基于手机切换定位技术的交通信息提取方法研究
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摘要
手机探测车是一种全新的、具有巨大应用前景的交通信息采集技术。目前,手机探测车的定位技术集中在基于终端的全球定位系统(Global Position System,简称GPS)和手机切换定位两种技术手段上。手机切换定位技术通过解析车载手机沿道路行驶过程中无线通信网络发生的切换来确定探测车的位置,具有充分利用现有无线通信系统基础设施无需对手机终端进行改造的优势,所以基于手机切换定位技术的交通信息提取研究已经成为国内外智能交通领域的研究热点之一然而,目前关于手机切换定位技术的交通信息提取研究还不成熟,尚未形成系统的、全面的把握符合手机切换定位技术特点的理论研究方法。因此,本文针对手机切换定位技术的特点,研究提出了基于手机切换定位技术的地图匹配方法、交通参数估计方法、交通状态辨识方法以及最小样本量估计方法,为实时、准确、覆盖范围广泛的城市交通信息采集提供理论支撑。具体来讲,本文研究工作主要包括以下几个方面：
(1)针对基于手机切换定位技术的地图匹配问题需要预先建立无线通信网络切换与城市路网道路二者对应关系的问题,通过分析手机探测车切换位置数据的特点,提出了一种切换位置数据库的建立方法,并分别结合D-S证据推理理论和贝叶斯决策理论设计了两种基于手机切换定位技术的地图匹配算法。以北京市部分近郊快速路路段为对象进行了实证分析,验证了本文提出的方法实现手机探测车切换位置数据地图匹配的可行性与有效性。
(2)对基于手机切换定位技术的切换路段和几何路段交通参数估计问题进行了深入研究,提出了一种降低手机切换位置数据定位误差的预处理方法,考虑到匹配到路上的手机切换位置数据非均匀稀疏分布的特点,将几何路段交通参数估计问题转化为包含几何路段起讫节点的切换路段旅行时问分配问题,以轨迹分析方法为基础结合车辆运行特征分析建立了切换路段旅行时间分配模型及算法。以北京市部分近郊快速路路段为对象进行了实证分析,验证了本文提出的方法适合于手机切换位置数据定位精度低、非均匀稀疏分布情形下切换路段和几何路段准确交通参数的估计。
(3)通过分析手机切换位置数据估计的交通参数特点对交通状态辨识的影响,指出了对交通参数进行鲁棒集聚的必要性,基于稳健估计理论与方法提出了一种自适应鲁棒的交通参数集聚方法和一种基于三元区间数形式的交通参数描述方法。在此基础上,首先以包含度概念为基础给出三元区间数关联度计算模型,并依据最大关联度准则提出了一种基于速度单属性的交通状态辨识方法；然后,根据Van-Aerde交通流模型计算车流密度,考虑到车流密度的可靠性受到交通流模型的影响,以速度、车流密度为证据提出了一种基于证据修正策略的交通状态辨识方法。以北京市部分近郊快速路路段为例进行了仿真验证,从准确性和可靠性两方面对不同交通状态条件下仿真实验结果进行了评估,验证了本文提出方法的可行性和有效性。
(4)结合手机切换位置数据特点对最小样本量估计的影响分析,提出了一种适用于手机切换定位技术的探测车采样策略,结合北京市部分近郊快速路路段实测数据和仿真实验,确定了满足交通参数估计精度要求的路段最小样本量,结果表明本文提出的探测车采样策略所需最小样本量小于简单随机采样策略确定的最小样本量。
Mobile probe has been a new and promising traffic information collection technology. The most potential location technologies for mobile probes currently have been handset-based GPS location technology and mobile handover location technology. Mobile handover location technology is to determine the locations of mobile probes through tracking handovers of on-board mobile phones moving along the road from wireless communication network, which have the advantages of making extensive use of current cellular network infrastructures and requiring no extra changes in the mobile terminals, therefore the researches of traffic information extraction based on mobile handover location technology have become one of the popular topics in the intelligent transportation research at home and abroad. However, the researches of traffic information extraction based on mobile handover location technology have still been premature, and it is lack of systematic and comprehensive theoretical research methods in keeping with the characteristics of mobile handover location technology. Therefore, according to the characteristics of mobile handover location technology, the map matching method, traffic data estimation method, traffic states recognition method and lowest sample size determination method based on mobile handover location technology have been studied and presented. The main contents of the dissertation are as follows:
(1) Because the issue of map matching based on mobile handover location technology needs develop a relationship between handovers in the wireless communication network and road network in advance, a handover location database development method was presented based on analyzing the characteristics of handover data of mobile probes, and two map matching algorithms separately based on D-S evidence theory and Bayes decision theory were developed. The results of empirical studies of Beijing inner-suburban freeway sections demonstrate that it is feasible and efficient to geo-locate handover data of mobile probes on the GIS map by the proposed method.
(2) Traffic data estimation of handover link and geographic link based on mobile handover location technology were detailed studied in the dissertation. A mobile handover location pre-processing method for reducing the location errors was proposed. Due to the nonuniform and sparse distribution of handover location data after map matching, the issue of traffic data estimation of geographic link was converted to the issue of handover link travel time allocation that includes nodes of the geographic link, which is modeled and solved based on the trajectory analyzing methodology and vehicle moving characteristic analysis. The results of empirical studies of Beijing inner-suburban freeway sections demonstrate that it is feasible to estimate correct traffic data of handover link and geographic link under the condition of mobile handover location data with low location accuracy and nonuniform and sparse distribution by the proposed method.
(3) Through analyzing the influence of the features of traffic data estimated from mobile handover location data on traffic states recognition, it is necessary to study the issue of traffic data aggregation. Based on robust estimation theory and methodology, an adaptive robust traffic data aggregation method and a traffic data representation method based on interval numbers with three elements were proposed. And then, a computation model of location association degree of interval numbers with three elements based on the concepts of inclusion degree was firstly proposed, and a traffic states recognition method using speed as a unique attribute was presented according to the rule of maximum association degree; afterwards, density was calculated by Van-Aerde speed-density-flow model, and a traffic states recognition method based on the evidence correction strategy using speed and density as multiple attributes was presented. The evaluation results of simulation of Beijing inner-suburban freeway sections on accuracy and reliability demonstrate that the proposed method is feasible and efficient.
(4) Based on analyzing the influence of the characteristics of mobile handover location data on the minimum sample size determination, an appropriate probe sampling strategy for mobile handover location technology was presented. Based on the field test data of Beijing inner-suburban freeway sections and simulation test analysis, the link minimum sample size of mobile probes was determined, which was no more than that determined by the simple random sampling strategy.
