城市公交系统模型与算法研究
|
摘要
优先发展城市公共交通系统是解决大、中城市交通问题的最佳途径。近年来，城市公交系统的智能化已经成为公共交通领域研究的主要方向。本文以大连市公交系统为工程背景，研究了蚁群算法、SCE-UA算法以及支持向量机等智能算法在公共交通规划和调度中的应用。城市公交系统是一个巨系统，其相关的模型和方法都非常复杂，为此，本文引入了高性能计算来提高智能算法的优化质量和收敛速度。本文的主要内容如下：
1)公交线路网是城市公共交通系统的根本和基础，其设置得是否合理会直接影响乘客的出行时间、换乘以及系统运营成本。本文提出了以直达客流密度最大为目标的公交线网优化模型。该模型综合考虑了乘客和运营者双方的利益，通过一种模拟进化算法——蚁群算法，来优化该模型。为了提高蚁群算法的搜索效率，采用了综合考虑全局和局部信息的信息素增量更新策略：ANT-Weight策略和基于粗粒度模型的并行方案。以大连市主城区的公交数据对该模型和算法进行了检验，与大连市现状的公交网络相比，基于本文模型优化的公交网络的效率更高。另外，结果还表明ANT-Weight策略和粗粒度模型可以提高蚁群算法的效率。
2)发车频率的制定是公交系统日常运营工作的核心，它决定了运行时刻表、车辆调度以及分派司机等其它的日常调度工作。本文提出了一个双层规划模型来优化公交线路发车频率，该模型可以反映运营者(供给者)和出行者(需求者)之间的相互作用。其中，上层模型以整个公交系统总成本最小为目标，通过进化算法——SCE-UA算法来优化公交线路的发车频率；下层模型基于最优出行策略，进行公交客流分配。以大连市主城区的公交系统为计算实例分析了该双层模型及其解法的效率，结果表明，该双层模型可以有效地节省系统的总成本。
3)开发能准确预测公交车辆到站时间的方法，是提高公交车辆准时性，减少乘客等待时间，从而提高公交吸引力的重要手段。本文提出了一种基于支持向量机(SVM)和Kalman滤波的混合模型，来预测公交车辆到站时间。在该混合模型中，SVM模型基于历史数据预测路段的基线运行时间；基于Kalman滤波的动态算法结合基线时间和最新的车辆运行信息，预测车辆到达各站点的时间。以大连市开发区7路公交线路的数据对该方法进行了检验，实例分析表明，与SVM模型相比，该混合模型的稳定性和预测精度都较高。
4)实时调度策略就是利用先进的技术手段，动态地获取实时交通信息，实现对车辆的实时监控和调度。本文主要研究了实时调度中最常用的滞站调度策略，提出了两种滞
Preferential development of urban public transportation system is a universally accepted approach of solving urban traffic problem. Recently, intelligent public transportation system has become the important development trend of public transportation field. Based on the project background of public transportation system in Dalian city, the applications on ant colony optimization algorithm, support vector machine, SCE-UA algorithm, etc in the planning and the operation of urban public transportation system, are studied. Since urban public transportation system is a giant system, the corresponding models and strategies are very complex. In this dissertation, the parallel intelligent algorithms running in PC cluster are adoped to improve the optimization quality and speed. The major contents and research progress are as follows:
1) Bus network is the basis of urban public transportation. The rationality of the bus network, therefore, directly influences the travel time and transfer rate of passengers, and the overall running cost of the transport systems. This dissertation presents an optimization model for bus network design, which aims to maximize the number of direct travelers per unit length, i.e. direct traveler density, subject to route length and non-linear rate constraints. Ant colony optimization (ACO), a new evolution algorithm, is used to solve the model. To improve the efficiency of the method, two improved strategies are proposed: 1) develops a new strategy to update the increased pheromone, called Ant-Weight, which considering the global and local information; 2) uses parallelization strategies of ACO improve the calculation time and the quality of the optimization. The data of Dalian city in China are used to test the model and the algorithm. The results show that compared with the preseng transit network the optimized one is more effective and efficient. They also reveal that the ANT-Weight strategy and coarse-grain strategy are effective.
