基于“3S”的实时交通信息系统关键技术研究
|
摘要
城市的交通拥堵问题一直是制约世界各国大城市发展和困扰城市正常运转秩序的难题。尤其对正处于快速城市化的中国,这种制约和困扰更甚。为此,世界各国均在城市交通优化中投入了巨大的财力和物力,实时交通信息系统RTTIS(Real-Time Traffic Information System)因此诞生和发展。近三十年来,RTTIS的软、硬件伴随信息、通讯、3S (GPS、GIS、RS)、传感和电子等技术的发展而不断进步和完善,其中3S技术已成为RTTIS的重要支撑。然而,3S理论与技术本身也在不断发展和完善,这些发展必将推动RTTIS理论与方法体系不断完善和发展。本文着重于3S技术支撑下的RTTIS应用研究,主要创新和研究内容如下：
(1)提出基于三种具有城市地理特征的抽象路网使用多智能体微观交通仿真技术来对RTTIS在不同装备比例下交通系统效率进行细致深入的研究,拓展了RTTIS评估研究结论适用性。结果表明：在平行结构路网中,当车辆RTTIS的使用比例少于30%时,交通系统效率得以提高,超过30%优化效果下降,当RTTIS的使用比例高于90%以上时,交通系统和车辆的运行状态最差,相比没有植入RTTIS交通系统车辆平均延迟时间几乎下降了26%；在格状结构和环状结构路网中,RTTIS的使用比例持续增高能够持续提高所有车辆交通效率。
(2)使用基于多智能体的微观仿真模型来交通系统中的交通博弈与均衡现象进行研究。对植入和没有植入RTTIS的交通系统是否能够收敛于均衡状态做了仿真的测定,发现在这两种情形下基于三种抽象路网运行下的交通系统均能收敛于均衡状态,RTTIS的比例变化能使系统收敛于不同的均衡状态,且均衡状态与系统优化无必然联系。
(3)针对RTTIS就多源遥感数据挖掘提出了基于K阶中心矩多源遥感影像数据融合方法。使用该方法产生的图像是多源遥感影像各通道均值向量与特征向量间的差异图,当K值处于低水平时,图像信息获得明显的增强。伴随K值增加,差异性增强,同时图像内容被简化。图像由信息增强到简化的变化随着K值的增大而变化,该方法将图像信息增强与简化这两种反向性能融于一身,使得该方法在实时交通信息数据挖掘中具有更大的灵活性。
(4)基于实时交通信息数据采集、存储和挖掘提出使用张量来构造时空数据模型,从GIS空间分析和面向对象的角度实现交通信息时-空数据有效联合建模,该模型相对以往模型更有效的反应了交通实时信息时-空数据联动特征,有效消除数据冗余。
论文基于3S理论技术力图推进RTTIS在交通领域的优化研究与应用。通过论文的研究工作,进一步明晰了RTTIS在交通系统中的作用和表现,优化了RTTIS系统。其成果在该领域具有重要的借鉴和参考价值。
The traffic congestion have always been a big problem to harass cities'development and people's life everywhere in the world. Especially in China, quick urbanization processing makes the problem worse. In order to tackle this problem, each country has spent a lot of money in urban traffic optimization, hence the RTTIS (Real-Time Traffic Information System) was born and developed. In recent thirty years, with the developments in the fields of information, communication,3S(GPS、GIS、RS) and electronic technology, the software and hardware of RTTIS have been greatly improved. Among these technologies,3S has provided the RTTIS with great support. However, the theory and technology of3S are keeping updating nowadays, so it is necessary to improve the application of3S in RTTIS accordingly. Research and innovations on RTTIS have conducted in four aspects as following.
Firstly, based on theory and technology of3S, the efficiency of traffic system with RTTIS is studied on the basis of three kinds of abstract road networks which widely typify features of city roads, and comprehensive analyses of traffic system efficiency through traffic simulation based on mulit-agent on are made in different proportion of RTTIS. The study results indicate:in the parallel road networks, though at first, the performance of every vehicle and whole traffic system is enhanced when the proportion of vehicles with RTTIS is more than0%and less than30%, when the proportion is more than30%, the optimization effect of traffic performance declines, and when the proportion of RTTIS is more than90%, vehicles and the traffic system perform worst, which is less26%than the effect of the traffic system performance without RTTIS at all; in the grid and ring road networks, the more RTTIS is used, the better the performance of vehicles both with and without RTTIS, and the efficiencies of traffic performance of these two kinds of vehicles are nearly the same when the proportion of RTTIS is100%, that is to say, RTTIS benefits not only individuals but the whole traffic system as well.
Secondly, traffic system equilibrium is also examined. Through simulation experiments, the measurements of whether the traffic system with as well as without RTTIS can converge on equilibrium are made in the thesis. The results show that both the traffic system with as well as without RTTIS are able to converge on equilibrium under the performance in three abstract road networks. The results also indicate that different proportion of RTTIS can make the system converge on different equilibrium, and some equilibrium can make the system optimized but some can not.
Thirdly, aiming at the traffic system, the remote image data fusion based on the K order central moment is proposed through multi-source remote image data digging. A series of images constructed in this way are based on the difference between the average vector of multichannel and the feature vector of single band image. The images'quantity of information improve greatly as k value is low. With k increased, the reconstructing images'quantity of information decreased sharply and the difference between feature vectors and mean vectors is amplified.
Fourthly, the new GIS Spatial-Temporal Data Model is put forward. This model reduces the data redundancy and can be calculated easily.
Based on3S technology, the research tries to promote the application of RTTIS in traffic optimization. The effect of RTTIS in traffic system is figured out in detail and the RTTIS is optimized by study in thesis. The conclusions are important and valuable in the traffic field.
(1)提出基于三种具有城市地理特征的抽象路网使用多智能体微观交通仿真技术来对RTTIS在不同装备比例下交通系统效率进行细致深入的研究,拓展了RTTIS评估研究结论适用性。结果表明：在平行结构路网中,当车辆RTTIS的使用比例少于30%时,交通系统效率得以提高,超过30%优化效果下降,当RTTIS的使用比例高于90%以上时,交通系统和车辆的运行状态最差,相比没有植入RTTIS交通系统车辆平均延迟时间几乎下降了26%；在格状结构和环状结构路网中,RTTIS的使用比例持续增高能够持续提高所有车辆交通效率。
(2)使用基于多智能体的微观仿真模型来交通系统中的交通博弈与均衡现象进行研究。对植入和没有植入RTTIS的交通系统是否能够收敛于均衡状态做了仿真的测定,发现在这两种情形下基于三种抽象路网运行下的交通系统均能收敛于均衡状态,RTTIS的比例变化能使系统收敛于不同的均衡状态,且均衡状态与系统优化无必然联系。
(3)针对RTTIS就多源遥感数据挖掘提出了基于K阶中心矩多源遥感影像数据融合方法。使用该方法产生的图像是多源遥感影像各通道均值向量与特征向量间的差异图,当K值处于低水平时,图像信息获得明显的增强。伴随K值增加,差异性增强,同时图像内容被简化。图像由信息增强到简化的变化随着K值的增大而变化,该方法将图像信息增强与简化这两种反向性能融于一身,使得该方法在实时交通信息数据挖掘中具有更大的灵活性。
(4)基于实时交通信息数据采集、存储和挖掘提出使用张量来构造时空数据模型,从GIS空间分析和面向对象的角度实现交通信息时-空数据有效联合建模,该模型相对以往模型更有效的反应了交通实时信息时-空数据联动特征,有效消除数据冗余。
论文基于3S理论技术力图推进RTTIS在交通领域的优化研究与应用。通过论文的研究工作,进一步明晰了RTTIS在交通系统中的作用和表现,优化了RTTIS系统。其成果在该领域具有重要的借鉴和参考价值。
The traffic congestion have always been a big problem to harass cities'development and people's life everywhere in the world. Especially in China, quick urbanization processing makes the problem worse. In order to tackle this problem, each country has spent a lot of money in urban traffic optimization, hence the RTTIS (Real-Time Traffic Information System) was born and developed. In recent thirty years, with the developments in the fields of information, communication,3S(GPS、GIS、RS) and electronic technology, the software and hardware of RTTIS have been greatly improved. Among these technologies,3S has provided the RTTIS with great support. However, the theory and technology of3S are keeping updating nowadays, so it is necessary to improve the application of3S in RTTIS accordingly. Research and innovations on RTTIS have conducted in four aspects as following.