(1)针对基于手机切换定位技术的地图匹配问题需要预先建立无线通信网络切换与城市路网道路二者对应关系的问题,通过分析手机探测车切换位置数据的特点,提出了一种切换位置数据库的建立方法,并分别结合D-S证据推理理论和贝叶斯决策理论设计了两种基于手机切换定位技术的地图匹配算法。以北京市部分近郊快速路路段为对象进行了实证分析,验证了本文提出的方法实现手机探测车切换位置数据地图匹配的可行性与有效性。
(2)对基于手机切换定位技术的切换路段和几何路段交通参数估计问题进行了深入研究,提出了一种降低手机切换位置数据定位误差的预处理方法,考虑到匹配到路上的手机切换位置数据非均匀稀疏分布的特点,将几何路段交通参数估计问题转化为包含几何路段起讫节点的切换路段旅行时问分配问题,以轨迹分析方法为基础结合车辆运行特征分析建立了切换路段旅行时间分配模型及算法。以北京市部分近郊快速路路段为对象进行了实证分析,验证了本文提出的方法适合于手机切换位置数据定位精度低、非均匀稀疏分布情形下切换路段和几何路段准确交通参数的估计。
(3)通过分析手机切换位置数据估计的交通参数特点对交通状态辨识的影响,指出了对交通参数进行鲁棒集聚的必要性,基于稳健估计理论与方法提出了一种自适应鲁棒的交通参数集聚方法和一种基于三元区间数形式的交通参数描述方法。在此基础上,首先以包含度概念为基础给出三元区间数关联度计算模型,并依据最大关联度准则提出了一种基于速度单属性的交通状态辨识方法；然后,根据Van-Aerde交通流模型计算车流密度,考虑到车流密度的可靠性受到交通流模型的影响,以速度、车流密度为证据提出了一种基于证据修正策略的交通状态辨识方法。以北京市部分近郊快速路路段为例进行了仿真验证,从准确性和可靠性两方面对不同交通状态条件下仿真实验结果进行了评估,验证了本文提出方法的可行性和有效性。
(4)结合手机切换位置数据特点对最小样本量估计的影响分析,提出了一种适用于手机切换定位技术的探测车采样策略,结合北京市部分近郊快速路路段实测数据和仿真实验,确定了满足交通参数估计精度要求的路段最小样本量,结果表明本文提出的探测车采样策略所需最小样本量小于简单随机采样策略确定的最小样本量。
Mobile probe has been a new and promising traffic information collection technology. The most potential location technologies for mobile probes currently have been handset-based GPS location technology and mobile handover location technology. Mobile handover location technology is to determine the locations of mobile probes through tracking handovers of on-board mobile phones moving along the road from wireless communication network, which have the advantages of making extensive use of current cellular network infrastructures and requiring no extra changes in the mobile terminals, therefore the researches of traffic information extraction based on mobile handover location technology have become one of the popular topics in the intelligent transportation research at home and abroad. However, the researches of traffic information extraction based on mobile handover location technology have still been premature, and it is lack of systematic and comprehensive theoretical research methods in keeping with the characteristics of mobile handover location technology. Therefore, according to the characteristics of mobile handover location technology, the map matching method, traffic data estimation method, traffic states recognition method and lowest sample size determination method based on mobile handover location technology have been studied and presented. The main contents of the dissertation are as follows:
(1) Because the issue of map matching based on mobile handover location technology needs develop a relationship between handovers in the wireless communication network and road network in advance, a handover location database development method was presented based on analyzing the characteristics of handover data of mobile probes, and two map matching algorithms separately based on D-S evidence theory and Bayes decision theory were developed. The results of empirical studies of Beijing inner-suburban freeway sections demonstrate that it is feasible and efficient to geo-locate handover data of mobile probes on the GIS map by the proposed method.
(2) Traffic data estimation of handover link and geographic link based on mobile handover location technology were detailed studied in the dissertation. A mobile handover location pre-processing method for reducing the location errors was proposed. Due to the nonuniform and sparse distribution of handover location data after map matching, the issue of traffic data estimation of geographic link was converted to the issue of handover link travel time allocation that includes nodes of the geographic link, which is modeled and solved based on the trajectory analyzing methodology and vehicle moving characteristic analysis. The results of empirical studies of Beijing inner-suburban freeway sections demonstrate that it is feasible to estimate correct traffic data of handover link and geographic link under the condition of mobile handover location data with low location accuracy and nonuniform and sparse distribution by the proposed method.
(3) Through analyzing the influence of the features of traffic data estimated from mobile handover location data on traffic states recognition, it is necessary to study the issue of traffic data aggregation. Based on robust estimation theory and methodology, an adaptive robust traffic data aggregation method and a traffic data representation method based on interval numbers with three elements were proposed. And then, a computation model of location association degree of interval numbers with three elements based on the concepts of inclusion degree was firstly proposed, and a traffic states recognition method using speed as a unique attribute was presented according to the rule of maximum association degree; afterwards, density was calculated by Van-Aerde speed-density-flow model, and a traffic states recognition method based on the evidence correction strategy using speed and density as multiple attributes was presented. The evaluation results of simulation of Beijing inner-suburban freeway sections on accuracy and reliability demonstrate that the proposed method is feasible and efficient.
(4) Based on analyzing the influence of the characteristics of mobile handover location data on the minimum sample size determination, an appropriate probe sampling strategy for mobile handover location technology was presented. Based on the field test data of Beijing inner-suburban freeway sections and simulation test analysis, the link minimum sample size of mobile probes was determined, which was no more than that determined by the simple random sampling strategy.
引文
[1]杨兆升.基础交通信息融合技术及其应用[M].北京：中国铁道出版社,2005.
[2]殷伟,郭磷,方廷健,等.一种基于FCD的城市道路车流速度估计算法[J].中国科学技术大学学报,2008,38(9)：1113-1117.
[3]Hellinga B, Fu L, Takada H. Obtaining traveler information via mobile phone location referencing challenges and opportunities[C]. Proceedings of Annual Conference of the Transportation Association of Canada, St. John's, Newfoundland, Canada,2003,1-10.
[4]White J, Quick J, Philippou P. The use of mobile phone location data for traffic information[C]. Proceedings of Twelfth International Conference on Road Transport Information and Control, London, United Kingdom,2004,321-325.
[5]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(1)：63-68.
[6]Rose G. Mobile phones as traffic probes:practices, prospects and issues[J]. Transport Reviews, 2006,26(3):275-291.
[7]Qiu Z, Jing J, Peng C, et al. State of the art and practice:cellular probe technology applied in advanced traveler information system[C]. Proceedings of Transportation Research Board 86th Annual Meeting, Washington DC, USA,2007,1-19.
[8]Caceres N, Wideberg J, Benitez F. Review of traffic data estimations extracted from cellular network[J]. IET Intelligent Transportation System,2008,2(3):179-192.
[9]Steenbruggen J, Borzacchiello M T, Nijkamp P,et al. Real-time data from mobile phone networks for urban incidence and traffic management-A review of applications and opportunities [OL]. Faculty of Economics and Business Administration, University of Amsterdam,March,2010.http://dare.ubvu.vu.nl/handle/1871/15715.
[10]Valerio D. Road traffic information from cellular network signaling[OL]. Forschungszentrum Telekommunikation Wien, March,2009. http://userver.ftw.at/-valerio/files/ROADCELLreport.pdf.