2) The optimization of the frequencies is the key of the operation plan, which determine the situation of the running schedule, vehicle adjustment and driver assignment. Considering the interactions between the operators (the demand) and the user (the supply), in this dissertation, a bi-level programming model for the bus frequencies design in the given network is presented. In the bi-level model, the upper-level is the leader and optimizes the bus frequencies by SCE-UA. The lower-level is the follower and its objective is to assign transit trips to the network based on travel strategy and the optimal frequencies. Finally the model and the algorithms are illustrated with the bus network in the city of Dalian in China. The results show that the model can effectively save the total cost of the operators and users
1)公交线路网是城市公共交通系统的根本和基础，其设置得是否合理会直接影响乘客的出行时间、换乘以及系统运营成本。本文提出了以直达客流密度最大为目标的公交线网优化模型。该模型综合考虑了乘客和运营者双方的利益，通过一种模拟进化算法——蚁群算法，来优化该模型。为了提高蚁群算法的搜索效率，采用了综合考虑全局和局部信息的信息素增量更新策略：ANT-Weight策略和基于粗粒度模型的并行方案。以大连市主城区的公交数据对该模型和算法进行了检验，与大连市现状的公交网络相比，基于本文模型优化的公交网络的效率更高。另外，结果还表明ANT-Weight策略和粗粒度模型可以提高蚁群算法的效率。
2)发车频率的制定是公交系统日常运营工作的核心，它决定了运行时刻表、车辆调度以及分派司机等其它的日常调度工作。本文提出了一个双层规划模型来优化公交线路发车频率，该模型可以反映运营者(供给者)和出行者(需求者)之间的相互作用。其中，上层模型以整个公交系统总成本最小为目标，通过进化算法——SCE-UA算法来优化公交线路的发车频率；下层模型基于最优出行策略，进行公交客流分配。以大连市主城区的公交系统为计算实例分析了该双层模型及其解法的效率，结果表明，该双层模型可以有效地节省系统的总成本。
3)开发能准确预测公交车辆到站时间的方法，是提高公交车辆准时性，减少乘客等待时间，从而提高公交吸引力的重要手段。本文提出了一种基于支持向量机(SVM)和Kalman滤波的混合模型，来预测公交车辆到站时间。在该混合模型中，SVM模型基于历史数据预测路段的基线运行时间；基于Kalman滤波的动态算法结合基线时间和最新的车辆运行信息，预测车辆到达各站点的时间。以大连市开发区7路公交线路的数据对该方法进行了检验，实例分析表明，与SVM模型相比，该混合模型的稳定性和预测精度都较高。
4)实时调度策略就是利用先进的技术手段，动态地获取实时交通信息，实现对车辆的实时监控和调度。本文主要研究了实时调度中最常用的滞站调度策略，提出了两种滞
Preferential development of urban public transportation system is a universally accepted approach of solving urban traffic problem. Recently, intelligent public transportation system has become the important development trend of public transportation field. Based on the project background of public transportation system in Dalian city, the applications on ant colony optimization algorithm, support vector machine, SCE-UA algorithm, etc in the planning and the operation of urban public transportation system, are studied. Since urban public transportation system is a giant system, the corresponding models and strategies are very complex. In this dissertation, the parallel intelligent algorithms running in PC cluster are adoped to improve the optimization quality and speed. The major contents and research progress are as follows:
1) Bus network is the basis of urban public transportation. The rationality of the bus network, therefore, directly influences the travel time and transfer rate of passengers, and the overall running cost of the transport systems. This dissertation presents an optimization model for bus network design, which aims to maximize the number of direct travelers per unit length, i.e. direct traveler density, subject to route length and non-linear rate constraints. Ant colony optimization (ACO), a new evolution algorithm, is used to solve the model. To improve the efficiency of the method, two improved strategies are proposed: 1) develops a new strategy to update the increased pheromone, called Ant-Weight, which considering the global and local information; 2) uses parallelization strategies of ACO improve the calculation time and the quality of the optimization. The data of Dalian city in China are used to test the model and the algorithm. The results show that compared with the preseng transit network the optimized one is more effective and efficient. They also reveal that the ANT-Weight strategy and coarse-grain strategy are effective.
