimation technology based on cellular phone probe car and Large-scale strategic traffic coordination control strategies and models, which are as follows:
     (1) Achitecture desgin of LSTCS
     Firstly, this disseration establish the system composition and logical architecture of NITCS based on dynamic hierachical coordination theory of large-scale system. Comparing to the traditional traffic signal control system, the difference and characteristic are proposed. Meanwhile, the system composition, physical architecture, logical architecture and system functions of LSTCS are mainly proposed, which break through the limitation of tradtional signal control system, which only has center supervising function.
     (2) Travel time extraction tehnology based on cellular phone probe car
     Analysis and introduction of state-of-art of this technology and cellular phone location technology are offered.The travel time extraction process based on cellular phone hand over is introduced. The travel time extraction process based on cellular phone latitude and longitude information is mainly proposed. Different from GPS, the celllar phone location has the charateristis of long sampling interval and low accuracy of location. Considering these questions, the travel time extraction process, the pretreatment methods, the map matching method under long sampling interval and single car travel time assignment method based on probability. What else, the link average travel time statistical method under sufficient samples and unsufficient sample are proposed.The field position data and simulation data of Changchun roadnetwork are applied to test the above methods.It is conluded that the above methods have good accuracy and efficiency.
     (3) Traffic information fusion technology based on fixed detector and probe car Analysis and introduction of state-of-art of this technology is offered. The travel time estimation based on g factor of single loop detector is proposed. In this dissertation, the estimated travel time from fixed loop detector and probe car are fused. A fusion method based on adaptive weighed average are proposed, in which the adaptive weight is computed by the dynamic error; Futhermore, the wavelet analysis are applied to the travel time fusion.Then, the result of the above method are used as the input, the nonlinear combination fusion method based on BP neural network is proposed. VISSIM simulated data of Changchun roadnetwork are applied to test the above methods.The results show that both of above methods have good accuracy. The fusion accaracy based on BP neural network has higher accuracy. The fusion method inhances the accuracy of the single resouce data.
     (4) Dyanmic OD estimation technology based on cellular phone probe car According to classification by the time dimension, OD mapping method, model estimation level, operation modes, the used roadnetwork and data collection method, the OD estimation state-of-art are analyzed. The prior OD collection process based on cellular phone base station is introduced. The prior OD collection method based on cellular phone longitude and latitude is maily proposed,including cellular phone probe car runing situation identification based on labelling algorithm, the OD sampling expansion method based on simple coefficients and fillment and reference method.What’more, the state space model of dynamic OD estimation based on kalman filter are established.The assignment matrix estimation method and kalman filter algorithm are also proposed. The field data and simulated data of Changchun roadnetwork are applied to test these methods.The results analysis show that the above methods has good accuracy and efficiency and could meet the need of real-time dynamic OD application.
     (5) Large-scale strategic traffic coordination control strategies and models The state-of-art of this technology is offered.According to requirement of the real saturated traffic flow, the Large-scale strategic traffic coordination control strategies are made up of region strategic traffic control strategies, traffic coordination control strateiges among regions and traffic coordination control strateiges of regional boundaries. This dissertation analyzes the influence factor of regional strategic traffic control strategies and establishs a regional strategic traffic control strategies optimization method based on hierachical support vector machine;Then, the running opportunity of traffic coordination control strateiges among regions and the traffic coordination control models among regions based on dynamic OD are proposed. On basis of regional boundary traffic situation identification, a regional boundary coordination control model based on fuzzy control is proposed. The VISSIM simulated data of Changchun roadnetwork are applied to test these methods.It comes to conclusion that the proposed strategies and methods have good efficiency and feasibility.It could inhance the control benefit of the whole system and efficiently allieviate traffic congestion.
     This disseration aims to break through traditional traffic signal control system’s limitation, enrich traffic information collection methods, enhance traffic information accuarcy, faster the integration of traffic signal control and traffic guidance, decrease traffic congestion and improve travel environment. Using several advanced methods, such as probability method, statistics method and artificial method, to research on the key technologies of LSTCS.It offers key technology basis for the construction of LSTCS and NITCS.