Firstly, based on theory and technology of3S, the efficiency of traffic system with RTTIS is studied on the basis of three kinds of abstract road networks which widely typify features of city roads, and comprehensive analyses of traffic system efficiency through traffic simulation based on mulit-agent on are made in different proportion of RTTIS. The study results indicate:in the parallel road networks, though at first, the performance of every vehicle and whole traffic system is enhanced when the proportion of vehicles with RTTIS is more than0%and less than30%, when the proportion is more than30%, the optimization effect of traffic performance declines, and when the proportion of RTTIS is more than90%, vehicles and the traffic system perform worst, which is less26%than the effect of the traffic system performance without RTTIS at all; in the grid and ring road networks, the more RTTIS is used, the better the performance of vehicles both with and without RTTIS, and the efficiencies of traffic performance of these two kinds of vehicles are nearly the same when the proportion of RTTIS is100%, that is to say, RTTIS benefits not only individuals but the whole traffic system as well.
Secondly, traffic system equilibrium is also examined. Through simulation experiments, the measurements of whether the traffic system with as well as without RTTIS can converge on equilibrium are made in the thesis. The results show that both the traffic system with as well as without RTTIS are able to converge on equilibrium under the performance in three abstract road networks. The results also indicate that different proportion of RTTIS can make the system converge on different equilibrium, and some equilibrium can make the system optimized but some can not.
Thirdly, aiming at the traffic system, the remote image data fusion based on the K order central moment is proposed through multi-source remote image data digging. A series of images constructed in this way are based on the difference between the average vector of multichannel and the feature vector of single band image. The images'quantity of information improve greatly as k value is low. With k increased, the reconstructing images'quantity of information decreased sharply and the difference between feature vectors and mean vectors is amplified.
Fourthly, the new GIS Spatial-Temporal Data Model is put forward. This model reduces the data redundancy and can be calculated easily.
Based on3S technology, the research tries to promote the application of RTTIS in traffic optimization. The effect of RTTIS in traffic system is figured out in detail and the RTTIS is optimized by study in thesis. The conclusions are important and valuable in the traffic field.
引文
[1]Chorus C.G, Molin E.J.E., Van Wee B. Use and Effects of Advanced Traveller Information Services (ATIS):A Review of the Literature [J]. Transport Rev,2006,26(2):127-149
[2]Basu D., Maitra B. Stated Preference Approach for Valuation of Travel Time Displayed as Traffic Information on a VMS Board [J]. Journal of Urban Planning and Development-Asce,2010,136(3):214-224
[3]Arnott R., de Pablma A., Lindsey R. Does providing information to drivers reduce traffic congestion? [J]. Transportation Research Part A:General,1991, 25(5):309-318
[4]Jayakrishnan R., McNally M., Cohen M., Center U.o.C.T. Simulation of Advanced Traveller Information Systems (ATIS) Strategies to Reduce Non-recurring Congestion from Special Events[M]. University of California, Institute of Transportation Studies,1993
[5]Wardrop J. G. [J]. Proceedings of the Institution of Civil Engineers Ⅱ,1952,1: 325
[6]Jarasuniene A., Yatskiv I., Kaunas Univ T. Analysis of improvement of road safety using intelligent transport systems (ITS)[M].2007
[7]Abdel-Aty M.A., Kitamura R., Jovanis P.P. Using stated preference data for studying the effect of advanced traffic information on drivers' route choice [J]. Transportation Research Part C:Emerging Technologies,1997,5(1):39-50
[8]Martin P.T. TRAVELER INFORMATION SYSTEMS [J].2005
[9]McMurchie L., Ebeling C. PathFinder:a negotiation-based performance-driven router for FPGAs,1995.111-117
[10]Saricks C.L., Schofer J.L., S t S., Belella P.A. Evaluating effectiveness of real-time advanced traveler information systems using a small test vehicle fleet [J]. Transportation Research Record:Journal of the Transportation Research Board,1997,1588(-1):41-48
[11]Inman V., Safety U.S.F.H.A.O.o., Research T.O., Development, Center T.-F.H.R. TravTek Evaluation, Yoked Driver Study[M]. US Dept. of Transportation, Federal Highway Administration,1995
[12]Senn L. SWIFT ATIS:consumer reactions and marketability,126/1-126/5 vol. 2
[13]READER N.T.O. Colorado Mayday [J].1998
[14]杨兆升,胡坚明,温慧敏.城市交通流诱导系统实施框架研究[J].ITS通讯,2001：18-24
[15]Jayakrishnan R., McNally M.G, Cohen M.I., Center U.o.C.T., University of California I.D.o.C.E., University of California I.I.o.T.S. Simulation of Advanced Traveller Information Systems (ATIS) Strategies to Reduce Non-recurring Congestion from Special Events[M]. University of California, Institute of Transportation Studies,1993
[16]Uno N., Iida Y., Kawaratani S. Effects of dynamic information system on travel time reliability of road network [C].3rd International Conference on Traffic and Transportation Studies, ICTTS 2002, July 23,2002-July 25, 2002. Guilin, China,2002.911-918
[17]Rickert M., Nagel K. Dynamic traffic assignment on parallel computers in TRANSIMS [J]. Future generation computer systems,2001,17(5):637-648
[18]Liu T., Huang H., Tian L. Microscopic simulation of multi-lane traffic under dynamic tolling and information feedback [J]. Journal of Central South University of Technology,2009,16(5):865-870
[19]Emmerink R.H.M., Verhoef E.T., Nijkamp P., Rietveld P. Information provision in road transport with elastic demand:A welfare economic approach [J]. Journal of Transport Economics and Policy,1996:117-136
[20]Ben-Elia E., Ere v I., Shiftan Y. The combined effect of information and experience on drivers' route-choice behavior [J]. Transportation,2008,35(2): 165-177
[21]Enge lson L. On dynamics of traffic queues in a road network with route choice based on real time traffic information [J]. Transportation Research Part C:Emerging Technologies,2003,11(2):161-183
[22]Hall R. Route choice and advanced traveler information systems on a capacitated and dynamic network [J]. Transportation Research Part C: Emerging Technologies,1996,4(5):289-306
[23]Wahle J., Bazzan A., Klugl F., Schreckenberg M. The impact of real-time information in a two-route scenario using agent-based simulation [J]. Transport Res C-Emer,2002,10(5-6):399-417
[24]Chootinan P., Wong S.C., Chen A. A reliability-based network design problem [J]. Journal of Advanced Transportation,2005,39(3):247-270
[25]Abdalla M.F., Abdel-Aty M. Modeling Travel Time Under ATIS Using Mixed Linear Models [J]. Transportation,2006,33(1):63-82
[26]Ziegelmeyer A., Koessler F., My K., Denant-Boemont L. Road Traffic Congestion and Public Information:An Experimental Investigation [J]. J Transp Econ Policy,2008,42(1):43-82
[27]Davis L. Realizing Wardrop equilibria with real-time traffic information [J]. Physica A:Statistical Mechanics and its Applications,2009,388(20): 4459.4474
[28]GAO. Surface Transportation:Efforts to address highway congestion through real-time traffic information systems are expanding but face implementation challenges. [J]. GAO-10-121R (Washington, D.C.:November 2009),2009
[29]Ge Y.E., Zhang H.M., Lam W.H.K. Network reserve capacity under influence of traveler information [J]. Journal of Transportation Engineering,2003, 129(Compendex):262-270
[30]Chorus C.G, Molin E.J.E., Van Wee B. Use and Effects of Advanced Traveller Information Services (ATIS):A Review of the Literature [J]. Transport Reviews:A Transnational Trans disciplinary Journal,2006,26(2): 127-149
[31]Chorus C.G, Arentze T.A., Timmermans H.J.P. Information impact on quality of multimodal travel choices:conceptualizations and empirical analyses [J]. Transportation,2007,34(6):625-645
[32]Davis L.C. Realizing Wardrop equilibria with real-time traffic information [J]. Physica a-Statistical Mechanics and Its Applications,2009,388(20): 4459.4474
[33]Ben-Akiva M., De Palma A. Dynamic network models and driver information systems [J]. Transport Res A-Pol,1991,25(5):251-266
[34]Stern E., Sinuany-Stern Z., Spharadi Z., Holm E. Congestion-related information and road network performance [J]. Journal of Transport Geography,1996,4(3):169-178
[35]Selten R., Chmura T., Pitz T., Kube S., Schreckenberg M. Commuters route choice behaviour [J]. Game Econ Behav,2007,58(2):394-406
[36]Jayakrishnan R., Mahmassani H.S. Dynamic simulation-assignment methodology to evaluate in-vehicle information strategies in urban traffic networks [C].1990 Winter Simulation Conference Proceedings (Cat. No.90CH2926-4),9-12 Dec.1990. New York, NY, USA,1990.763-9
[37]Hall R. Route choice and advanced traveler information systems on a capacitated and dynamic network [J]. Transport Res C-Emer,1996,4(5): 289-306
[38]Faghri A. Trip generation analysis by artificial neural networks [C]. Microcomputer in Transportation. Proceedings of the 4th International Conference,22-24 July 1992. New York, NY, USA,1993.805-15
[39]Zhenghong P., Wenting D., Junfeng X. Research on trip-generation forecasting model based on neural networks and genetic algorithms [C].2010 International Conference on Mechanic Automation and Control Engineering (MACE),26-28 June 2010. Piscataway, NJ, USA,2010.4 pp.