2) The optimization of the frequencies is the key of the operation plan, which determine the situation of the running schedule, vehicle adjustment and driver assignment. Considering the interactions between the operators (the demand) and the user (the supply), in this dissertation, a bi-level programming model for the bus frequencies design in the given network is presented. In the bi-level model, the upper-level is the leader and optimizes the bus frequencies by SCE-UA. The lower-level is the follower and its objective is to assign transit trips to the network based on travel strategy and the optimal frequencies. Finally the model and the algorithms are illustrated with the bus network in the city of Dalian in China. The results show that the model can effectively save the total cost of the operators and users
引文
[1] 杨兆升，城市智能公共交通系统理论与方法．2004：中国铁道出版社．
[2] 蒋冰蕾，美国智能公共交通领域发展近况．城市公共交通，1999(1)：p．9-13．
[3] 陆化普，日本智能公共交通系统的开发应用现状与展望．城市公共交通，1999(1)：p．5-8．
[4] Desaulniers, G., Hickman,M.D., Public Transit, in Les Cahiers du GERAD. 2003. p. G-2003-77.
[5] 武长顺，城市交通领域中的系统工程．天津大学学报(社会科学版)，2005．7(4)：p．262-265．
[6] 刘振阳，张秀媛，徐进等，城市智能公共交通系统．2005：中国铁道出版社．
[7] 周尚明，关于现代工业自动化中的计算智能和信息处理问题的研究，in铁道部科学研究院博士后研究工作报告．1998．
[8] Bezdek, J.C., On the relationship between neural networks, pattern recognition and intelligence. International Journal of Approximate Reasoning 1992. 6(2): p. 85-107.
[9] 吴佑寿，世界计算智能大会简介．电子科技导报，1998(10)：p．37-38．
[10] 季厌浮，张．，计算智能技术及其应用．煤炭技术，2004．23(4)：p．112-113．
[11] Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R., Fuzzy Models and Algorithms for Pattern Recognition and Image Processing 2005: Springer-Verlag New York, Inc. Secaucus, NJ, USA
[12] Dubois, D., Bel, G. & Libre, M., A Set of Methods in Transportation Network Synthesis and Analysis. Journal of Operational Research Society, 1979. 30(9): p. 797-808.
[13] Ceder, A., Wilson, N. H. M., Bus network design. Transportation Research-B, 1986.20B(4): p. 331-44.
[14] Hasselstr6m, D., Public Transportation Planning-A Mathematical Programming Approach, in Doctoral Dissertation, University of Goteborg, Sweden. 1981.
[15] Baaj, M.H., Mahmassani, H.S., Hybrid Route Generation Heuristic Algorithm for the Design of Transit Networks. Transportation Research-C, 1995.3(1): p. 31-50.
[16] Guan, J.F., Yang, H., Wirasinghe, S.C. Determine network transit line configuration for optimal passenger line assignment, in Proceedings of the 2004 IEEE International Conference on Networking: Sensing & Control. 2004: p. 839-843.
[17] 王炜，杨新苗，陈学武等，城市公共交通系统规划方法与管理技术．2002：科学出版社．
[18] 单连龙，高自友，城市公交系统连续平衡网络设计的双层规划模型及求解算法．系统工程理论与实践，2000(7)：p．85-93．
[19] Steenbrink.P.A, Optimization of Transport Networks. 1974: Wiley, London.
[20] Chakroborty, P., Deb, K., Subrahmanyam, P. S., Optimal Scheduling of Urban Transit System Using Genetic Algorithms. Journal of Transportation Engineering, 1995. 121(6): p. 544-553.
[21] Dashora, M., Dhingra, S.L., Expert System for Bus Routing and Scheduling. Computers in urban planning and urban management, 1998.2: p. 841-853.
[22] Fernandez, R., An Expert System for the Preliminary Design and Location of High Capacity Bus Stops. Traffic Eng. Control, 1993. 34: p. 533-539.
[23] Senevirante, EN., Analysis of on Time Performance of Bus Services Using Simulation Journal of Transportation Engineering, 1990. 116(4): p. 517-531.
[24] Dorigo, M., Maniezzo, V., Colorni, A., The Ant System: Optimization by A Colony of Cooperating Agents. IEEE Transactions on Systems, Man and Cybernetics, 1996.1(26): p. 29-41.[25] 黄席樾，张著洪，何传江等，现代智能算法理论及应用．2005：科学出版社．
[26] Stutzle, T., Hoos,H.. MAX-MIN Ant System and Local Search for Combinatorial Optimization Problems. in S. Voβ, S. Martello, I.H. Osman, C. Roucairol (Eds.), Meta-heuristics: Advances and Trends in Local Search Paradigms for Optimization: Kluwer Academic, Boston, MA. 1999: p. 313-329.