[11]王贤君.现代无线通信系统与技术[M].南京：东南大学出版社,2009.
[12]杨家玮,盛敏,刘勤.移动通信基础[M].北京：电子工业出版社,2005.
[13]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.
[14]Zhao Y. Mobile phone location determination and its impact on intelligent transportation systems[J]. IEEE Transactions on Intelligent Transportation Systems,2000,1(1):55-64.
[15]Sun G, Chen J, Guo W, et al. Signal Processing Techniques in Network-aided Positioning A survey of state-of-the-art positioning designs[J]. IEEE Signal Processing Magazine,2005, 22(4):12-23.
[16]兰云飞.蜂窝网无线定位算法研究[D].郑州：郑州大学,2007.
[17]徐政五.基于GSM系统的定位技术研究[D].成都：电子科技大学,2006.
[18]王斌.基于多手机定位的公交车辆定位研究[D].重庆：重庆大学,2006.
[19]Wunnava S V, Yen K, Babij T, et al. Travel time estimation using cell phones (TTECP) for highways and roadways [OL]. Florida International University, April 12,2007. http://www.dot.state.fl.us/research-center/Completed_Proj/Summary_TE/FDOT_BD015_L2_rpt .pdf.
[20]Smith B L, Fontaine M D, Beaton J, et al. Private-Sector Provision of Congestion Data[OL]. University of Virginia, February 27,2007. http://cts.virginia.edu/docs/UVACTS-13-0-118.pdf.
[21]Cheng P, Qiu Z, Ran B. Particle filter based traffic state estimation using cell phone network data[C]. Proceedings of 2006 IEEE Intelligent Transportation Systems Conference,2006, 1047-1052.
[22]Cheng P, Qiu Z, Ran B. Traffic estimation based on particle filtering with stochastic state reconstruction using mobile network data[C]. Proceedings of Transportation Research Board 86th Annual Meeting,2007,1-15.
[23]Sankar R, Civil L. Intelligent traffic monitoring system using wireless cellular communications[C]. Proceedings of Proceedings of the 1997 IEEE SOUTHEASTCON, Blacksburg, USA,1997,210-214.
[24]Sankar R, Civil L. Traffic monitoring and congestion prediction using handovers in wireless cellular communications[C]. Proceedings of Proceedings of the 1997 47th IEEE Vehicular Technology Conference, Phoenix, USA,1997,520-524.
[25]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.
[26]杨飞,惠英,杨东援.基于手机切换定位的交通路网标定方法[J].同济大学学报(自然科学版),2009,37(1)：67-72.
[27]Ramm K, Czommer R, Schwieger V. Map-based Positioning using Mobile Phones[C]. Proceedings of The XXIII FIG Congress,2006,1-16.
[28]Wiltschko T, Schwieger V, Mohlenbrink W. Acquisition of traffic state information by mobile phone positioning[C]. Proceedings of The 6th European Congress on ITS,2007,1-8.
[29]Friedrich M, Jehlicka P, Otterstatter T, et al. Mobile phone data for telematic applications[C]. Proceedings of International Multi-Conference on Engineering and Technological Innovation, 2008,1-6.
[30]Schlaich J, Otterstatter T, Friedrich M. Generating Trajectories from Mobile Phone Data[C]. Proceedings of Transportation Research Board 89th Annual Meeting,2010,1-17.
[31]Fontaine M D, Smith B L. Improving the electiveness of traffic monitoring based on wireless location technology[OL]. Virginia Transportation Research Council, December,2004. http://www.virginiadot.org/vtrc/main/online reports/pdf/05-r17.pdf.
[32]Fontaine M D, Smith B L. Investigation of the performance of wireless location technology-based traffic monitoring systems[J]. Journal of Transportation Engineering,2007, 133(3):157-164.
[33]Kummala J. Travel time service utilising mobile phones[OL] Finnish Road Administration, May, 2002.http://alk.tiehallinto.fi/julkaisut/pdf/3200787-vm-aikapalvelu tulokset.pdf.
[34]Virtanen J. Mobile phones as probes in travel time monitoring[OL]. Finnish Road Administration,2002, http://www.durbit.se/Archives/ExternalPDF/MIP2002/Domain1/Deliverables/VIKINGY200213 I FI GSM travel time monitoring.pdf.
[35]Birle C, Wermuth M. The traffic online project[C]. Proceedings of 13th ITS World Congress, 2006,1-16.
[36]Bar-Gera H. Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times:A case study from Israel[J]. Transportation Research Part C,2007,15(6):380-391.
[37]Fontaine M D. Improving the effectiveness of wireless location technology-based traffic monitoring:an exploration of system design issues[D]. USA:School of Engineering and Applied Science, University of Virginia, Jan,2005.
[38]Gundlegard D, Karlsson J. Handover location accuracy for travel time estimation in GSM and UMTS[J]. IET Intelligent Transport Systems,2009,3(1):87-94.
[39]Qiu Z. Macroscopic traffic state estimation for large scale freeway network using wireless network data[D]. USA:University of Wisconsin-Madison, May,2007.
[40]Wilson E. Real-time traffic monitoring using mobile phone data[OL]. Vodafone Pilotentwicklung GmbH, May,2005. http://www.maths-in-industry.org/miis/30/1/TrafficMonitoring.pdf.
[41]Birle C. Traffic Online via GSM network data to traffic information in real time[OL]. Vodafone Group R&D, April,2008. http://www.its-munich.de/pdf/DynInfoBY/4-Birle.pdf.
[42]Maerivoet S, Logghe S. Validation of travel times based on cellular floating vehicle data[C]. Proceedings of 6th European Congress and Exhibition on Intelligent Transport Systems and Services,2007,1-8.
[43]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.
[44]Hellinga B, Izadpanah P, Takada H, et al. Decomposing travel times measured by probe-based traffic monitoring systems to individual road segments[J]. Transportation Research Part C,2008, 16(6):768-782.
[45]Takada H. Road traffic condition acquisition via mobile phone location referencing[D].Canada: University of Waterloo,2006.
[46]胡小文,杨东援.交通探测车的样本量模型[J].公路交通科技,2009,26(10)：111-116.
[47]Chen M, Chien S. Determining the number of probe vehicles for freeway travel time estimation using microscopic simulation[C]. Proceedings of Transportation Research Board 79th Annual Meeting,2000,1-25.
[48]Yim Y. The state of cellular probes[OL]. Institute of Transportation Studies, University of California, July,2003. http://www.path.berkeley.edu/PATH/publications/pdf/prr/2003/prr-2003-25.pdf.
[49]Smith B L, Zhang H, Fontaine M, et al. Cell phone probes as an ATMS tool[OL]. Center for Transportation Studies, University of Virginia, June,2003. http://www.cts.virginia.edu/docs/UVACTS-15-5-79.pdf.
[50]Smith B L, Zhang H, Fontaine M D, et al. Wireless location technology-based traffic monitoring: critical assessment and evaluation of an early-generation system[J]. Journal of Transportation Engineering,2004,130(5):576-574.