[40]Dey S.S., Fricker J.D. Bayesian updating of trip generation data:combining national trip generation rates with local data [J]. Transportation,1994, 21(Compendex):393-403
[41]Spiess H. A maximum likelihood model for estimating origin-destination matrices [J]. Transportation Research Part B:Methodological,1987,21(5): 395-412
[42]Shou-Ren H., Chi-Bang C. Dynamic estimation of freeway origin-destination demand and travel time using extended Kalman filtering algorithm [C].2004 IEEE International Conference on Networking, Sensing and Control,21-23 March 2004. Piscataway, NJ, USA,2004.1329-34
[43]Li J., Lin B., Sun Z., Geng X. An estimation model of time-varying origin-destination flows in expressway corridors based on unscented Kalman filter [J]. Science in China Series E:Technological Sciences,2009, 52(Copyright 2009, The Institution of Engineering and Technology):2069-78
[44]Agents A. Multi-Agent Systems [J]. New York, NY, USA. ACM:394"C401
[45]Kittock J.E. Emergent conventions and the structure of multi-agent systems, 1993.
[46]Luck M. Guest editorial:Challenges for agent-based computing [J]. Autonomous Agents and Multi-Agent Systems,2004,9(3):199-201
[47]Chen B., Cheng H.H. A review of the applications of agent technology in traffic and transportation systems [J]. Intelligent Transportation Systems, IEEE Transactions on,2010,11(2):485-497
[48]Garcia-Serrano A., Teruel Vioque D., Carbone F., M"|ndez V. FIPA-compliant MAS development for road traffic management with a knowledge-based approach:the TRACK-R agents,2003.
[49]Tom(?)s V.R., Garc"aa L.A. Agent-based management of non urban road meteorological incidents [J]. Multi-Agent Systems and Applications IV,2005: 213-222
[50]Chen B., Cheng H.H., Palen J. Mobile(?)C:a mobile agent platform for mobile C/C++ agents [J]. Software:Practice and Experience,2006,36(15): 1711-1733
[51]Hern"(?)ndez J.Z., Ossowski S., Garc"aa-Serrano A. Multiagent architectures for intelligent traffic management systems [J]. Transportation Research Part C:Emerging Technologies,2002,10(5):473-506
[52]Wang F.Y. Agent-based control for networked traffic management systems [J]. Intelligent Systems, IEEE,2005,20(5):92-96
[53]Katwijk R.T., Koningsbruggen P., Schutter B., Hellendoorn J. A test bed for multi-agent control systems in road traffic management [J]. Applications of Agent Technology in Traffic and Transportation,2005:113-131
[54]Logi F., Ritchie S.G A multi-agent architecture for cooperative inter-jurisdictional traffic congestion management [J]. Transportation Research Part C:Emerging Technologies,2002,10(5-6):507-527
[55]Roozemond D.A. Using autonomous intelligent agents for urban traffic control systems,1999.
[56]Guo D., Li Z., Song J., Zhang Y. A study on the framework of urban traffic control system,842-846 vol. 1
[57]Choy M.C., Srinivasan D., Cheu R.L. Cooperative, hybrid agent architecture for real-time traffic signal control [J]. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on,2003,33(5):597-607
[58]Zhao J., Bukkapatnam S., Dessouky M.M. Distributed architecture for real-time coordination of bus holding in transit networks [J]. Intelligent Transportation Systems, IEEE Transactions on,2003,4(1):43-51
[59]Zhang H.S., Zhang Y., Li Z.H., Hu D.C. Spatial-temporal traffic data analysis based on global data management using MAS [J]. Intelligent Transportation Systems, IEEE Transactions on,2004,5(4):267-275
[60]Ossowski S., Cuena J., Garc"aa-Serrano A. A case of multiagent decision support:using autonomous agents for urban traffic control [J]. Progress in Artific ial Inte lligence(?)aIBERAMIA 98,1998:468-469
[61]Srinivasan D., Choy M. Cooperative multi-agent system for coordinated traffic signal control [J].2006
[62]Wang F.Y. Agent-based control for fuzzy behavior programming in robotic excavation [J]. Fuzzy Systems, IEEE Transactions on,2004,12(4):540-548
[63]Chen R.S., Chen D.K., Lin S.Y. Actam:Cooperative multi-agent system architecture for urban traffic signal control [J]. IEICE transactions on information and systems,2005,88(1):119-126
[64]Kosonen I. Multi-agent fuzzy signal control based on real-time simulation [J]. Transportation Research Part C:Emerging Technologies,2003,11(5): 389-403
[65]Moreno A., Valls A., Viejo A., Inform(?)tica U.R.i.V.D.d.E. Using JADE-LEAP implement agents in mobile devices[M]. Univers itat Rovira i Virgili. Departament d'Enginyeria Inforrm(?)tica,2003
[66]Seow K.T., Dang N.H., Lee D.H. A collaborative multiagent taxi-dispatch system [J]. Automation Science and Engineering, IEEE Transactions on, 2010,7(3):607-616
[67]Ossowski S., Hern(?)ndez J.Z., Belmonte M.V., Fer(?)ndez A., Garc"aa-Serrano A., P"|rez-de-la-Cruz J.L., Serrano J.M., Triguero F. Decision support for traffic management based on organisational and communicative multiagent abstractions [J]. Transportation Research Part C: Emerging Technologies,2005,13(4):272-298
[68]Balbo F., Pinson S. Toward a multi-agent modelling approach for urban public transportation systems [J]. Engineering societies in the agents world II, 2001:160-174
[69]Balbo F., Pinson S. Dynamic modeling of a disturbance in a multi-agent system for traffic regulation [J]. Decision Support Systems,2005,41(1): 131-146
[70]Burmeister B., Doormann J., Matylis G Agent-oriented traffic simulation [J]. Trans. Soc. Comput. SimuL Int.,1997,14(2):79-86
[71]Fischer K., Chaib-draa B., Muller J.P., Pischel M., Gerber C. A simulation approach based on negotiation and cooperation between agents:a case study [J]. Systems, Man, and Cybernetics, Part C:Applications and Reviews, IEEE Transactions on,1999,29(4):531-545
[72]Rossetti R.J.F., Bordini R.H., Bazzan A.L.C., Bampi S., Liu R., Vliet D.V. Using BDI agents to improve driver modelling in a commuter scenario [J]. Transportation Research Part C:Emerging Technologies,2002,10(5-6): 373-398