     At present, the ownership of cellular phone is already 0.608 billion.In the future, the ownership will be incresed.Therefore, the traffic collection technology based on cellular phone probe car will have large market prospect.Meanwhile, with the ehanced understanding of traffic manager and increased signal control nodes, the LSTCS, which has the characteristics of intelligence and macrosopic coordination, will achieve widely engineeing application and improve traffic congestion of cities.
引文
[1]林瑜.信号控制交叉口群交通阻塞机理解析方法[D].同济大学博士论文.2006
    [2]高中岗.从京、沪城市交通政策的差异看北京的交通拥堵[J].城市规划学刊.2004(4):35-39
    [3] Chard B M and Lines C J.TRANSYT: The latest developments[J]. Traffic engineering and control,1987, 28:59-74
    [4] Lowrie P.The Sydney coordinated adaptive traffic system-principles, methodology, algorithm[C]. Proceedings of the international conference on road traffic signaling. Institution of electrical engineers, London.1982.67-70
    [5] Sims A G and Finlay A B.SCATS. Splits and offsets simplified(S.O.S) [C].ARRB Proceedings Part 4,1984 12:138-152
    [6] Hunt P B,et al.The SCOOT online traffic signal optimization technique[J]. Traffic Engineering & Control 1982,23:190-192
    [7] Martin PT and Hockaday S L M.SCOOT-An update[J].ITE Journal,1995,65(1):44-48
    [8] Hunt P B,et al.SCOOT-A traffic responsive method of coordinating signals[R].TRRL report LR1014, 1981,Crowthorne
    [9] Mirchandani P B and Head K L.A Real-time traffic signal control system:Achitecture algorithm and analysis[J]. Transportation Research Part C,2001,415-432
    [10]顾九春,于泉等.城市交通信号控制系统研究(一)[J].交通科技.206(5):78-81
    [11]王海忠,于泉等.城市交通信号控制系统研究(二)[J].交通科技.207(6):94-96
    [12]吉林大学.大范围战略交通控制技术研发[R].国家“863计划”课题《新一代智能化交通控制系统关键技术研发》分技术报告一,2007
    [13]杨兆升.城市交通流诱导系统[M] .北京:中国铁道出版社,2003
    [14]吉林大学.交通控制相关状态获取技术研发[R].国家“863计划”课题《新一代智能化交通控制系统关键技术研发》分技术报告二,2007
    [15]张周强.浮动车交通数据采集技术研究[D] .同济大学硕士论文,2008
    [16]吴超腾.基于GPS的路段行程时间提取技术研究[D].吉林大学硕士论文,2006
    [17]朱丽云,温慧敏,孙建平.北京市浮动车交通状态信息实时计算系统[J] .城市交通,2008,6(1):77-80
    [18]秦玲,张剑飞,郭鹏.浮动车交通信息处理与应用系统核心功能及实现[J].公路交通科技,2006,7(11):44-46
    [19]韩舒,林航飞,辛飞飞.浮动车采集系统中城市道路分段方法研究[J].交通与计算机,2007,25(5):105-109
    [20]童小华,陈建阳,吴淑琴.基于GIS和GPS的交通状态参数估计仿真分析[J].同济大学学报,2006,34(1):47-52
    [21]朱爱华.基于浮动车数据的路段旅行时间预测研究[D].北京交通大学硕士论文,2007
    [22] Cesar A.Quiroga,Darch Bulllock.Travel time Studies with Global Positioning and Geographic Information Systems:an Integrated Methodology[J].Transportation Research Part C, 1998,6:101-127
    [23]吉林大学.交通状态指标与量化方法研究[R].国家“十五”科技攻关课题《基础交通信息融合技术研究》分技术报告二,2004
    [24]姜桂艳.道路交通状态判别技术与应用[M].北京:人民交通出版社
    [25]姜桂艳,郭海锋,吴超腾.基于感应线圈数据的城市道路交通状态判别方法研究[J].吉林大学学报(工学版),2008,增刊(1):37-42
    [26]冮龙晖.城市道路交通状态判别及拥挤扩散范围估计方法研究[D].吉林大学博士学位论文,2007
    [27]冯金巧.城市道路交通拥挤预测关键技术研究[D].吉林大学博士学位论文,2008.6
    [28] Zhang, H.M.Recursive prediction of traffic conditions with neural network models [J] .ASCE Journal of Transportation Engineering. 2000, 126 (6):472–481
    [29]吉林大学.动态交通信息服务系统开发[R].国家“十五”科技攻关课题《车载信息装置开发》分技术报告四,2004
    [30] Lelitha Devi Vanajakshi.Estimation and Prediction of travel time from loop detector data for intelligent transportations systems applications[D].Doctor dissertation of Texas A&M University,2004