[27] Gambardella, L.M., Dorigo, M., An Ant Colony System Hybridized With A New Local Search for The Sequential Ordering Problem. INFORMS Journal on Computing, 2000. 12(3): p. 237-255.
[28] Botee, H.M., Bonabeau E., Evolving Ant Colony Optimization. Complex Systems, 1998. 1(2): p. 149-159.
[29] 吴庆洪，张纪会，徐心和，具有变异特征的蚁群算法．计算机研究与发展，1999．36(10)：p．1240-1245．
[30] 陈岐，沈洁，秦玲，陈宏建，基于分布均匀度的自适应蚁群算法．软件学，2003．14(8)：p．1379-1387．
[31] 詹士昌，徐捷，吴俊，蚁群算法中有关算法参数的最优选择．科技通报，2003．19(5)：p．381-386．
[32] 康立山，谢云，尤矢勇，罗祖华，非数值并行算法-模拟退火算法．2000：科学出版社．
[33] Matsumura, T., Nakamura, M., Okech, J., Onaga, K., A parallel and distributed genetic algorithm on loosely-coupled multiprocessor systems. IEICE Trans. Fundam. Electron. Commun. Comput 1998. E81-A(4): p. 540-546.
[34] 苏)M*C*费舍里松，城市交通．1984：中国建筑工业出版社．
[35] Silman, L.A., Barzily, Z., Passy, U. , Planning the Route System for Urban Buses. Computers and Operations Research 1974(1): p. 210-211.
[36] Scheele, S., A Supply Model for Public Transit Services. Transportation Research-B, 1980. 14: p. 133-146.
[37] Furth, P.G., Wilson, N.H.M., Setting Frequencies on Bus Routes: Theory and Practice. Trans. Res. Record, 1982. 818: p. 1-7.
[38] Koutsopoulos, H.N., Odoni, A., Wilson, N.H.M. Determination of Headways as a Function of Time Varying Characteristics on a Transit Network. in Computer Scheduling of Public Transport 2, North-Holland, Amsterdam. 1985: p. 391-414.
[39] Lazar, N.S., Paul, M., Schonfeld, Method for Optimizing Transit Service Coverage,. Transportation Research Record 1993. 1402: p. 28-39.
[40] Shih, M.C., Mahmassani, H.S., Vehicle Sizing Model for Bus Transit Networks. Transportation Research Record, 1995. 1452: p. 35-41.
[41] 孙芙灵，公交调度中发车间隔的确定方法的探讨．西安公路交通大学学报，1997．17(2)：p．44q8．
[42] Gao, Z.Y., Sun, H.J., Shan, L.L., A continuous equilibrium network design model and algorithm for transit systems. Transportation Research part B, 2004. 38: p. 235-250.
[43] Wong, S.C., Yang, H., Reserve capacity of a signal-controlled road network. Transportation Research-B, 1997.30: p. 397-402.
[44] Spiess, H., Florian, M., Optimal Strategies: A New Assignment Model for Transit Networks. Transportation Research-B, 1998. 23(2): p. 83-102.
[45] 郭亚军，多属性综合评价．1996：东北大学出版社．
[46] Duan Q. Y., S.S., and Gupta V. K. , Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res., 1992.28(4): p. 1015-1031.
[47] Duan Q. Y., G., V. K., and Sorooshian, S., Shuffled complex evolution approach for effective and??efficient minimization. J Optim. Theory Appl., 1993.76(3): p. 501-521.
[48] Kuczera, G., Efficient subspace probabilistic parameter optimization for catchment models. Water Resources Research, 1997.33(1): p. 177-185.
[49] Ben-Akiva, M., Lerman, S.R., Discrete Choice Analysis: Theory and Application to Travel Demand. 1985: MIT Press, Cambridge, Mass.
[50] DeLurgio, S.A., Forecasting principles and applications. 1998: McGraw-Hill, New York. 1998,
[51] Stephanedes, Y.J., Kwon, E., Michalopoulos, P., On-Line Diversion Prediction for Dynamic Control And Vehicle Guidance In Freeway Corridors. Transportation Research Record, 1990. 1287: p. 11-19.
[52] Smith, B.L., Demetsky, M. J, Short-Term Traffic Flow Prediction:Neural Network Approach Transp. Res. Rec., 1995. 1453: p. 98-104.