[73]Panwai S., Dia H. Neural agent car-following models [J]. Intelligent Transportation Systems, IEEE Transactions on,2007,8(1):60-70
[74]Wahle J., Bazzan A.L.C., Kl(?)gl F., Schreckenberg M. The impact of real-time information in a two-route scenario using agent-based simulation [J]. Transportation Research Part C:Emerging Technologies,2002,10(5-6): 399-417
[75]Adler J.L., Satapathy G., Manikonda V., Bowles B., Blue V.J. A multi-agent approach to cooperative traffic management and route guidance [J]. Transportation Research Part B:Methodological,2005,39(4):297-318
[76]Sukthankar R., Baluja S., Hancock J. Multiple adaptive agents for tactical driving [J]. Applied Intelligence,1998,9(1):7-23
[77]Hidas P. Modelling vehicle interactions in microscopic simulation of merging and weaving [J]. Transportation Research Part C:Emerging Technologies, 2005,13(1):37-62
[78]Castro da Silva B., Bazzan A., Andriotti G., Lopes F., de Oliveira D. ITSUMO:an intelligent transportation system for urban mobility [J]. Innovative Internet Community Systems,2006:224-235
[79]Li M., Hallam J., Pryor L., Chan S., Chong K. A cooperative intelligent system for urban traffic problems,1996.162-167
[80]Liu Z., Ishida T., Sheng H. Multiagent-based demand bus simulation for Shanghai [J]. Massively Multi-Agent Systems I,2005:309-322
[81]Kukla R., Kerridge J., Willis A., Hine J. PEDFLOW:Development of an autonomous agent model of pedestrian flow [J]. Transportation Research Record:Journal of the Transportation Research Board,2001,1774(-1):11-17
[82]Gambardella L.M., Rizzoli A.E., Funk P. Agent-based planning and simulation of combined rail/road transport [J]. Simulation,2002,78(5):293
[83]Weigang L., Dib M.V.P., de Melo A.C.M. Method to balance the communication among multi-agents in real time traffic synchronization [J]. Fuzzy Systems and Knowledge Discovery,2005:1053-1062
[84]Hall"| S., Chaib-draa B. A collaborative driving system based on multiagent modelling and simulations [J]. Transportation Research Part C:Emerging Technologies,2005,13(4):320-345
[85]Rossetti R.J.F., Ferreira P.A.F., Braga RA.M., Oliveira E.C. Towards an artificial traffic control system,2008.14-19
[86]Rakha H.A., Van Aerde M.W. Comparison of simulation modules of TRANS YT and INTEGRATION models [J]. Transportation Research Record: Journal of the Transportation Research Board,1996,1566(-1):1-7
[87]Taori S., Rathi A.K. Comparison of NETSIM, NETFLO I, and NETFLO II traffic simulation models for fixed-time signal control [J]. Transportation Research Record:Journal of the Transportation Research Board,1996, 1566(-1):20-30
[88]Wang Y., Prevedouros P.D. Comparison of INTEGRATION, TSIS/CORSIM, and WATSim in replicating volumes and speeds on three small networks [J]. Transportation Research Record:Journal of the Transportation Research Board,1998,1644(-1):80-92
[89]Middleton M.D., Transportation T.D.o., Institute T.T. Evaluation of Simulation Models for Congested Dallas Freeways[M]. Texas Transportation Institute, Texas A & M University System,1999
[90]Prevedouros P.D., Wang Y. Simulation of large freeway and arterial network with CORSIM, INTEGRATION, and WATSim [J]. Transportation Research Record:Journal of the Transportation Research Board,1999,1678(-1): 197-207
[91]Bloomberg L., Dale J. A comparison of the VISSIM and CorSim traffic simulation Models,2000.3-16
[92]Boxill S.A., Yu L., Training T.S.U.C.f.T., Research, Center S.R.U.T. An evaluation of traffic simulation models for supporting ITS development[M]. Center for Transportation Training and Research, Texas Southern University, 2000
[93]Prevedouros P.D., Li H. Comparison of Freeway Simulation with INTEGRATION, KRONOS, and KWaves,2000.96C107
[94]Barrios E., Ridgway M., Choa F. The Best Simulation Tool for Bus Operations,2001.
[95]Trueblood M., Engineer P.E.T. Should I Use CORSIM or SimTraffie? [J]. HDR Engineering Inc,
[96]Choa F., Milam R.T., Stanek D. CORSIM, PARAMICS, and VISSIM:What the Manuals Never Told You,2004.
[97]Kosman K.P., Hallmark S.L., Poska S., Planner T. Evaluation of Simulation Models for Project-Level Emissions Modeling,2003.
[98]Jones S.L., Civil U.o.A.a.B.D.o., Engineering E., Alabama U.T.C.f., Program U.T.C. Traffic simulation software comparison study[M]. Citeseer,2004
[99]Panwai S., Dia H. Comparative evaluation of microscopic car-following behavior [J]. Intelligent Transportation Systems, IEEE Transactions on,2005, 6(3):314-325
[100]Xiao H., Ambadipudi R., Hourdakis J., Michalopoulos P. Methodology for selecting microscopic simulators:Comparative evaluation of AIMSUN and VISSIM [J]. Methodology,2005:6
[101]Hadi M., Sinha P., Wang A. Modeling Reductions in Freeway Capacity due to Incidents in Microscopic Simulation Models [J]. Transportation Research Record:Journal of the Transportation Research Board,2007,1999(-1):62-68
[102]Robertson D.I., TRANSYT. TRAFFIC NETWORK STUDY TOOL.1968.
[103]Chiou S.-W. An efficient algorithm for computing traffic equilibria using TRANSYT model [J]. Applied Mathematical Modelling,2010,34(11): 3390-3399
[104]Bourrel E., Lesort J. Mixing micro and macro representations of traffic flow: a hybrid model based on the LWR theory [J]. Transportation Research Board. Washington DC,2003
[105]Bottom J., Ben-Akiva M., Bierlaire M., Chabini I., Koutsopoulos H., Yang Q. Investigation of route guidance generation issues by simulation with DynaMIT,1999.
[106]Rakha H., Hellinga B., Van Aerde M., Perez W. Systematic verification, validation and calibration of traffic simulation models,1996.
[107]Hellinga B.R. Requirements for the calibration of traffic simulation models [J]. Proceedings of the Canadian Society for Civil Engineering,1998,4: 211-222
[108]Dixit A.K., Skeath S., Reiley D.H. Games of strategy[M]. WW Norton,2004
[109]Fudenberg D., Tirole J., Game theory.1991.1991, MIT Press.