    [31]杨兆升,王媛,管青.基于支持向量机的交通流量预测方法研究[J].吉林大学学报工学版,2006,36(6):881-884
    [32]王媛,杨兆升,王志建等.基于遗传回归分析的无检测器交叉口流量预测[J].北京工业大学学报,2008,34(10):1077-1083
    [33]吉林大学.网络动态路径选择技术研发[R].“863计划”课题《新一代智能化交通控制系统关键技术研发》分技术报告五.2007
    [34] Ahas, R., and U lar, M. Location based services-new challenges for planning and public administration [J].Futures, 2005, 37 (6): 547-561
    [35] Rose, G. .Mobile phones as traffic probes[D]. PHD dissertation of Monash University, Australia, 2004
    [36] Ratti, C., Pulselli, R.M., Williams, S.et.al. Mobile landscapes: using location data fromcell-phones for urban analysis[J]. Environ. Plan. B - Plan. Des., 33 (5) :727-748
    [37] http://news.ccidnet.com/art/1032/20080924/1577569_1.html
    [38] Smith,B.,et al.Cellphone Probes as an ATMS Tool,Center of Transporation Studies[R].University of Virginia, Smart Travel Lab Report No.STL-2003-01
    [39] http://www.cts.umn.edu/research/proectdetail.pl?id=2007022
    [40] University of Virginia Center for Transportation Studies.Probe-based Traffic Monitoring[R]. NCHRP Project
    [41] A. Hillson and M. De Santis.Traffic Monitoring Application of Cellular Positioning Technology:Proof of Concept[R]. Cell-Loc Inc.Report, TP 13936E, 2002
    [42] Fontaine,M.,Smith,B.Improving the Effectiveness of Traffic Monitoring Based on Wireless Location Technology[R].Virginia Transportation Research Council, VTRC05-R17, http://www.viginiadot.org/vtrc/main/online_reports/pdf/05-r17.pdf
    [43] Yim,Y. .The State of Cellular Phobes[R].California PATH Report UCB- ITS- PRR- 2003-25, 2003
    [44] Liu,H. .Evaluation of Cell Phone Data Traffic[R].CTS Project Number 2007022, active project,2006
    [45] Subbarao V.Wunnava,Kang Yen et.al.Travel Time Estimation Using Cell Phones (TTECP)for Highways and Roadways[R].Final Report Prepared for the Florida Department of Transportaion.Contract BD015-12
    [46] http://www.baltometro.org/content/view/655/423/
    [47]杨飞.无线定位技术的发展及其在交通数据采集中的应用[C].第一届中国智能交通年会,2005,12:411-417
    [48]杨飞,惠英.基于手机切换变化模式的道路匹配方法[J].系统工程,2007,25(11):6-13
    [49]杨飞.基于手机切换定位的道路行程时间车速采集技术关键问题研究[D].同济大学博士论文,2007
    [50]胡明伟.无线定位技术应用于实时交通信息采集研究[J].深圳大学学报(理工版),2007,24(3):246-251
    [51]胡坚明,宋靖雁,李伟.基于无线定位技术的交通信息获取方法研究[J].公路交通科技,2007,24(10):113-117
    [52]王斌.基于多手机定位的公交车辆定位研究[D].重庆大学硕士论文,2006
    [53]陈伟霞.基于手机定位的高速公路自动事件检测(AIDS)方法研究[D].重庆大学硕士论文,2005
    [54]马丽.基于手机定位及聚类的高速公路实时交通参数估计研究[D].重庆大学硕士论文,2005
    [55]杨兆升,王媛.基于手机探测车的交通信息采集方法研究[C].中国第一届ITS年会,2005
    [56] http://info.secu.hc360.com/2008/12/170824147194.shtml
    [57] Brian L.Smith,Michael D.Fontaine,et al.Private-Sector Provision of Congestion Data[R]. NCHRP 70-01 final report, 2007
    [58]杨飞,裘炜毅.基于手机定位的实时交通数据采集技术[J].城市交通,2005,3(4):63-68
    [59] Hiroyuki Takada.Road traffic condition acquisition via mobile phone location referenceing[D].Dissertion of university of Waterloo. 2006
    [60] Quiroga,C.A. An integrated GPS-GIS methodology for performing travel time studies[D].Louisiana USA, Louisiana State University,1997
    [61]徐建闽,邹亮.浮动车与感应线圈检测技术融合模型[C].Proceedings of the 24th Chinese Control Conference, 2005,15-18
    [62]李存军,杨儒贵,靳蕃.基于神经网络的交通信息融合预测方法[J].系统工程,2004,22(3):80-83
    [63]杨兆升,王爽,马道松.基础交通信息融合方法综述[J].公路交通科技,2006,23(3):111-116