[53] Dailey, D., Maclean, S., Cathey, F., Wall, Z., Transit Vehicle Arrival Prediction: Algorithm And Large-Scale Implementation,. Journal of the Transportation Research Board, 2001.1771: p. 46-51.
[54] Okutani, I., Stephanedes, Y. J., Dynamic Prediction of Traffic Volume through Kalman Filtering Theory. Transportation Research Record, 1984. 18B(1): p. 1-11.
[55] Shalaby, A., Farhan, A. Bus Travel Time Prediction Model for Dynamic Operations Control And Passenger Information Systems. in The 82nd Annual Meeting of the Transportation Research Board. Washington, DC. 2003.
[56] Park, D., Rilett, L. R, Forecasting Freeway Link Travel Times With A Multilayer Feedforward Neural Network Computer-Aided Civil and Infrastructure Engineering., 1999.14(5): p. 357-367.
[57] Ding, Y., Chien, S. The Prediction Of Bus Arrival Times With Link-Based Artificial Neural Networks. in Proceedings of the International Conference on Computational Intelligence & Neurosciences (CI&N)—Intelligent Transportation Systems. Atlantic City, New Jersey. 2000: p. 730-739.
[58] Chien, I.J., Ding, Y., Wei, C., Dynamic Bus Arrival Time Prediction With Artificial Neural Networks. Journal of Transportation Engineering, ASCE, 2002. 128(5): p. 429-438.
[59] Chen, M., Liu, X.B., Xia, J.X., Chien S.I., A Dynamic Bus-Arrival Time Prediction Model Based on APC Data. Computer-Aided Civil and Infrastructure Engineering, 2004. 19: p. 364-376.
[60] Lawrence, S., Giles, C.L., Tsoi, A.-C. Lessons In Neural Network Training: Overfitting May Be Harder Than Expected. in Proceedings of the fourteenth national conference on artificial intelligence. Mento Park: AAA1 Press. 1997: p. 540-545.
[61] Moody, J.E., The Effective Number Of Parameters: An Analysis Of Generalization And Regularization In Nonlinear Learning Systems. NIPS, 1992.4: p. 847-854.
[62] Sarle, W.S. Stopped Training And Other Remedies For Overfitting. in Proceedings of the twenty-seventh symposium on the interface of computing science and statistics. 1995: p. 352-360.
[63] Weigend, A. On Overfitting And The Effective Number Of Hidden Units, in Proceedings of the 1993 connectionist models summer school. 1994.
[64] Vapnik, V.N., The Nature of Statistical Learning Theory. 1995: Springer.
[65] Vapnik, V.N., An Overview Of Statistical Learning Theory. IEEE Transactions on Neural Networks, 1999. 10 (5): p. 988-999.
[66] Vapnik, V.N., The Nature of Statistical Learning Theory. Springer, New York, 2000.
[67] Vapnik, V.N., Statistical Learning Theory. Springer, 1998.
[68] Dong, B., Cao,C., Lee,S.E.; , Applying Support Vector Machines To Predict Building Energy??Consumption In Tropical Region. Energy and Buildings, 2005.37: p. 545-553.
[69] 王定成，方廷健，高理富等，支持向量机回归在线建模及应用．控制与决策，2003．18(1)：p．89-91．
[70] Cao, L.J., Francis, E.H., Tay, Support Vector Machine With Adaptive Parameters In Financial Time Series Forecasting, IEEE Transactions on Neural Networks, 2003.14(6).
[71] Grewal, M.S., Andrews, A. P., Kalman Filtering: Theory and Practice Using MAL TAB (2nd Ed.). 2001, New York John Wiley & Sons.
[72] 王田，车载组合导航系统误差修正方法研究 2002，国防科学技术大学；．
[73] Hsu, C.W., Chang, C.C., and Lin, C.J., A Practical Guide to Support Vector Classification, Technical report, in Department of Computer Science and Information Engineering. 2003, National Taiwan University.
[74] Turnquist, M.A., Strategies for improving reliability of bus transit service. Transportation Research Record 1978.818: p. 7-13.
[75] Turnquist, M.A., Blume, S.W., Evaluating potential effectiveness of headway control strategies for transit systems. Transportation Research Record 1980. 746: p. 25-29.