[110]Zhang B., Fang S.-C. A SELFISH ROUTING BASED NETWORK IMPROVEMENT PROBLEM [J]. Journal of Systems Science & Complexity, 2011,24(1):68-78
[111]Anshelevich E., Ukkusuri S. Equilibria in dynamic selfish routing [J]. Algorithmic Game Theory,2009:171-182
[112]Iryo T. On the Existence of Pure Nash Equilibrium in Dynamic Traffic Assignments [J].2010
[113]Fotakis D., Kontogiannis S., Koutsoupias E., Mavronicolas M., Spirakis P. The structure and complexity of Nash equilibria for a selfish routing game [J]. Theoretical Computer Science,2009,410(36):3305-3326
[114]Haurie A., Marcotte P. On the relationship between NashiaCournot and Wardrop equilibria [J]. Networks,1985,15(3):295-308
[115]Roughgarden T. Stackelberg scheduling strategies,2001.104-113
[116]Arnott R., de Palma A., Lindsey R. Does providing information to drivers reduce traffic congestion? [J]. Transport Res A-Pol,1991,25(5):309-318
[117]Chorus C, Molin E., van Wee B. Travel information as an instrument to change car drivers travel choices [J]. EJTIR,2006,6(4):335-364
[118]Abdalla M., Abdel-Aty M. Modeling travel time under atis using mixed linear models [J]. Transportation,2006,33(1):63-82
[119]Jayakrishnan R., Mahmassani H. Dynamic simulation-assignment methodology to evaluate in-vehicle information strategies in urban traffic networks,2002.763-769
[120]Stern Zila E., Spharadi Einar Z. Congestion-related information and road network performance [J]. J Transport Geogr,1996,4(3):169-178
[121]Chen X., Zhan F.B. Agent-based modelling and simulation of urban evacuation:relative effectiveness of simultaneous and staged evacuation strategies [J]. J Oper Res Soc,2008,59(1):25-33
[122]Feng S.-m., Gao H., Guo C.-x. Evaluation of structural types of urban road network [J]. Journal of the Harbin Institute of Technology,2007,39(10): 1610-13
[123]Zhang P., Wu Y.-H., Liu L.-P., Yu R.-H. Evaluation of structural types of urban road tunnel network for city center district in China [J]. Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of Science and Technology (Natural Science Edition),2011,12(4):361-366
[124]He G, Shumin F., Caixiang G Research On Selection Method Of Urban Road Network Structure Form [C].2010 WASE International Conference on Information Engineering (ICIE 2010),14-15 Aug.2010. Los Alamitos, CA, USA,2010.360-4
[125]Altman E., Basar T., Jimenez T., Shimkin N. Routing into two parallel links: Game-theoretic distributed algorithms [J]. Journal of Parallel and Distributed Computing,2001,61(9):1367-1381
[126]Fotakis D., Kontogiannis S., Koutsoupias E., Mavronicolas M., Spirakis P. The structure and complexity of Nash equilibria for a selfish routing game [J]. Automata, Languages and Programming,2002:785-785
[127]Economides A.A., Silvester J.A. Multi-objective routing in integrated services networks:A game theory approach,1991.1220-1227 vol. 3
[128]GAO. Surface Transportation:Efforts to address highway congestion through real-time traffic information systems are expanding but face implementation challenges [J]. GAO-10-121R (Washington, D.C.:November 2009),2009
[129]Friesz T.L., Shah S. An overview of nontraditional formulations of static and dynamic equilibrium network design [J]. Transportation Research Part B-Methodological,2001,35(1):5-21
[130]Altman E., Wynter L. Equilibrium, games, and pricing in transportation and telecommunication networks [J]. Networks & Spatial Economics,2004,4(1): 7-21
[131]Cho H.J., Hwang M.C. Day-to-day vehicular flow dynamics in intelligent transportation network [J]. Mathematical and Computer Modelling,2005, 41(4-5):501-522
[132]Deng X.D., Wu X.C., Xu J.M. New Multiclass Traffic Equilibrium Assignment Model with Similarity Measure[M].2009
[133]Pappalardo M., Passacantando M., Equilibrium concepts in transportation networks:Generalized wardrop conditions and variational formulations [M] //P. Daniele, F. Giannessi, and A. Maugeri. Equilibrium Problems and Variational Models.2003:333-346
[134]李德仁,李清泉,杨必胜,余建伟.3S技术与智能交通[J].武汉大学学报(信息科学版),2008,4(33)：331-336
[135]Pohl C. Review article Multisensor image fusion in remote sensing:concepts, methods and applications [J]. International journal of remote sensing,1998, 19(5):823-854
[136]Ehlers M. Multi-image fusion in remote sensing:spatial enhancement vs. spectral characteristics preservation [J]. Advances in Visual Computing,2008: 75-84
[137]Song Y.-c., Zhang Y.-y., Meng H.-d. Research based on Euclid distance with weights of clustering method [J]. Computer Engineering and Applications, 2007,43(Copyright 2007, The Institution of Engineering and Technology): 179-226
[138]Chuanqiang Y., Xiaosong G., An Z., Xingjie P. An Improvement Algorithm of Principal Component Analysis,2007.2-529-2-534
[139]Anastassopoulos V. Fusion and super-resolution in multispectral data using neural networks for improved RGB representation [J]. Imaging Science Journal, The,2005,53(2):83-94
[140]Yanchang Z., Junde S. A general framework for clustering high-dimensional datasets,2003.1091-1094 vol. 2
[141]SHIFTING L.T.C.H. Contrast entropy based image enhancement and logarithmic transform coefficient histogram shifting [J].
[142]Shao F., Deng W., Zhang B. Traffic Information Management and Promulgating System Based on GIS [C].2010 International Conference on Optoelectronics and Image Processing (ICOIP 2010),11-12 Nov.2010. Los Alamitos, CA, USA,2010.676-9
[143]Zhang J., Xu Z., Wang Y An intelligent traffic information service system based on agent and GIS-T [C].2010 International Conference on Mechanic Automation and Control Engineering, MACE2010, June 26,2010-June 28, 2010. Wuhan, China,2010.2791-2794
[144]Levin E., Kupiec S.A., Forrester T., DeBacker T.A., Jannson T.P. GIS-based UAV real-time path planning and navigation [C]. Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Defense and Law Enforcement,1-5 April 2002. USA,2002.296-303
[145]Haowei S., Wei Z. The dynamic traffic information collection and processing methods of ITS common information platform based on floating car technology [C].2008 International Seminar on Future BioMedical Information Engineering (FBIE 2008),18 Dec.2008. Piscataway, NJ, USA, 2008.156-9
[146]Li Q., Yang B., Zheng N. An integrated spatio-temporal data model for GIS-transportation and related applications [J]. Geomatics and Information Science of Wuhan University,2007,32(Compendex):1034-1041
[147]Silva J.P., Santos M.Y. Spatiotemporal database models and languages for moving objects:A review [C].5th Iberian Conference on Information Systems and Technologies, CISTI 2010, June 16,2010-June 19,2010. Santiago de Compostela, Spain,2010.
[148]H gerstrand T. What about people in regional science? [J]. Papers in Regional Science,1970,24(1):6-21
[149]佘江峰,冯学智,都金康.时空数据模型的研究进展评述[J].南京大学学报：自然科学版,2005,41(003)：259-267
[150]Abraham R., Marsden J.E., Ra iu T.S. Manifolds, tensor analysis, and applications[M]. Springer,1988
[151]Egenhofer M.J., Herring J. A mathematical framework for the definition of topological relationships,1990.803-813
[2]Basu D., Maitra B. Stated Preference Approach for Valuation of Travel Time Displayed as Traffic Information on a VMS Board [J]. Journal of Urban Planning and Development-Asce,2010,136(3):214-224
[3]Arnott R., de Pablma A., Lindsey R. Does providing information to drivers reduce traffic congestion? [J]. Transportation Research Part A:General,1991, 25(5):309-318
[4]Jayakrishnan R., McNally M., Cohen M., Center U.o.C.T. Simulation of Advanced Traveller Information Systems (ATIS) Strategies to Reduce Non-recurring Congestion from Special Events[M]. University of California, Institute of Transportation Studies,1993
[5]Wardrop J. G. [J]. Proceedings of the Institution of Civil Engineers Ⅱ,1952,1: 325
[6]Jarasuniene A., Yatskiv I., Kaunas Univ T. Analysis of improvement of road safety using intelligent transport systems (ITS)[M].2007
[7]Abdel-Aty M.A., Kitamura R., Jovanis P.P. Using stated preference data for studying the effect of advanced traffic information on drivers' route choice [J]. Transportation Research Part C:Emerging Technologies,1997,5(1):39-50
[8]Martin P.T. TRAVELER INFORMATION SYSTEMS [J].2005