    [64]张存保,严新平.固定检测器和移动检测器的交通信息融合方法[J].交通与计算机,2007,25(3):14-17
    [65]曹晶,李清泉.城市网络中浮动车数据和线圈数据的融合[J].交通与计算机,2008,26(4):11-14
    [66] Seri Park.Vehicle monitoring for traffic surveillance and performance using multi-sensor data fusion[D].Doctor dissertation of University of California, Irvine, 2004
    [67] Cremer,M.,and Schriber,S. Monitoring traffic load profiles with heterogeneous data source configurations[C].Proceedings of 13th Internation Symposium on Transportation and Traffic Theory,1996
    [68] Kummerer,B.,and Kuhne,R. New filtering methods for data fusion and short term forecasting for urban traffic[C].Proceeding of the 2001 IEEE intelligent Transportation systems conference,2001
    [69] Okada,T.,Tsujimichi,S.,and Kosuge,Y. Multi sensor vehicle tracking method for intelligent highway systems[C].Proceedings of 39th SICE annual conference, 2000, Iizuka, Japan
    [70] Choi,K.,andChang,Y.S. Travel Time estimation algorithm using GPS probe and loop detector data fusion[R].Presented at the 80th annual meeting of transportation research board,2001
    [71] Tarko,A.,and Rouphail,N,M. Travel time data fusion in ADVANCE[R]. ADVANCE working report, 1993
    [72] Goebel,K.F and Agogino A.M. Sensor validation and fusion for automated vehicle control using fuzzy techniques[J].Journal of dynamic systems,Measurement and control 2001,123:145-146
    [73] Thomas,N.E. Multi State and Mutli Sensor Incident Detection System for Arterial Streets[J].Transpoatation Research Part C,1998,6:337-357
    [74] Franke,E.,Kinkler,S,and Magee,M. A sensor fusion based approach for feature extraction in intelligent transportation systems applications[C].Applications of digital image processing XXII,proceedings of SPIE,1999, 38(8):789-885
    [75] Ivan,J.N.Neural network representations for arterial street incident detection data fusion[J].Transportation research part C, 1997,5:245-254
    [76] Marcel Westerman,Remco Litjens,Jean-Paul Linnartz.Integration of probe vehicle and induction loop data-estimation of travel times and automatic incident detection[R].California PATH research report,UCB-ITS-PRR-96-13
    [77] Wang Y,Nihan N.Freeway traffic speed estimation using single loop outputs[C].The 79th annual meeting of the transportation research board, Washington,DC,2000
    [78] Pushkar A,Hall F,Acha-DazaJ.Estimation of speeds from single-loop freeway flow and occupancy data using cusp catastrophe theory model[C].Transportation Research Record,1994, 1457:149-157
    [79] Dailey D J.A statistical algorithm for estimating speed from single loop volume and occupancy measurements[J].Transportation Research Part B, 1999, 33(5): 313-322
    [80] Sun C,S G Ritchie.Individual vehicle speed estimation using single inductive waveforms[J].ASCE Journal of Transportation Engineering, 1999, 125(6): 531-538
    [81]宫兴斌,姚丹亚.一种基于线圈速度估计的简单模型方法[J].计算机工程与应用.2004(35):226-228
    [82] Abrahamsson, T.Estimation of origin–destination matrices using traffic counts- a literature survey[R]. Interim Report IR-98-021, International Institute for Applied Systems Analysis, Laxenburg, Austria,1998
    [83] Cascetta and Postorino. Fixed point approaches to the estimation of O/D matrices using traffic counts on congested networks[J].Transportation Science, 2001, 35 (2):134-147