[76] Adamski, A. Optimal Dispatching Control of Bus Lines. in Proceedings of the 9th IFIP Conference on Optimization Techniques, Lecture Notes in Control and Information Science: Springer Verlag, New York. 1980: p. 334-344.
[77] Adamski, A. Optimal Adaptive Dispatching Control in an Integrated Public Transport Management System. in Proceedings of the Second Meeting of the EURO Working Group on Urban Traffic and Transportation: Paris. 1993: p. 913-938.
[78] Adamski, A. Real-time Computer-aided Control in Public Transport from the Point of View of Xchedule Reliability. in Lecture Notes in Economics and Mathematics Systems. 1995: p. 278-295.
[79] Eberlein, X.J., The Real-time Deadheading Problem in Transit Operations Control. transportation Research-B, 1998.32B: p. 77-100.
[80] Adebisi, O., A Mathematical Model for Headway Variance of Fixed Route Buses. Transportation Research-B, 1986.20B: p. 59-70.
[81] Adamski, A., Turnau, A. , Simulation Support Tool for Real-time Dispatching Control in Public Transport transportation Research-A, 1998 32: p. 73-87.
[82] Koffiman, D., A Simulation Study of Alternative Realtime Bus Headway Control Strategies. transportation Research Record, 1978 663 p. 41-46.
[83] Vanderbona, U., Richardson, A.J., Effect of Checkpoint Control Strategies in a Simulated Transit Operation transportation Research-A, 1986 20 p. 429-436.
[84] Barnett, A., On Controlling Randomness in Transit Operations Transportation Science 1974. 8: p. 102-116.
[85] Adamski, A. Expert System for dispatching control of the public transport vehicles at terminals, in Proc. International Conference TRANSSYSTEM-89. 1989.
[86] Eberlein, X.J., Wilson, N.H.M., Bernstein, D. Modeling real-time control strategies in public transit operations, in Computer Aided Transit Scheduling, Lecture Note in Economics and Mathematical Systems: Springer-Verlag, Berlin,. 1999: p. 325-346.
[87] Dessouky, M., Randolph Hall, R., Zhang, L., Singh, A., Real-time control of buses for schedule coordination at a terminal. Transportation Research Part A 2003. 37: p. 145-164.[88] Zolfaghari, S., Azizi, N., Jaber, M.Y., A model for holding strategy in public transit systems with real-time information. International Journal of Transport Management 2004. 2: p. 99-110.
[89] Fu, L.P., Liu, Q., Calamai, P., Real-Time Optimization Model for Dynamic Scheduling of Transit Operations. Transportation Research Record 2003. 1857.
[90] SUN, A., HICKMAN, M., The Real-Time Stop-Skipping Problem. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 2005.9(2): p. 91-109.
[91] Ceder, A., Designing Transit Short-turn Trips with the Elimination of Imbalanced Loads 1988: Computer-aided Transit Scheduling, Springer-Verlag.
[92] Furth, P.G., Short-turning on Transit Routes transportation Research Record, 1988 1108: p. 45-52.
[93] Chang, G.L., Adaptive Bus-preemption Control. transportation Research-C, 1996.5: p. 251-268.
[94] Cornewell, P.R., Dynamic Signal Co-ordination and Public Transport Priority IEE, Road Traffic Monitoring and Control, 1986: p. 158-161.
[95] Eberlein, X.J., Real-time Control Strategies in Transit Operations: Models and Analysis. 1995, Massachusetts Institute of Technology, Cambridge, MA.
[96] Abkowitz, M.D., Engelstein, I., Methods for maintaining transit service regularity. Transportation Research Record 1984. 961: p. 1-8.
[97] Lin, G., P. Liang, Schonfeid,P., Larson, R., Adaptive Control of Transit Operations. Final Report for Project MD-26-7002. 1995: University of Maryland.
[98] Abdulhai, B., Sheu, J.B., Recker, W.W., Simulation of ITS on the Irvine FOT area using the 'Paramics 1.5' scalable microscopic traffic simulator: Phase Ⅰ: Model calibration and validation. California PATH Research Report UCB-ITS-PRR-99-12, 1999. 12: p. 1055-1425.
[99] Sheu, J.B., Chou, Y.H., Chen, A., Stochastic modeling and real-time prediction of incident effects on surface street traffic congestion. Applied Mathematical Modelling, 2004. 28: p. 445-468.