[9]McMurchie L., Ebeling C. PathFinder:a negotiation-based performance-driven router for FPGAs,1995.111-117
[10]Saricks C.L., Schofer J.L., S t S., Belella P.A. Evaluating effectiveness of real-time advanced traveler information systems using a small test vehicle fleet [J]. Transportation Research Record:Journal of the Transportation Research Board,1997,1588(-1):41-48
[11]Inman V., Safety U.S.F.H.A.O.o., Research T.O., Development, Center T.-F.H.R. TravTek Evaluation, Yoked Driver Study[M]. US Dept. of Transportation, Federal Highway Administration,1995
[12]Senn L. SWIFT ATIS:consumer reactions and marketability,126/1-126/5 vol. 2
[13]READER N.T.O. Colorado Mayday [J].1998
[14]杨兆升,胡坚明,温慧敏.城市交通流诱导系统实施框架研究[J].ITS通讯,2001：18-24
[15]Jayakrishnan R., McNally M.G, Cohen M.I., Center U.o.C.T., University of California I.D.o.C.E., University of California I.I.o.T.S. Simulation of Advanced Traveller Information Systems (ATIS) Strategies to Reduce Non-recurring Congestion from Special Events[M]. University of California, Institute of Transportation Studies,1993
[16]Uno N., Iida Y., Kawaratani S. Effects of dynamic information system on travel time reliability of road network [C].3rd International Conference on Traffic and Transportation Studies, ICTTS 2002, July 23,2002-July 25, 2002. Guilin, China,2002.911-918
[17]Rickert M., Nagel K. Dynamic traffic assignment on parallel computers in TRANSIMS [J]. Future generation computer systems,2001,17(5):637-648
[18]Liu T., Huang H., Tian L. Microscopic simulation of multi-lane traffic under dynamic tolling and information feedback [J]. Journal of Central South University of Technology,2009,16(5):865-870
[19]Emmerink R.H.M., Verhoef E.T., Nijkamp P., Rietveld P. Information provision in road transport with elastic demand:A welfare economic approach [J]. Journal of Transport Economics and Policy,1996:117-136
[20]Ben-Elia E., Ere v I., Shiftan Y. The combined effect of information and experience on drivers' route-choice behavior [J]. Transportation,2008,35(2): 165-177
[21]Enge lson L. On dynamics of traffic queues in a road network with route choice based on real time traffic information [J]. Transportation Research Part C:Emerging Technologies,2003,11(2):161-183
[22]Hall R. Route choice and advanced traveler information systems on a capacitated and dynamic network [J]. Transportation Research Part C: Emerging Technologies,1996,4(5):289-306
[23]Wahle J., Bazzan A., Klugl F., Schreckenberg M. The impact of real-time information in a two-route scenario using agent-based simulation [J]. Transport Res C-Emer,2002,10(5-6):399-417
[24]Chootinan P., Wong S.C., Chen A. A reliability-based network design problem [J]. Journal of Advanced Transportation,2005,39(3):247-270
[25]Abdalla M.F., Abdel-Aty M. Modeling Travel Time Under ATIS Using Mixed Linear Models [J]. Transportation,2006,33(1):63-82
[26]Ziegelmeyer A., Koessler F., My K., Denant-Boemont L. Road Traffic Congestion and Public Information:An Experimental Investigation [J]. J Transp Econ Policy,2008,42(1):43-82
[27]Davis L. Realizing Wardrop equilibria with real-time traffic information [J]. Physica A:Statistical Mechanics and its Applications,2009,388(20): 4459.4474
[28]GAO. Surface Transportation:Efforts to address highway congestion through real-time traffic information systems are expanding but face implementation challenges. [J]. GAO-10-121R (Washington, D.C.:November 2009),2009
[29]Ge Y.E., Zhang H.M., Lam W.H.K. Network reserve capacity under influence of traveler information [J]. Journal of Transportation Engineering,2003, 129(Compendex):262-270
[30]Chorus C.G, Molin E.J.E., Van Wee B. Use and Effects of Advanced Traveller Information Services (ATIS):A Review of the Literature [J]. Transport Reviews:A Transnational Trans disciplinary Journal,2006,26(2): 127-149
[31]Chorus C.G, Arentze T.A., Timmermans H.J.P. Information impact on quality of multimodal travel choices:conceptualizations and empirical analyses [J]. Transportation,2007,34(6):625-645
[32]Davis L.C. Realizing Wardrop equilibria with real-time traffic information [J]. Physica a-Statistical Mechanics and Its Applications,2009,388(20): 4459.4474
[33]Ben-Akiva M., De Palma A. Dynamic network models and driver information systems [J]. Transport Res A-Pol,1991,25(5):251-266
[34]Stern E., Sinuany-Stern Z., Spharadi Z., Holm E. Congestion-related information and road network performance [J]. Journal of Transport Geography,1996,4(3):169-178
[35]Selten R., Chmura T., Pitz T., Kube S., Schreckenberg M. Commuters route choice behaviour [J]. Game Econ Behav,2007,58(2):394-406
[36]Jayakrishnan R., Mahmassani H.S. Dynamic simulation-assignment methodology to evaluate in-vehicle information strategies in urban traffic networks [C].1990 Winter Simulation Conference Proceedings (Cat. No.90CH2926-4),9-12 Dec.1990. New York, NY, USA,1990.763-9
[37]Hall R. Route choice and advanced traveler information systems on a capacitated and dynamic network [J]. Transport Res C-Emer,1996,4(5): 289-306
[38]Faghri A. Trip generation analysis by artificial neural networks [C]. Microcomputer in Transportation. Proceedings of the 4th International Conference,22-24 July 1992. New York, NY, USA,1993.805-15
[39]Zhenghong P., Wenting D., Junfeng X. Research on trip-generation forecasting model based on neural networks and genetic algorithms [C].2010 International Conference on Mechanic Automation and Control Engineering (MACE),26-28 June 2010. Piscataway, NJ, USA,2010.4 pp.
[40]Dey S.S., Fricker J.D. Bayesian updating of trip generation data:combining national trip generation rates with local data [J]. Transportation,1994, 21(Compendex):393-403
[41]Spiess H. A maximum likelihood model for estimating origin-destination matrices [J]. Transportation Research Part B:Methodological,1987,21(5): 395-412
[42]Shou-Ren H., Chi-Bang C. Dynamic estimation of freeway origin-destination demand and travel time using extended Kalman filtering algorithm [C].2004 IEEE International Conference on Networking, Sensing and Control,21-23 March 2004. Piscataway, NJ, USA,2004.1329-34
[43]Li J., Lin B., Sun Z., Geng X. An estimation model of time-varying origin-destination flows in expressway corridors based on unscented Kalman filter [J]. Science in China Series E:Technological Sciences,2009, 52(Copyright 2009, The Institution of Engineering and Technology):2069-78
[44]Agents A. Multi-Agent Systems [J]. New York, NY, USA. ACM:394"C401
[45]Kittock J.E. Emergent conventions and the structure of multi-agent systems, 1993.
[46]Luck M. Guest editorial:Challenges for agent-based computing [J]. Autonomous Agents and Multi-Agent Systems,2004,9(3):199-201
[47]Chen B., Cheng H.H. A review of the applications of agent technology in traffic and transportation systems [J]. Intelligent Transportation Systems, IEEE Transactions on,2010,11(2):485-497
[48]Garcia-Serrano A., Teruel Vioque D., Carbone F., M"|ndez V. FIPA-compliant MAS development for road traffic management with a knowledge-based approach:the TRACK-R agents,2003.
[49]Tom(?)s V.R., Garc"aa L.A. Agent-based management of non urban road meteorological incidents [J]. Multi-Agent Systems and Applications IV,2005: 213-222
[50]Chen B., Cheng H.H., Palen J. Mobile(?)C:a mobile agent platform for mobile C/C++ agents [J]. Software:Practice and Experience,2006,36(15): 1711-1733
[51]Hern"(?)ndez J.Z., Ossowski S., Garc"aa-Serrano A. Multiagent architectures for intelligent traffic management systems [J]. Transportation Research Part C:Emerging Technologies,2002,10(5):473-506
[52]Wang F.Y. Agent-based control for networked traffic management systems [J]. Intelligent Systems, IEEE,2005,20(5):92-96
[53]Katwijk R.T., Koningsbruggen P., Schutter B., Hellendoorn J. A test bed for multi-agent control systems in road traffic management [J]. Applications of Agent Technology in Traffic and Transportation,2005:113-131
[54]Logi F., Ritchie S.G A multi-agent architecture for cooperative inter-jurisdictional traffic congestion management [J]. Transportation Research Part C:Emerging Technologies,2002,10(5-6):507-527
[55]Roozemond D.A. Using autonomous intelligent agents for urban traffic control systems,1999.