[51]Cayford R, Johnson T. Operational parameters affecting the use of anonymous cell phone tracking for generating traffic information[C]. Proceedings of Transportation Research Board 82th Annual Meeting, Washington DC, USA,2003.
[52]Fontaine M D, Yakkala A P, Smith B L. Probe sampling strategies for traffic monitoring sys-tems based on wireless location technology[OL]. Virginia Transportation Research Council, January,2007. http://www.virginiadot.org/vtrc/main/online reports/pdf/07-cr12.pdf.
[53]Markopoulos A, Pissaris P, Kyriazakos S, et al. Optimized handover procedure based on mobile location in cellular systems[C]. Proceedings of The 14th IEEE 2003 International Symposium on Personal, Indoor and Mobile Radio Communication Proceedings, Beijing, China,2003, 2490-2494.
[54]林洪桦.测量误差与不确定度评估[M].北京：机械工业出版社,2010.
[55]毕华,梁洪力,王珏.重采样方法与机器学习[J].计算机学报,2009,32(5)：862-877.
[56]冯春,谢进,李柏林,等.混沌优化算法的研究[C].第十四届全国机构学学术研讨会暨第二届海峡两岸机构学学术交流会论文集,重庆,中国,2004,301-306.
[57]杨迪雄,李刚,程耿东.非线性函数的混沌优化方法比较研究[J].计算力学学报,2004,21(3)：257-262.
[58]张文修,梁怡,徐萍.基于包含度的不确定推理[M].北京：清华大学出版社,2007.
[59]Florea M C, Jousselme A L, Bosse E, et al. Robust combination rules for evidence theory[J]. Information Fusion,2009,10(2):183-197.
[60]Cascetta E, Russo F, Viola F A, et al. A model of route perception in urban road networks [J]. Transportation Research Part B,2002,36(7):577-592.
[61]王熙照.模糊测度和模糊积分在分类技术中的应用[M].北京：科学出版社,2008.
[62]程维虎,陈冬.Logistic分布参数的渐近置信估计[J].北京工业大学学报,2001,27(2)：169-173.
[63]Suykens J A K, Gestel T V, Brabanter J D, et al. Least Squares Support Vector Machines[M]. Singapore:World Scientific Publishing Co. Pte. Ltd.,2002.
[64]刘鹏.基于LS-SVM目标识别的研究[D].西安：西安电子科技大学,2007.
[65]李丽娟.最小二乘支持向量机建模及预测控制算法研究[D].杭州：浙江大学,2008.
[66]向峥嵘.基于LS-SVM的复杂工业过程故障诊断方法研究[D].南京：南京理工大学,2009.
[67]Theodoridis S, Koutroumbas K. Pattern Recognition[M]. Singapore:Elsevier (Singapore) Pte. Ltd.,2009.
[68]Rokach L.Taxonomy for characterizing ensemble methods in classification tasks:A review and annotated bibliography [J]. Computational Statistics and Data Analysis,2009, 53(12):4046-4072.
[69]Gao C, Sang N, Tang Q. On selection and combination of weak learners in AdaBoost[J]. Pattern Recognition Letters,2010,31(9):991-1001.
[70]Kanji G.100 Statistical Tests[M]. London:SAGE Publications Ltd.,2006.
[71]林洪桦.剔除异常数据的稳健性处理方法[J].中国计量学院学报,2004,15(1)：20-24.
[72]朱连超,殷敬华,温松明,等.高分子科学实验中异常数据的判别与剔除[J].高分子材料科学与工程,2007,23(2)：5-8.
[73]Haykin S. Neural Networks:A Comprehensive Foundation[M]. Upper Saddle River, New Jersey, USA:Pearson Education Inc.,2004.
[74]Boyce D, Ruphail N, Kirson A. Estimation and Measurement of Link Travel Times in the ADVANCE Project[C], Proceedings of Proceedings of the IEEE-IEE Vehicle Navigation and Informations Systems Conference, Ottawa, Canada,1993,62-66.
[75]Xia J, Chen M. Freeway Travel Time Forecasting Under Incident[OL]. Department of Civil Engineering, University of Kentucky, September,2007. http://stc.utk.edu/STCresearch/completed/PDFs/S21 xia.pdf.
[76]Li R, Rose G, Sarvi M. Evaluation of speed-based travel time estimation models. Journal of Transportation Engineering[J],2006,132(7):540-547.
[77]Sun L, Yang J, Mahmassani H. Travel time estimation based on piecewise truncated quadratic speed trajectory[J]. Transportation Research Part A,2008,42(1):173-186.
[78]Ou Q, Lint J V, Hoogendoorn S. TravRes:A Method for High-resolution Traffic Speed Recon-struction Using GPS-based Travel-times[C]. Proceedings of 2010 13th International IEEE Annual Conference on Intelligent Transportation Systems, Madeira Island, Portugal,2010, 1195-1201.
[79]Van Lint J, Van Der Zijpp N. An improved travel-time estimation algorithm using dual loop detectors[C]. Proceedings of Transportation Research Board 82th Annual Meeting, Washington DC, USA,2003,1-13.
[80]Shen L. Freeway travel time estimation and prediction using dynamic neural networks[D]. USA: Florida International University, Jul,2008.
[81]杨晓芳,付强,张红军.入口匝道衔接区交通流运行机理解析[J].同济大学学报(自然科学版),2008,36(12)：1652-1655.
[82]熊列强,邵春福,王富,等. 出口匝道连接处通行能力分析计算模型[J]. 北京交通大学学报(自然科学版),2005,29(3)：70-72.
[83]Panichpapiboon S, Pattaraatikom W. An analysis of GPS sampling rates required in travel time estimation[C]. Proceedings of 2009 IEEE Vehicular Networking Conference, Tokyo, Japan, 2009,1-6.
[84]Lin C K, Chang G L. Development of a fuzzy-expert system for incident detection and classification[J]. Mathematical and Computer Modelling,27(9):9-25.
[85]Sheu J B. A fuzzy clustering-based approach to automatic freeway incident detection and characterization[J]. Fuzzy Sets and Systems,2002,128(3):377-388.
[86]陈德望.基于模糊聚类的快速路交通流状况分类[J].交通运输系统工程与信息,2005,5(1)：62-67.
[87]刘澜,戢晓峰.基于Vague集的道路交通状态决策信息融合[J].公路交通科技,2009,26(5)：112-116.
[88]Yuan F, Cheu R L. Incident detection using support vector machines[J]. Transportation Research Part C,11(3):309-328.
[89]诸彤宇,郭胜敏,吕卫锋.基于SVM的浮动车行驶模式判断模型[J].北京航空航天大学学报,2008,34(8)：976-980.
[90]Wang Y, Papageorgiou M, Messmer A, et al. An adaptive freeway traffic state estimation [J]. Automatica,2009,45(1):10-24.