[100] Holland, J.H., Adaptation in Natural and Artificial Systems.. 1975: University of Michigan Press, Ann Arbor, MI.
[101] 陈国良，王熙法，庄镇泉等，遗传算法及其应用 1996，北京 人民邮电出版社
[102] Harik, G., Paz E, C., Miller B. The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations. in Proc. of the 5th Infl Conf. on Evolution Computation: IEEE Press, New York. 1997: p. 7-12.
[103] 曾国荪，丁春玲，并行遗传算法分析．计算机工程，2001．27(9)：p．53-55．
[104] Beckers, M.L.M., Derks E., Melssen, W.J. et al Using Genetic Algorithms for Conformational Analysis of Biomacro molecules Comput Chem, 1996.20(4): p. 449-457.
[105] Dengiz, B., Altiparmak, F., Smith, A.E., Local Search Genetic Algorithm for Optimal Design of Reliable Networks IEEE Transactionson Evolutionary Computation 1, 1997.1(3): p. 179-188.
[106] Easton, F.F., Mansour, N., A Distributed Genetic Algorithm for Determ Inistic and Stochastic Labor Scheduling Problems European J Of Operational Research 1999. 118(3): p. 505-523.
[107] Mayer, M.K., A Network Parallel Genetic Algorithm for The One Machine Sequencing Problem Computers & Mathematics with Applications 1999.37(3): p. 71-78.
[2] 蒋冰蕾，美国智能公共交通领域发展近况．城市公共交通，1999(1)：p．9-13．
[3] 陆化普，日本智能公共交通系统的开发应用现状与展望．城市公共交通，1999(1)：p．5-8．
[4] Desaulniers, G., Hickman,M.D., Public Transit, in Les Cahiers du GERAD. 2003. p. G-2003-77.
[5] 武长顺，城市交通领域中的系统工程．天津大学学报(社会科学版)，2005．7(4)：p．262-265．
[6] 刘振阳，张秀媛，徐进等，城市智能公共交通系统．2005：中国铁道出版社．
[7] 周尚明，关于现代工业自动化中的计算智能和信息处理问题的研究，in铁道部科学研究院博士后研究工作报告．1998．
[8] Bezdek, J.C., On the relationship between neural networks, pattern recognition and intelligence. International Journal of Approximate Reasoning 1992. 6(2): p. 85-107.
[9] 吴佑寿，世界计算智能大会简介．电子科技导报，1998(10)：p．37-38．
[10] 季厌浮，张．，计算智能技术及其应用．煤炭技术，2004．23(4)：p．112-113．
[11] Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R., Fuzzy Models and Algorithms for Pattern Recognition and Image Processing 2005: Springer-Verlag New York, Inc. Secaucus, NJ, USA
[12] Dubois, D., Bel, G. & Libre, M., A Set of Methods in Transportation Network Synthesis and Analysis. Journal of Operational Research Society, 1979. 30(9): p. 797-808.
[13] Ceder, A., Wilson, N. H. M., Bus network design. Transportation Research-B, 1986.20B(4): p. 331-44.
[14] Hasselstr6m, D., Public Transportation Planning-A Mathematical Programming Approach, in Doctoral Dissertation, University of Goteborg, Sweden. 1981.
[15] Baaj, M.H., Mahmassani, H.S., Hybrid Route Generation Heuristic Algorithm for the Design of Transit Networks. Transportation Research-C, 1995.3(1): p. 31-50.
[16] Guan, J.F., Yang, H., Wirasinghe, S.C. Determine network transit line configuration for optimal passenger line assignment, in Proceedings of the 2004 IEEE International Conference on Networking: Sensing & Control. 2004: p. 839-843.
[17] 王炜，杨新苗，陈学武等，城市公共交通系统规划方法与管理技术．2002：科学出版社．
[18] 单连龙，高自友，城市公交系统连续平衡网络设计的双层规划模型及求解算法．系统工程理论与实践，2000(7)：p．85-93．
[19] Steenbrink.P.A, Optimization of Transport Networks. 1974: Wiley, London.
[20] Chakroborty, P., Deb, K., Subrahmanyam, P. S., Optimal Scheduling of Urban Transit System Using Genetic Algorithms. Journal of Transportation Engineering, 1995. 121(6): p. 544-553.