    [84] LO H P,CHAN C P. Simultaneous Estimation of an Origin-destination Matrix and Link Choice Proportions Using Traffic Counts[J].Transportation Research Part A, 2003,37(1):771-788
    [85] Sherali H. D., Narayanan, A., and Sivanandan, R. . Estimation of Origin-Destination Trip-Tables based on a partial set of traffic link volumes[J]. Transportation Research-B, 2003, 37:815-836
    [86] I. Okutani.The Kalman filtering approaches in some transportation and traffic problems[J]. Transportation and Traffic Theory, 1987,36: 397-416
    [87] K. Ashok.Estimation and prediction of time-dependant origin–destination flows. Ph.D. dissertation, Mass. Inst. Technol., Cambridge,MA, 1996
    [88] M. Cremer and H. Keller.A new class of dynamic methods for the identification of origin destination flows[J]. Transp. Res., Part B, 1987, 21(2): 117-132
    [89] N. L. Nihan and G. A. Davis.Recursive estimation of origin–destination matrices from input/output counts[J]. Transp. Res., Part B, 1987, 21(2):149-163
    [90] M. G. H. Bell.The real time estimation of origin–destination flows in presence of platoon dispersion[J].Transp. Res., Part B, 1991, 25(2/3):115-125
    [91] G. L. Chang and J. Wu.Estimation of time-varying origin–destination distributions with dynamic screenline flows[J]. Transp. Res., Part B, 1996,30(4): 277-290
    [92] N. van der Zijpp.Dynamic OD-matrix estimation from traffic counts and automated vehicle identification data. Transp. Res. Rec.: J. Transp. Res.Board, 1607:87-94, 1992
    [93] M. P. Dixon and L. R. Rilett.Real-time OD estimation using automatic vehicle identification and traffic count data[J]. Comput.-Aided Civil Infrastruct.Eng., 2002, 17(1):7-21
    [94] Cremer, M., Keller, H. A systems dynamics approach to the estimation of entry and exit O-D flows[J].Proc. 9th International Symposium on Transportation and Traffic Theory. Delft University of Technology, 1984
    [95] Van Zuylen H.J. and Willumsen L.G. .The Most Likely Trip Matrix Estimated from Traffic Counts[J]. Transportation Research B, 1980,14:281-293
    [96] Bell M.G.H. .The Real Time Estimation of Origin-Destination Flows in the Presence of Platoon Dispersion[J]. Transportation Research B, 1991,36(4):115-125
    [97] Van Der Zijpp, N. J. and De Romph, E. A dynamic traffic forecasting application on the Amsterdam beltway[J]. International Journal of Forecasting,1997, 13:87-103
    [98] Sherali H D,Park T. Estimation of dynamic origin-destination trip tables for a generalnetwork[J] . Transportation Research Part B,2001,35(3): 217-235
    [99] Tsekeris, T., Stathopoulos, A. Demand-oriented approach to estimate the operational performance of urban networks with and without traffic information provision[J]. EJTIR, 2005,5(2):81-96
    [100] Zhou, X., Mahmassani, H.S. A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework[J].Transportation Research B, 2007, 41(8):823-840
    [101] Van Zuylen, H.J., Van Lint, et al. ITS and dynamic traffic management. lecture notes, code CT5804, Delft University of Technology, 2006
    [102] Cascetta, E., Inaudi, D., Marquis, G. Dynamic Estimators of Origin-Destination matrices using traffic counts[J]. Transportation Science,1993, 27: 363-373