[56]Guo D., Li Z., Song J., Zhang Y. A study on the framework of urban traffic control system,842-846 vol. 1
[57]Choy M.C., Srinivasan D., Cheu R.L. Cooperative, hybrid agent architecture for real-time traffic signal control [J]. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on,2003,33(5):597-607
[58]Zhao J., Bukkapatnam S., Dessouky M.M. Distributed architecture for real-time coordination of bus holding in transit networks [J]. Intelligent Transportation Systems, IEEE Transactions on,2003,4(1):43-51
[59]Zhang H.S., Zhang Y., Li Z.H., Hu D.C. Spatial-temporal traffic data analysis based on global data management using MAS [J]. Intelligent Transportation Systems, IEEE Transactions on,2004,5(4):267-275
[60]Ossowski S., Cuena J., Garc"aa-Serrano A. A case of multiagent decision support:using autonomous agents for urban traffic control [J]. Progress in Artific ial Inte lligence(?)aIBERAMIA 98,1998:468-469
[61]Srinivasan D., Choy M. Cooperative multi-agent system for coordinated traffic signal control [J].2006
[62]Wang F.Y. Agent-based control for fuzzy behavior programming in robotic excavation [J]. Fuzzy Systems, IEEE Transactions on,2004,12(4):540-548
[63]Chen R.S., Chen D.K., Lin S.Y. Actam:Cooperative multi-agent system architecture for urban traffic signal control [J]. IEICE transactions on information and systems,2005,88(1):119-126
[64]Kosonen I. Multi-agent fuzzy signal control based on real-time simulation [J]. Transportation Research Part C:Emerging Technologies,2003,11(5): 389-403
[65]Moreno A., Valls A., Viejo A., Inform(?)tica U.R.i.V.D.d.E. Using JADE-LEAP implement agents in mobile devices[M]. Univers itat Rovira i Virgili. Departament d'Enginyeria Inforrm(?)tica,2003
[66]Seow K.T., Dang N.H., Lee D.H. A collaborative multiagent taxi-dispatch system [J]. Automation Science and Engineering, IEEE Transactions on, 2010,7(3):607-616
[67]Ossowski S., Hern(?)ndez J.Z., Belmonte M.V., Fer(?)ndez A., Garc"aa-Serrano A., P"|rez-de-la-Cruz J.L., Serrano J.M., Triguero F. Decision support for traffic management based on organisational and communicative multiagent abstractions [J]. Transportation Research Part C: Emerging Technologies,2005,13(4):272-298
[68]Balbo F., Pinson S. Toward a multi-agent modelling approach for urban public transportation systems [J]. Engineering societies in the agents world II, 2001:160-174
[69]Balbo F., Pinson S. Dynamic modeling of a disturbance in a multi-agent system for traffic regulation [J]. Decision Support Systems,2005,41(1): 131-146
[70]Burmeister B., Doormann J., Matylis G Agent-oriented traffic simulation [J]. Trans. Soc. Comput. SimuL Int.,1997,14(2):79-86
[71]Fischer K., Chaib-draa B., Muller J.P., Pischel M., Gerber C. A simulation approach based on negotiation and cooperation between agents:a case study [J]. Systems, Man, and Cybernetics, Part C:Applications and Reviews, IEEE Transactions on,1999,29(4):531-545
[72]Rossetti R.J.F., Bordini R.H., Bazzan A.L.C., Bampi S., Liu R., Vliet D.V. Using BDI agents to improve driver modelling in a commuter scenario [J]. Transportation Research Part C:Emerging Technologies,2002,10(5-6): 373-398
[73]Panwai S., Dia H. Neural agent car-following models [J]. Intelligent Transportation Systems, IEEE Transactions on,2007,8(1):60-70
[74]Wahle J., Bazzan A.L.C., Kl(?)gl F., Schreckenberg M. The impact of real-time information in a two-route scenario using agent-based simulation [J]. Transportation Research Part C:Emerging Technologies,2002,10(5-6): 399-417
[75]Adler J.L., Satapathy G., Manikonda V., Bowles B., Blue V.J. A multi-agent approach to cooperative traffic management and route guidance [J]. Transportation Research Part B:Methodological,2005,39(4):297-318
[76]Sukthankar R., Baluja S., Hancock J. Multiple adaptive agents for tactical driving [J]. Applied Intelligence,1998,9(1):7-23
[77]Hidas P. Modelling vehicle interactions in microscopic simulation of merging and weaving [J]. Transportation Research Part C:Emerging Technologies, 2005,13(1):37-62
[78]Castro da Silva B., Bazzan A., Andriotti G., Lopes F., de Oliveira D. ITSUMO:an intelligent transportation system for urban mobility [J]. Innovative Internet Community Systems,2006:224-235
[79]Li M., Hallam J., Pryor L., Chan S., Chong K. A cooperative intelligent system for urban traffic problems,1996.162-167
[80]Liu Z., Ishida T., Sheng H. Multiagent-based demand bus simulation for Shanghai [J]. Massively Multi-Agent Systems I,2005:309-322
[81]Kukla R., Kerridge J., Willis A., Hine J. PEDFLOW:Development of an autonomous agent model of pedestrian flow [J]. Transportation Research Record:Journal of the Transportation Research Board,2001,1774(-1):11-17
[82]Gambardella L.M., Rizzoli A.E., Funk P. Agent-based planning and simulation of combined rail/road transport [J]. Simulation,2002,78(5):293
[83]Weigang L., Dib M.V.P., de Melo A.C.M. Method to balance the communication among multi-agents in real time traffic synchronization [J]. Fuzzy Systems and Knowledge Discovery,2005:1053-1062
[84]Hall"| S., Chaib-draa B. A collaborative driving system based on multiagent modelling and simulations [J]. Transportation Research Part C:Emerging Technologies,2005,13(4):320-345
[85]Rossetti R.J.F., Ferreira P.A.F., Braga RA.M., Oliveira E.C. Towards an artificial traffic control system,2008.14-19
[86]Rakha H.A., Van Aerde M.W. Comparison of simulation modules of TRANS YT and INTEGRATION models [J]. Transportation Research Record: Journal of the Transportation Research Board,1996,1566(-1):1-7
[87]Taori S., Rathi A.K. Comparison of NETSIM, NETFLO I, and NETFLO II traffic simulation models for fixed-time signal control [J]. Transportation Research Record:Journal of the Transportation Research Board,1996, 1566(-1):20-30
[88]Wang Y., Prevedouros P.D. Comparison of INTEGRATION, TSIS/CORSIM, and WATSim in replicating volumes and speeds on three small networks [J]. Transportation Research Record:Journal of the Transportation Research Board,1998,1644(-1):80-92
[89]Middleton M.D., Transportation T.D.o., Institute T.T. Evaluation of Simulation Models for Congested Dallas Freeways[M]. Texas Transportation Institute, Texas A & M University System,1999
[90]Prevedouros P.D., Wang Y. Simulation of large freeway and arterial network with CORSIM, INTEGRATION, and WATSim [J]. Transportation Research Record:Journal of the Transportation Research Board,1999,1678(-1): 197-207
[91]Bloomberg L., Dale J. A comparison of the VISSIM and CorSim traffic simulation Models,2000.3-16
[92]Boxill S.A., Yu L., Training T.S.U.C.f.T., Research, Center S.R.U.T. An evaluation of traffic simulation models for supporting ITS development[M]. Center for Transportation Training and Research, Texas Southern University, 2000
[93]Prevedouros P.D., Li H. Comparison of Freeway Simulation with INTEGRATION, KRONOS, and KWaves,2000.96C107
[94]Barrios E., Ridgway M., Choa F. The Best Simulation Tool for Bus Operations,2001.
[95]Trueblood M., Engineer P.E.T. Should I Use CORSIM or SimTraffie? [J]. HDR Engineering Inc,
[96]Choa F., Milam R.T., Stanek D. CORSIM, PARAMICS, and VISSIM:What the Manuals Never Told You,2004.
[97]Kosman K.P., Hallmark S.L., Poska S., Planner T. Evaluation of Simulation Models for Project-Level Emissions Modeling,2003.
[98]Jones S.L., Civil U.o.A.a.B.D.o., Engineering E., Alabama U.T.C.f., Program U.T.C. Traffic simulation software comparison study[M]. Citeseer,2004
[99]Panwai S., Dia H. Comparative evaluation of microscopic car-following behavior [J]. Intelligent Transportation Systems, IEEE Transactions on,2005, 6(3):314-325
[100]Xiao H., Ambadipudi R., Hourdakis J., Michalopoulos P. Methodology for selecting microscopic simulators:Comparative evaluation of AIMSUN and VISSIM [J]. Methodology,2005:6
[101]Hadi M., Sinha P., Wang A. Modeling Reductions in Freeway Capacity due to Incidents in Microscopic Simulation Models [J]. Transportation Research Record:Journal of the Transportation Research Board,2007,1999(-1):62-68
[102]Robertson D.I., TRANSYT. TRAFFIC NETWORK STUDY TOOL.1968.
[103]Chiou S.-W. An efficient algorithm for computing traffic equilibria using TRANSYT model [J]. Applied Mathematical Modelling,2010,34(11): 3390-3399
[104]Bourrel E., Lesort J. Mixing micro and macro representations of traffic flow: a hybrid model based on the LWR theory [J]. Transportation Research Board. Washington DC,2003
[105]Bottom J., Ben-Akiva M., Bierlaire M., Chabini I., Koutsopoulos H., Yang Q. Investigation of route guidance generation issues by simulation with DynaMIT,1999.