[91]Zhang H, Zhang Y, Yao D, et al. An architecture of traffic state analysis based on multi-sensor fusion[C]. Proceedings of Proceedings of 2005 IEEE Intelligent Vehicles Symposium,2005, 855-860.
[92]Teng H, Qi Y. Application of wavelet technique to freeway incident detection [J]. Transportation Research Part C,11(3):289-308.
[93]邹亮,徐建闽,朱玲湘.基于融合技术的道路交通状态判别模型[J].清华大学学报(自然科学版),2007,47(Z2)：1822-1825.
[94]段后利,李志恒,李力,等.一种基于伪色彩图的网络交通状态观测分析方法[J].交通运输系统工程与信息,2009,9(4)：46-52.
[95]刘雁.区间数排序方法的新探讨[D].南宁：广西大学,2007.
[96]胡启洲.区间数理论的研究及其应用[M].北京：科学出版社,2010.
[97]胡启洲,于莉,张爱萍.基于三元区间数的多指标决策方法[J].系统管理学报,2010,19(1)：25-10.
[98]罗党.三参数区间灰数信息下的决策方法[J].系统工程理论与实践,2009,29(1)：124-130.
[99]兰蓉,范九伦.三参数区间值模糊集上的TOPSIS决策方法[J1.系统工程理论与实践,2009,29(5)：129-136.
[100]王力,张海,余贵珍,等.移动交通流检测技术探测车样本比例模型研究[J].交通运输系统工程与信息,2007,7(5)：31-36.
[101]洪珺,张学聃,韦忠亚,等.交通信息移动采集关键参数估计方法[J].微计算机信息,2008,24(3)：1-3.
[102]Cheu R L, Xie C, Lee D H. Probe vehicle population and sample size for arterial speed estimation[J]. Computer-Aided Civil and Infrastructure Engineering,2002,17(1):53-60.
[103]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,2004.
[104]Board T R. Highway Capacity Manual[R]. National Research Council,2000.
[105]Kerner B S. Three-phase traffic theory and highway capacity[J]. Physica A, 333(1):379-440.
[106]Kerner B S. The physics of traffic:empirical freeway pattern features, engineering applications and theory[M]. Heidelberg, Germany:Springer,2004.
[107]Guan W, He S. Phase identification of urban freeway traffic based on statistical properties [J]. Journal of Transportation Systems Engineering and Information Technology,2007, 7(5):42-50.
[108]He S, Guan W. Empirical investigations on traffic phase transitions at Beijing ring road[C]. Proceedings of Proceedings of IEEE International Conference on Intelligent Transportation Sys-tems, Seattle, USA,2007,290-295.
[109]Guan W, He S. Statistical Features of Traffic Flow on Urban Freeways[J]. Physica A,2008, 387(4):944-954.
[110]Guan W, He S, Ma J. Phase transition of urban freeway traffic flow[C]. Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China,2008, 868-874.
[111]仇国芳,李怀祖.区间数排序的包含度度量及构造方法[J].运筹与管理,2003,12(3)：13-17.
[112]Qiu G, Li H, Xu L, et al. A knowledge processing method for intelligent systems based on inclusion degree[J]. Expert Systems,2003,20(4):187-195.
[113]Chen S M, Lee L W. Fuzzy multiple attributes group decision-making based on ranking values and the arithmetic operations of interval type-2 fuzzy sets[J]. Expert Systems with Applications,2010,37(1):824-833.
[114]郭春香,郭耀煌.格序决策的格序化研究[J].系统工程理论方法应用,2004,13(5)：463-466.
[115]Lieu H. Traffic Flow Theory[OL]. Transportation Research Board, National Research Council,1999. http://www.fhwa.dot.gov/publications/research/operations/tft/index.cfm.
[116]杨永勤,刘小明,于泉,等.交通流三参数关系的研究[J].北京工业大学大学,2006,32(1)：43-47.
[117]雷丽,宋涛,董力耘,等.城市高架道路交通流三参数的现场实测和分析[J].山东大学学报(工学版),2006,36(1)：23-27.
[118]赵娜乐,于雷,陈旭梅,等.北京城市快速路交通流特性多维度研究[J].北京交通大学学报,2010,34(6)：35-39.
[119]Van Aerde M. Single Regime Speed-Flow-Density Relationship for Congested and Uncon-gested Highways [C]. Proceedings of Transportation Research Board 74th Annual Meeting, Wash-ington DC, USA,1995.
[120]Smets P. Belief functions:the disjunctive rule of combination and the generalized bayesian theorem[J]. International Journal of Approximate Reasoning,1993,9(1):1-35.
[121]Xu H, Smets P. Evidential Reasoning with Conditional Belief Functions[C]. Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, Seattle, USA,1994,598-605.
[122]Smets P. Analyzing the combination of conflicting belief functions[J]. Information Fusion, 2007,8(4):387-412.
[123]刘大有.知识系统中不确定性和模糊性处理的数值方法[M].长春：吉林大学出版社,2000.
[124]关伟.宏观交通流的定性建模方法[J].土木工程学报,2003,36(7)：66-71.
[125]Quiroga C, Bullock D. Determining of sample sizes for travel time studies[J]. ITE Journal, 1998,68(8):92-98.
[126]Quiroga C, Bullock D. Travel time studies with global positioning and geographic information systems:an integrated methodology[J]. Transportation Research Part C, 6(1):101-127.
[127]Wang L, Wang C, Shen X, et al. Probe vehicle sampling for real-time traffic data collection[C]. Proceedings of 8th IEEE ITSC, Vienna, Austria,2005,222-224.
[128]Wang L, Zhang H, Yu G, et al. Study of probe sample size model in probe vehicle technology[J]. Journal of Transportation Systems Engineering and Information Technology, 2007,7(5):31-36.
[129]Kovacevic-Vujcic V V, Cangalovic M M, Asic M D, et al. TABU search methodology in global optimization[J]. Computers & Mathematics with Applications,37(4),125-133.
[130]符卓.带装载能力约束的开放式车辆路径问题及其禁忌搜索算法研究[J].系统工程理论与实践,2004,24(3)：123-128.
[131]李相勇,田澎.带时间窗和随机时间车辆路径问题：模型和算法[J].系统工程理论与实践,2009,29(8)：81-90.
[2]殷伟,郭磷,方廷健,等.一种基于FCD的城市道路车流速度估计算法[J].中国科学技术大学学报,2008,38(9)：1113-1117.
[3]Hellinga B, Fu L, Takada H. Obtaining traveler information via mobile phone location referencing challenges and opportunities[C]. Proceedings of Annual Conference of the Transportation Association of Canada, St. John's, Newfoundland, Canada,2003,1-10.
[4]White J, Quick J, Philippou P. The use of mobile phone location data for traffic information[C]. Proceedings of Twelfth International Conference on Road Transport Information and Control, London, United Kingdom,2004,321-325.
[5]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(1)：63-68.
[6]Rose G. Mobile phones as traffic probes:practices, prospects and issues[J]. Transport Reviews, 2006,26(3):275-291.