    [103] Ashok, K., Ben-Akiva, M.E. Dynamic origin-destination matrix estimation for real-time traffic management systems[C]. Proc. 12th Int. Symposium on Transportation and Traffic Theory, Berkeley, California,1993
    [104] Willumsen, L.G. Estimating time-dependent trip matrices from traffic counts[C]. Proc. 9th International Symposium on Transportation and Traffic Theory, Delft University of Technology, Delft The Netherlands,1984
    [105] Hamerslag, R., Taale, H. Deriving road networks from floating car data[C]. 9th world conference on transport research, Seoul, Korea, 2001
    [106] Nihan, N.L., Davis, G.A. Application of prediction-error minimization and maximum likelihood to estimate intersection O-D matrices from traffic counts[J].Transportation Science, 1989, 23(2):189-197
    [107] Wu, J.A real-time origin-destination matrix updating algorithm for on-line applications[J]. Transportation Research B, 1997, 31(5): 381-396
    [108] Lin, P.-W., Chang, G.-L. A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix[J]. Transportation Research B, 2007, 41(5): 554-572
    [109] Zhou, X., Qin, X., Mahmassani, H.S. Dynamic origin-destination demand estimation using multi-day link traffic counts for planning applications[J].Transportation Research Record, 2003, 1831:30-38
    [110] Spiess, H. A maximum likelihood model for estimating origin destination matrices[J]. Transportation Research B. 1987, 21(5):395-412
    [111] CanadaChen, Y.S. REMDOE– a dynamic OD estimator[M]. Software manual,1993-2007
    [112] Van Zuylen, H.J.Some improvements in the estimation of an OD matrix from traffic counts[C]. Proc. 8th Int. Symposium on Transportation and Traffic Theory, University of Toronto Press, 1981,Toronto Canada
    [113] Van Aerde, M., Hellinga, B., Yu, L. and Rakha, H. Vehicle Probes as Real-Time ATMS Sources of Dynamic O-D and Travel Time Data[C]. Large Urban Systems. Proceedings of the ATMS Conference, St. Petersburg, FL, 1993:207-230
    [114] Cascetta, E.Estimation of trip matrices from traffic counts and survey data: a generalized least squares estimator[J]. Transportation Research B, 1984,18(4-5): 289-299
    [115] Maher, M.J. Inferences on trip matrices from observations on link volumes: a Bayesian statistical approach[J] . Transportation Research B, 1983, 17(6):435-447
    [116] N. van der Zijpp. Dynamic OD-matrix estimation from traffic counts and automated vehicle identification data[J]. Transporation Research Record, 1992,1607:87–94
    [117] Watling, D.P. and Maher, M.J. A Statistical Procedure for Estimating a Mean Origin-Destination Matrix from a Partial Registration Plate Survey[J]. Transportation Research B. 1992,26:289-314
    [118] Rúunaásmundsdóttir. Dynamic OD matrix estimation using floating car data [D] .Master thesis of Civil Engineering Delft University of Technology, 2008
    [119] N.Caceres,J.P.Wideberg and F.G.Benitez.Deriving origin destination data from a mobile phone network[J].Intelligent Transport Systems, IET 2007 (1):15-26
    [120]郝光.动态OD矩阵推算模型及算法研究[D] .西南交通大学博士论文,2007.6
    [121]林勇,蔡远利,黄永宣.基于广义最小二乘模型的动态交通OD矩阵估计[J].系统工程理论与实践,2004(1):136-140