[106]Rakha H., Hellinga B., Van Aerde M., Perez W. Systematic verification, validation and calibration of traffic simulation models,1996.
[107]Hellinga B.R. Requirements for the calibration of traffic simulation models [J]. Proceedings of the Canadian Society for Civil Engineering,1998,4: 211-222
[108]Dixit A.K., Skeath S., Reiley D.H. Games of strategy[M]. WW Norton,2004
[109]Fudenberg D., Tirole J., Game theory.1991.1991, MIT Press.
[110]Zhang B., Fang S.-C. A SELFISH ROUTING BASED NETWORK IMPROVEMENT PROBLEM [J]. Journal of Systems Science & Complexity, 2011,24(1):68-78
[111]Anshelevich E., Ukkusuri S. Equilibria in dynamic selfish routing [J]. Algorithmic Game Theory,2009:171-182
[112]Iryo T. On the Existence of Pure Nash Equilibrium in Dynamic Traffic Assignments [J].2010
[113]Fotakis D., Kontogiannis S., Koutsoupias E., Mavronicolas M., Spirakis P. The structure and complexity of Nash equilibria for a selfish routing game [J]. Theoretical Computer Science,2009,410(36):3305-3326
[114]Haurie A., Marcotte P. On the relationship between NashiaCournot and Wardrop equilibria [J]. Networks,1985,15(3):295-308
[115]Roughgarden T. Stackelberg scheduling strategies,2001.104-113
[116]Arnott R., de Palma A., Lindsey R. Does providing information to drivers reduce traffic congestion? [J]. Transport Res A-Pol,1991,25(5):309-318
[117]Chorus C, Molin E., van Wee B. Travel information as an instrument to change car drivers travel choices [J]. EJTIR,2006,6(4):335-364
[118]Abdalla M., Abdel-Aty M. Modeling travel time under atis using mixed linear models [J]. Transportation,2006,33(1):63-82
[119]Jayakrishnan R., Mahmassani H. Dynamic simulation-assignment methodology to evaluate in-vehicle information strategies in urban traffic networks,2002.763-769
[120]Stern Zila E., Spharadi Einar Z. Congestion-related information and road network performance [J]. J Transport Geogr,1996,4(3):169-178
[121]Chen X., Zhan F.B. Agent-based modelling and simulation of urban evacuation:relative effectiveness of simultaneous and staged evacuation strategies [J]. J Oper Res Soc,2008,59(1):25-33
[122]Feng S.-m., Gao H., Guo C.-x. Evaluation of structural types of urban road network [J]. Journal of the Harbin Institute of Technology,2007,39(10): 1610-13
[123]Zhang P., Wu Y.-H., Liu L.-P., Yu R.-H. Evaluation of structural types of urban road tunnel network for city center district in China [J]. Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of Science and Technology (Natural Science Edition),2011,12(4):361-366
[124]He G, Shumin F., Caixiang G Research On Selection Method Of Urban Road Network Structure Form [C].2010 WASE International Conference on Information Engineering (ICIE 2010),14-15 Aug.2010. Los Alamitos, CA, USA,2010.360-4
[125]Altman E., Basar T., Jimenez T., Shimkin N. Routing into two parallel links: Game-theoretic distributed algorithms [J]. Journal of Parallel and Distributed Computing,2001,61(9):1367-1381
[126]Fotakis D., Kontogiannis S., Koutsoupias E., Mavronicolas M., Spirakis P. The structure and complexity of Nash equilibria for a selfish routing game [J]. Automata, Languages and Programming,2002:785-785
[127]Economides A.A., Silvester J.A. Multi-objective routing in integrated services networks:A game theory approach,1991.1220-1227 vol. 3
[128]GAO. Surface Transportation:Efforts to address highway congestion through real-time traffic information systems are expanding but face implementation challenges [J]. GAO-10-121R (Washington, D.C.:November 2009),2009
[129]Friesz T.L., Shah S. An overview of nontraditional formulations of static and dynamic equilibrium network design [J]. Transportation Research Part B-Methodological,2001,35(1):5-21
[130]Altman E., Wynter L. Equilibrium, games, and pricing in transportation and telecommunication networks [J]. Networks & Spatial Economics,2004,4(1): 7-21
[131]Cho H.J., Hwang M.C. Day-to-day vehicular flow dynamics in intelligent transportation network [J]. Mathematical and Computer Modelling,2005, 41(4-5):501-522
[132]Deng X.D., Wu X.C., Xu J.M. New Multiclass Traffic Equilibrium Assignment Model with Similarity Measure[M].2009
[133]Pappalardo M., Passacantando M., Equilibrium concepts in transportation networks:Generalized wardrop conditions and variational formulations [M] //P. Daniele, F. Giannessi, and A. Maugeri. Equilibrium Problems and Variational Models.2003:333-346
[134]李德仁,李清泉,杨必胜,余建伟.3S技术与智能交通[J].武汉大学学报(信息科学版),2008,4(33)：331-336
[135]Pohl C. Review article Multisensor image fusion in remote sensing:concepts, methods and applications [J]. International journal of remote sensing,1998, 19(5):823-854
[136]Ehlers M. Multi-image fusion in remote sensing:spatial enhancement vs. spectral characteristics preservation [J]. Advances in Visual Computing,2008: 75-84
[137]Song Y.-c., Zhang Y.-y., Meng H.-d. Research based on Euclid distance with weights of clustering method [J]. Computer Engineering and Applications, 2007,43(Copyright 2007, The Institution of Engineering and Technology): 179-226
[138]Chuanqiang Y., Xiaosong G., An Z., Xingjie P. An Improvement Algorithm of Principal Component Analysis,2007.2-529-2-534
[139]Anastassopoulos V. Fusion and super-resolution in multispectral data using neural networks for improved RGB representation [J]. Imaging Science Journal, The,2005,53(2):83-94
[140]Yanchang Z., Junde S. A general framework for clustering high-dimensional datasets,2003.1091-1094 vol. 2
[141]SHIFTING L.T.C.H. Contrast entropy based image enhancement and logarithmic transform coefficient histogram shifting [J].
[142]Shao F., Deng W., Zhang B. Traffic Information Management and Promulgating System Based on GIS [C].2010 International Conference on Optoelectronics and Image Processing (ICOIP 2010),11-12 Nov.2010. Los Alamitos, CA, USA,2010.676-9
[143]Zhang J., Xu Z., Wang Y An intelligent traffic information service system based on agent and GIS-T [C].2010 International Conference on Mechanic Automation and Control Engineering, MACE2010, June 26,2010-June 28, 2010. Wuhan, China,2010.2791-2794
[144]Levin E., Kupiec S.A., Forrester T., DeBacker T.A., Jannson T.P. GIS-based UAV real-time path planning and navigation [C]. Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Defense and Law Enforcement,1-5 April 2002. USA,2002.296-303
[145]Haowei S., Wei Z. The dynamic traffic information collection and processing methods of ITS common information platform based on floating car technology [C].2008 International Seminar on Future BioMedical Information Engineering (FBIE 2008),18 Dec.2008. Piscataway, NJ, USA, 2008.156-9
[146]Li Q., Yang B., Zheng N. An integrated spatio-temporal data model for GIS-transportation and related applications [J]. Geomatics and Information Science of Wuhan University,2007,32(Compendex):1034-1041
[147]Silva J.P., Santos M.Y. Spatiotemporal database models and languages for moving objects:A review [C].5th Iberian Conference on Information Systems and Technologies, CISTI 2010, June 16,2010-June 19,2010. Santiago de Compostela, Spain,2010.
[148]H gerstrand T. What about people in regional science? [J]. Papers in Regional Science,1970,24(1):6-21
[149]佘江峰,冯学智,都金康.时空数据模型的研究进展评述[J].南京大学学报：自然科学版,2005,41(003)：259-267
[150]Abraham R., Marsden J.E., Ra iu T.S. Manifolds, tensor analysis, and applications[M]. Springer,1988
[151]Egenhofer M.J., Herring J. A mathematical framework for the definition of topological relationships,1990.803-813