[7]Qiu Z, Jing J, Peng C, et al. State of the art and practice:cellular probe technology applied in advanced traveler information system[C]. Proceedings of Transportation Research Board 86th Annual Meeting, Washington DC, USA,2007,1-19.
[8]Caceres N, Wideberg J, Benitez F. Review of traffic data estimations extracted from cellular network[J]. IET Intelligent Transportation System,2008,2(3):179-192.
[9]Steenbruggen J, Borzacchiello M T, Nijkamp P,et al. Real-time data from mobile phone networks for urban incidence and traffic management-A review of applications and opportunities [OL]. Faculty of Economics and Business Administration, University of Amsterdam,March,2010.http://dare.ubvu.vu.nl/handle/1871/15715.
[10]Valerio D. Road traffic information from cellular network signaling[OL]. Forschungszentrum Telekommunikation Wien, March,2009. http://userver.ftw.at/-valerio/files/ROADCELLreport.pdf.
[11]王贤君.现代无线通信系统与技术[M].南京：东南大学出版社,2009.
[12]杨家玮,盛敏,刘勤.移动通信基础[M].北京：电子工业出版社,2005.
[13]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.
[14]Zhao Y. Mobile phone location determination and its impact on intelligent transportation systems[J]. IEEE Transactions on Intelligent Transportation Systems,2000,1(1):55-64.
[15]Sun G, Chen J, Guo W, et al. Signal Processing Techniques in Network-aided Positioning A survey of state-of-the-art positioning designs[J]. IEEE Signal Processing Magazine,2005, 22(4):12-23.
[16]兰云飞.蜂窝网无线定位算法研究[D].郑州：郑州大学,2007.
[17]徐政五.基于GSM系统的定位技术研究[D].成都：电子科技大学,2006.
[18]王斌.基于多手机定位的公交车辆定位研究[D].重庆：重庆大学,2006.
[19]Wunnava S V, Yen K, Babij T, et al. Travel time estimation using cell phones (TTECP) for highways and roadways [OL]. Florida International University, April 12,2007. http://www.dot.state.fl.us/research-center/Completed_Proj/Summary_TE/FDOT_BD015_L2_rpt .pdf.
[20]Smith B L, Fontaine M D, Beaton J, et al. Private-Sector Provision of Congestion Data[OL]. University of Virginia, February 27,2007. http://cts.virginia.edu/docs/UVACTS-13-0-118.pdf.
[21]Cheng P, Qiu Z, Ran B. Particle filter based traffic state estimation using cell phone network data[C]. Proceedings of 2006 IEEE Intelligent Transportation Systems Conference,2006, 1047-1052.
[22]Cheng P, Qiu Z, Ran B. Traffic estimation based on particle filtering with stochastic state reconstruction using mobile network data[C]. Proceedings of Transportation Research Board 86th Annual Meeting,2007,1-15.
[23]Sankar R, Civil L. Intelligent traffic monitoring system using wireless cellular communications[C]. Proceedings of Proceedings of the 1997 IEEE SOUTHEASTCON, Blacksburg, USA,1997,210-214.
[24]Sankar R, Civil L. Traffic monitoring and congestion prediction using handovers in wireless cellular communications[C]. Proceedings of Proceedings of the 1997 47th IEEE Vehicular Technology Conference, Phoenix, USA,1997,520-524.
[25]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.
[26]杨飞,惠英,杨东援.基于手机切换定位的交通路网标定方法[J].同济大学学报(自然科学版),2009,37(1)：67-72.
[27]Ramm K, Czommer R, Schwieger V. Map-based Positioning using Mobile Phones[C]. Proceedings of The XXIII FIG Congress,2006,1-16.
[28]Wiltschko T, Schwieger V, Mohlenbrink W. Acquisition of traffic state information by mobile phone positioning[C]. Proceedings of The 6th European Congress on ITS,2007,1-8.
[29]Friedrich M, Jehlicka P, Otterstatter T, et al. Mobile phone data for telematic applications[C]. Proceedings of International Multi-Conference on Engineering and Technological Innovation, 2008,1-6.
[30]Schlaich J, Otterstatter T, Friedrich M. Generating Trajectories from Mobile Phone Data[C]. Proceedings of Transportation Research Board 89th Annual Meeting,2010,1-17.
[31]Fontaine M D, Smith B L. Improving the electiveness of traffic monitoring based on wireless location technology[OL]. Virginia Transportation Research Council, December,2004. http://www.virginiadot.org/vtrc/main/online reports/pdf/05-r17.pdf.
[32]Fontaine M D, Smith B L. Investigation of the performance of wireless location technology-based traffic monitoring systems[J]. Journal of Transportation Engineering,2007, 133(3):157-164.
[33]Kummala J. Travel time service utilising mobile phones[OL] Finnish Road Administration, May, 2002.http://alk.tiehallinto.fi/julkaisut/pdf/3200787-vm-aikapalvelu tulokset.pdf.
[34]Virtanen J. Mobile phones as probes in travel time monitoring[OL]. Finnish Road Administration,2002, http://www.durbit.se/Archives/ExternalPDF/MIP2002/Domain1/Deliverables/VIKINGY200213 I FI GSM travel time monitoring.pdf.
[35]Birle C, Wermuth M. The traffic online project[C]. Proceedings of 13th ITS World Congress, 2006,1-16.
[36]Bar-Gera H. Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times:A case study from Israel[J]. Transportation Research Part C,2007,15(6):380-391.
[37]Fontaine M D. Improving the effectiveness of wireless location technology-based traffic monitoring:an exploration of system design issues[D]. USA:School of Engineering and Applied Science, University of Virginia, Jan,2005.
[38]Gundlegard D, Karlsson J. Handover location accuracy for travel time estimation in GSM and UMTS[J]. IET Intelligent Transport Systems,2009,3(1):87-94.
[39]Qiu Z. Macroscopic traffic state estimation for large scale freeway network using wireless network data[D]. USA:University of Wisconsin-Madison, May,2007.
[40]Wilson E. Real-time traffic monitoring using mobile phone data[OL]. Vodafone Pilotentwicklung GmbH, May,2005. http://www.maths-in-industry.org/miis/30/1/TrafficMonitoring.pdf.
[41]Birle C. Traffic Online via GSM network data to traffic information in real time[OL]. Vodafone Group R&D, April,2008. http://www.its-munich.de/pdf/DynInfoBY/4-Birle.pdf.
[42]Maerivoet S, Logghe S. Validation of travel times based on cellular floating vehicle data[C]. Proceedings of 6th European Congress and Exhibition on Intelligent Transport Systems and Services,2007,1-8.
[43]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.
[44]Hellinga B, Izadpanah P, Takada H, et al. Decomposing travel times measured by probe-based traffic monitoring systems to individual road segments[J]. Transportation Research Part C,2008, 16(6):768-782.
[45]Takada H. Road traffic condition acquisition via mobile phone location referencing[D].Canada: University of Waterloo,2006.
[46]胡小文,杨东援.交通探测车的样本量模型[J].公路交通科技,2009,26(10)：111-116.