    [122]董敬欣,吴建平.使用浮动车检测OD矩阵的算法及可靠性分析[J].北京交通大学学报,2005,29(3):73-76
    [123]张存保.基于浮动车的交通信息采集与处理理论及方法研究[D].同济大学博士论文,2006
    [124]邓力.动态OD矩阵估计方法研究[D].北京交通大学硕士学位论文,2007
    [125]杨飞.基于手机定位的交通OD数据获取技术[J].系统工程,2007,25(1):42-48
    [126]张存保,杨晓光,严新平.基于浮动车的交通信息采集系统研究[J].交通与计算机,2006(5):31-34
    [127] Keemin Sohn and Daehyun Kim.Dynamic Origin–Destination Flow Estimation Using Cellular Communication System[J].IEEE Transactions on vehicular technology,2008,57(5):2703-2713
    [128] Asakura Y, Hato E. Tracking survey for individual travel behaviour using mobile communication instruments[J].Transportation Research Part(C). 2004, 12: 273-291
    [129] Masao Kuwahara,Edward Chung,Tomotaka Ishida.Fundamental Study on the Issues of using Probe Data for OD Estimation and Route Identification[A]. Proceedings of 11th world congress on ITS,Nagoya, 2004
    [130]张志良.基于移动电话定位的公交车辆定位研究[D].重庆大学硕士论文,2005
    [131]黄俊,胡雄刚.OD调查数据的扩样方法[J].交通科技,2003(6):56-59
    [132]何延龄,李旭宏等人.中小城市出入口机动车OD调查及其扩样方法研究[J].交通科技,2002(6):66-68
    [133]王媛,杨兆升,王志建等..基于遗传回归分析的无检测器交叉口流量预测[J].北京工业大学学报,34(10):1077-1083
    [134]翟忠民.道路交通组织优化[M].人民交通出版社,北京,2000
    [135]郭海锋.局部拥挤条件下城市道路交通信号控制方法研究[D].吉林大学博士论文,2008
    [136]王薇.基于网络平衡的大范围交通协调控制系统理论及技术研究[D].吉林大学博士论文,2008
    [137]李凤.过饱和状态下交叉口车辆延误和排队长度模型研究[D].吉林大学硕士论文,2006
    [138] Gartner,N.H.,Stamatiadis,C.,Tarnoff,P.J.Development of advanced traffic signal control strategies for intelligent transportation systems:multilevel design[J]. Transportation Research Record ,1995(1494):98-105
    [139] Ghassan Abu-Lebdeh,Rahim F.Benekohal.Design and evaluation of dynamic traffic management strategies for congested conditions[J].Transportation Research Part A,37(2003):109-127
    [140] Michael G.H.Bell. Future Direction in Traffic Signal Control[J].Transportation Research A, 1992, 26(4): 303-312
    [141] Hong K.Lo. A Novel Traffic Signal Control Formulation[J]. Transportation Research A, 1999, 33(4):433-448
    [142] E.Kwon,et,al. Development of an Adaptive Control Strategy in a Live Intersection Laboratory[R]. Transportation Research Record Paper No.98-0991
    [143] Hai Yang and Sam Yagar. Traffic Assignment and Signal Control in Saturated Road Networks[J]. Transportation Research A, 1997,29(2):125-139
    [144] Diakaki, C.M. Papageorgiou, T. McLean.Application and evaluation of the integrated traffic responsive urban corridor control strategy IN-TUC in Glasgow[R].Transportation Research Record 2000(1727):101-111
    [145]夏百战,夏浩军.基于模式识别的自适应区域控制系统研究[J].中南公路工程,2006(4):154-157
    [146] Ghassan Abu-Lebdeh,Rahim F Benekohal.Design and evaluation of dynamic traffic management strategies for congested conditions[J].Transportation Research-A, 2003,37(2):109-127
    [147]赵晖,荣莉莉,李晓.一种设计层次支持向量机多类分类器的新方法[J].计算机应用研究,2006(6):34-37
    [148]刘志刚,李德仁,秦前清等.支持向量机在多类分类问题中的推广[J].计算机工程与应用,2004(7):10-14
    [149]马笑潇,黄席樾,柴毅.基于SVM的二叉树多类分类算法及其在故障诊断中的应用[J].控制与决策,2003,18(3):272-276
    [150]殷天石,孙济庆,基于树型结构的SVM多类组合分类器在文本分类中的应用[J] .情报技术,2006(2):34-36
    [151]保丽霞.基于信息集成的城市交通流诱导与交通控制协同的关键理论及技术研究[D] .吉林大学博士论文,2006
    [152]高自友,宋一凡,四兵锋.城市交通连续平衡网络设计理论与方法[M] .中国铁道出版社,北京,2000
    [153]翟忠民,景东升,陆化普.道路交通实战案例[M].人民交通出版社,北京,2007

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