[47]Chen M, Chien S. Determining the number of probe vehicles for freeway travel time estimation using microscopic simulation[C]. Proceedings of Transportation Research Board 79th Annual Meeting,2000,1-25.
[48]Yim Y. The state of cellular probes[OL]. Institute of Transportation Studies, University of California, July,2003. http://www.path.berkeley.edu/PATH/publications/pdf/prr/2003/prr-2003-25.pdf.
[49]Smith B L, Zhang H, Fontaine M, et al. Cell phone probes as an ATMS tool[OL]. Center for Transportation Studies, University of Virginia, June,2003. http://www.cts.virginia.edu/docs/UVACTS-15-5-79.pdf.
[50]Smith B L, Zhang H, Fontaine M D, et al. Wireless location technology-based traffic monitoring: critical assessment and evaluation of an early-generation system[J]. Journal of Transportation Engineering,2004,130(5):576-574.
[51]Cayford R, Johnson T. Operational parameters affecting the use of anonymous cell phone tracking for generating traffic information[C]. Proceedings of Transportation Research Board 82th Annual Meeting, Washington DC, USA,2003.
[52]Fontaine M D, Yakkala A P, Smith B L. Probe sampling strategies for traffic monitoring sys-tems based on wireless location technology[OL]. Virginia Transportation Research Council, January,2007. http://www.virginiadot.org/vtrc/main/online reports/pdf/07-cr12.pdf.
[53]Markopoulos A, Pissaris P, Kyriazakos S, et al. Optimized handover procedure based on mobile location in cellular systems[C]. Proceedings of The 14th IEEE 2003 International Symposium on Personal, Indoor and Mobile Radio Communication Proceedings, Beijing, China,2003, 2490-2494.
[54]林洪桦.测量误差与不确定度评估[M].北京：机械工业出版社,2010.
[55]毕华,梁洪力,王珏.重采样方法与机器学习[J].计算机学报,2009,32(5)：862-877.
[56]冯春,谢进,李柏林,等.混沌优化算法的研究[C].第十四届全国机构学学术研讨会暨第二届海峡两岸机构学学术交流会论文集,重庆,中国,2004,301-306.
[57]杨迪雄,李刚,程耿东.非线性函数的混沌优化方法比较研究[J].计算力学学报,2004,21(3)：257-262.
[58]张文修,梁怡,徐萍.基于包含度的不确定推理[M].北京：清华大学出版社,2007.
[59]Florea M C, Jousselme A L, Bosse E, et al. Robust combination rules for evidence theory[J]. Information Fusion,2009,10(2):183-197.
[60]Cascetta E, Russo F, Viola F A, et al. A model of route perception in urban road networks [J]. Transportation Research Part B,2002,36(7):577-592.
[61]王熙照.模糊测度和模糊积分在分类技术中的应用[M].北京：科学出版社,2008.
[62]程维虎,陈冬.Logistic分布参数的渐近置信估计[J].北京工业大学学报,2001,27(2)：169-173.
[63]Suykens J A K, Gestel T V, Brabanter J D, et al. Least Squares Support Vector Machines[M]. Singapore:World Scientific Publishing Co. Pte. Ltd.,2002.
[64]刘鹏.基于LS-SVM目标识别的研究[D].西安：西安电子科技大学,2007.
[65]李丽娟.最小二乘支持向量机建模及预测控制算法研究[D].杭州：浙江大学,2008.
[66]向峥嵘.基于LS-SVM的复杂工业过程故障诊断方法研究[D].南京：南京理工大学,2009.
[67]Theodoridis S, Koutroumbas K. Pattern Recognition[M]. Singapore:Elsevier (Singapore) Pte. Ltd.,2009.
[68]Rokach L.Taxonomy for characterizing ensemble methods in classification tasks:A review and annotated bibliography [J]. Computational Statistics and Data Analysis,2009, 53(12):4046-4072.
[69]Gao C, Sang N, Tang Q. On selection and combination of weak learners in AdaBoost[J]. Pattern Recognition Letters,2010,31(9):991-1001.
[70]Kanji G.100 Statistical Tests[M]. London:SAGE Publications Ltd.,2006.
[71]林洪桦.剔除异常数据的稳健性处理方法[J].中国计量学院学报,2004,15(1)：20-24.
[72]朱连超,殷敬华,温松明,等.高分子科学实验中异常数据的判别与剔除[J].高分子材料科学与工程,2007,23(2)：5-8.
[73]Haykin S. Neural Networks:A Comprehensive Foundation[M]. Upper Saddle River, New Jersey, USA:Pearson Education Inc.,2004.
[74]Boyce D, Ruphail N, Kirson A. Estimation and Measurement of Link Travel Times in the ADVANCE Project[C], Proceedings of Proceedings of the IEEE-IEE Vehicle Navigation and Informations Systems Conference, Ottawa, Canada,1993,62-66.
[75]Xia J, Chen M. Freeway Travel Time Forecasting Under Incident[OL]. Department of Civil Engineering, University of Kentucky, September,2007. http://stc.utk.edu/STCresearch/completed/PDFs/S21 xia.pdf.
[76]Li R, Rose G, Sarvi M. Evaluation of speed-based travel time estimation models. Journal of Transportation Engineering[J],2006,132(7):540-547.
[77]Sun L, Yang J, Mahmassani H. Travel time estimation based on piecewise truncated quadratic speed trajectory[J]. Transportation Research Part A,2008,42(1):173-186.
[78]Ou Q, Lint J V, Hoogendoorn S. TravRes:A Method for High-resolution Traffic Speed Recon-struction Using GPS-based Travel-times[C]. Proceedings of 2010 13th International IEEE Annual Conference on Intelligent Transportation Systems, Madeira Island, Portugal,2010, 1195-1201.
[79]Van Lint J, Van Der Zijpp N. An improved travel-time estimation algorithm using dual loop detectors[C]. Proceedings of Transportation Research Board 82th Annual Meeting, Washington DC, USA,2003,1-13.
[80]Shen L. Freeway travel time estimation and prediction using dynamic neural networks[D]. USA: Florida International University, Jul,2008.
[81]杨晓芳,付强,张红军.入口匝道衔接区交通流运行机理解析[J].同济大学学报(自然科学版),2008,36(12)：1652-1655.
[82]熊列强,邵春福,王富,等. 出口匝道连接处通行能力分析计算模型[J]. 北京交通大学学报(自然科学版),2005,29(3)：70-72.
[83]Panichpapiboon S, Pattaraatikom W. An analysis of GPS sampling rates required in travel time estimation[C]. Proceedings of 2009 IEEE Vehicular Networking Conference, Tokyo, Japan, 2009,1-6.
[84]Lin C K, Chang G L. Development of a fuzzy-expert system for incident detection and classification[J]. Mathematical and Computer Modelling,27(9):9-25.
[85]Sheu J B. A fuzzy clustering-based approach to automatic freeway incident detection and characterization[J]. Fuzzy Sets and Systems,2002,128(3):377-388.
[86]陈德望.基于模糊聚类的快速路交通流状况分类[J].交通运输系统工程与信息,2005,5(1)：62-67.
[87]刘澜,戢晓峰.基于Vague集的道路交通状态决策信息融合[J].公路交通科技,2009,26(5)：112-116.
[88]Yuan F, Cheu R L. Incident detection using support vector machines[J]. Transportation Research Part C,11(3):309-328.
[89]诸彤宇,郭胜敏,吕卫锋.基于SVM的浮动车行驶模式判断模型[J].北京航空航天大学学报,2008,34(8)：976-980.
[90]Wang Y, Papageorgiou M, Messmer A, et al. An adaptive freeway traffic state estimation [J]. Automatica,2009,45(1):10-24.
[91]Zhang H, Zhang Y, Yao D, et al. An architecture of traffic state analysis based on multi-sensor fusion[C]. Proceedings of Proceedings of 2005 IEEE Intelligent Vehicles Symposium,2005, 855-860.
[92]Teng H, Qi Y. Application of wavelet technique to freeway incident detection [J]. Transportation Research Part C,11(3):289-308.
[93]邹亮,徐建闽,朱玲湘.基于融合技术的道路交通状态判别模型[J].清华大学学报(自然科学版),2007,47(Z2)：1822-1825.
[94]段后利,李志恒,李力,等.一种基于伪色彩图的网络交通状态观测分析方法[J].交通运输系统工程与信息,2009,9(4)：46-52.
[95]刘雁.区间数排序方法的新探讨[D].南宁：广西大学,2007.
[96]胡启洲.区间数理论的研究及其应用[M].北京：科学出版社,2010.
[97]胡启洲,于莉,张爱萍.基于三元区间数的多指标决策方法[J].系统管理学报,2010,19(1)：25-10.
[98]罗党.三参数区间灰数信息下的决策方法[J].系统工程理论与实践,2009,29(1)：124-130.
[99]兰蓉,范九伦.三参数区间值模糊集上的TOPSIS决策方法[J1.系统工程理论与实践,2009,29(5)：129-136.
[100]王力,张海,余贵珍,等.移动交通流检测技术探测车样本比例模型研究[J].交通运输系统工程与信息,2007,7(5)：31-36.
[101]洪珺,张学聃,韦忠亚,等.交通信息移动采集关键参数估计方法[J].微计算机信息,2008,24(3)：1-3.
[102]Cheu R L, Xie C, Lee D H. Probe vehicle population and sample size for arterial speed estimation[J]. Computer-Aided Civil and Infrastructure Engineering,2002,17(1):53-60.
[103]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,2004.
[104]Board T R. Highway Capacity Manual[R]. National Research Council,2000.
[105]Kerner B S. Three-phase traffic theory and highway capacity[J]. Physica A, 333(1):379-440.
[106]Kerner B S. The physics of traffic:empirical freeway pattern features, engineering applications and theory[M]. Heidelberg, Germany:Springer,2004.
[107]Guan W, He S. Phase identification of urban freeway traffic based on statistical properties [J]. Journal of Transportation Systems Engineering and Information Technology,2007, 7(5):42-50.
[108]He S, Guan W. Empirical investigations on traffic phase transitions at Beijing ring road[C]. Proceedings of Proceedings of IEEE International Conference on Intelligent Transportation Sys-tems, Seattle, USA,2007,290-295.
[109]Guan W, He S. Statistical Features of Traffic Flow on Urban Freeways[J]. Physica A,2008, 387(4):944-954.
[110]Guan W, He S, Ma J. Phase transition of urban freeway traffic flow[C]. Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China,2008, 868-874.
[111]仇国芳,李怀祖.区间数排序的包含度度量及构造方法[J].运筹与管理,2003,12(3)：13-17.
[112]Qiu G, Li H, Xu L, et al. A knowledge processing method for intelligent systems based on inclusion degree[J]. Expert Systems,2003,20(4):187-195.
[113]Chen S M, Lee L W. Fuzzy multiple attributes group decision-making based on ranking values and the arithmetic operations of interval type-2 fuzzy sets[J]. Expert Systems with Applications,2010,37(1):824-833.
[114]郭春香,郭耀煌.格序决策的格序化研究[J].系统工程理论方法应用,2004,13(5)：463-466.
[115]Lieu H. Traffic Flow Theory[OL]. Transportation Research Board, National Research Council,1999. http://www.fhwa.dot.gov/publications/research/operations/tft/index.cfm.
[116]杨永勤,刘小明,于泉,等.交通流三参数关系的研究[J].北京工业大学大学,2006,32(1)：43-47.
[117]雷丽,宋涛,董力耘,等.城市高架道路交通流三参数的现场实测和分析[J].山东大学学报(工学版),2006,36(1)：23-27.
[118]赵娜乐,于雷,陈旭梅,等.北京城市快速路交通流特性多维度研究[J].北京交通大学学报,2010,34(6)：35-39.
[119]Van Aerde M. Single Regime Speed-Flow-Density Relationship for Congested and Uncon-gested Highways [C]. Proceedings of Transportation Research Board 74th Annual Meeting, Wash-ington DC, USA,1995.
[120]Smets P. Belief functions:the disjunctive rule of combination and the generalized bayesian theorem[J]. International Journal of Approximate Reasoning,1993,9(1):1-35.
[121]Xu H, Smets P. Evidential Reasoning with Conditional Belief Functions[C]. Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, Seattle, USA,1994,598-605.
[122]Smets P. Analyzing the combination of conflicting belief functions[J]. Information Fusion, 2007,8(4):387-412.
[123]刘大有.知识系统中不确定性和模糊性处理的数值方法[M].长春：吉林大学出版社,2000.
[124]关伟.宏观交通流的定性建模方法[J].土木工程学报,2003,36(7)：66-71.
[125]Quiroga C, Bullock D. Determining of sample sizes for travel time studies[J]. ITE Journal, 1998,68(8):92-98.
[126]Quiroga C, Bullock D. Travel time studies with global positioning and geographic information systems:an integrated methodology[J]. Transportation Research Part C, 6(1):101-127.
[127]Wang L, Wang C, Shen X, et al. Probe vehicle sampling for real-time traffic data collection[C]. Proceedings of 8th IEEE ITSC, Vienna, Austria,2005,222-224.
[128]Wang L, Zhang H, Yu G, et al. Study of probe sample size model in probe vehicle technology[J]. Journal of Transportation Systems Engineering and Information Technology, 2007,7(5):31-36.
[129]Kovacevic-Vujcic V V, Cangalovic M M, Asic M D, et al. TABU search methodology in global optimization[J]. Computers & Mathematics with Applications,37(4),125-133.
[130]符卓.带装载能力约束的开放式车辆路径问题及其禁忌搜索算法研究[J].系统工程理论与实践,2004,24(3)：123-128.
[131]李相勇,田澎.带时间窗和随机时间车辆路径问题：模型和算法[J].系统工程理论与实践,2009,29(8)：81-90.