[21] Dashora, M., Dhingra, S.L., Expert System for Bus Routing and Scheduling. Computers in urban planning and urban management, 1998.2: p. 841-853.
[22] Fernandez, R., An Expert System for the Preliminary Design and Location of High Capacity Bus Stops. Traffic Eng. Control, 1993. 34: p. 533-539.
[23] Senevirante, EN., Analysis of on Time Performance of Bus Services Using Simulation Journal of Transportation Engineering, 1990. 116(4): p. 517-531.
[24] Dorigo, M., Maniezzo, V., Colorni, A., The Ant System: Optimization by A Colony of Cooperating Agents. IEEE Transactions on Systems, Man and Cybernetics, 1996.1(26): p. 29-41.[25] 黄席樾，张著洪，何传江等，现代智能算法理论及应用．2005：科学出版社．
[26] Stutzle, T., Hoos,H.. MAX-MIN Ant System and Local Search for Combinatorial Optimization Problems. in S. Voβ, S. Martello, I.H. Osman, C. Roucairol (Eds.), Meta-heuristics: Advances and Trends in Local Search Paradigms for Optimization: Kluwer Academic, Boston, MA. 1999: p. 313-329.
[27] Gambardella, L.M., Dorigo, M., An Ant Colony System Hybridized With A New Local Search for The Sequential Ordering Problem. INFORMS Journal on Computing, 2000. 12(3): p. 237-255.
[28] Botee, H.M., Bonabeau E., Evolving Ant Colony Optimization. Complex Systems, 1998. 1(2): p. 149-159.
[29] 吴庆洪，张纪会，徐心和，具有变异特征的蚁群算法．计算机研究与发展，1999．36(10)：p．1240-1245．
[30] 陈岐，沈洁，秦玲，陈宏建，基于分布均匀度的自适应蚁群算法．软件学，2003．14(8)：p．1379-1387．
[31] 詹士昌，徐捷，吴俊，蚁群算法中有关算法参数的最优选择．科技通报，2003．19(5)：p．381-386．
[32] 康立山，谢云，尤矢勇，罗祖华，非数值并行算法-模拟退火算法．2000：科学出版社．
[33] Matsumura, T., Nakamura, M., Okech, J., Onaga, K., A parallel and distributed genetic algorithm on loosely-coupled multiprocessor systems. IEICE Trans. Fundam. Electron. Commun. Comput 1998. E81-A(4): p. 540-546.
[34] 苏)M*C*费舍里松，城市交通．1984：中国建筑工业出版社．
[35] Silman, L.A., Barzily, Z., Passy, U. , Planning the Route System for Urban Buses. Computers and Operations Research 1974(1): p. 210-211.
[36] Scheele, S., A Supply Model for Public Transit Services. Transportation Research-B, 1980. 14: p. 133-146.
[37] Furth, P.G., Wilson, N.H.M., Setting Frequencies on Bus Routes: Theory and Practice. Trans. Res. Record, 1982. 818: p. 1-7.
[38] Koutsopoulos, H.N., Odoni, A., Wilson, N.H.M. Determination of Headways as a Function of Time Varying Characteristics on a Transit Network. in Computer Scheduling of Public Transport 2, North-Holland, Amsterdam. 1985: p. 391-414.
[39] Lazar, N.S., Paul, M., Schonfeld, Method for Optimizing Transit Service Coverage,. Transportation Research Record 1993. 1402: p. 28-39.
[40] Shih, M.C., Mahmassani, H.S., Vehicle Sizing Model for Bus Transit Networks. Transportation Research Record, 1995. 1452: p. 35-41.
[41] 孙芙灵，公交调度中发车间隔的确定方法的探讨．西安公路交通大学学报，1997．17(2)：p．44q8．
[42] Gao, Z.Y., Sun, H.J., Shan, L.L., A continuous equilibrium network design model and algorithm for transit systems. Transportation Research part B, 2004. 38: p. 235-250.
[43] Wong, S.C., Yang, H., Reserve capacity of a signal-controlled road network. Transportation Research-B, 1997.30: p. 397-402.
[44] Spiess, H., Florian, M., Optimal Strategies: A New Assignment Model for Transit Networks. Transportation Research-B, 1998. 23(2): p. 83-102.
[45] 郭亚军，多属性综合评价．1996：东北大学出版社．
[46] Duan Q. Y., S.S., and Gupta V. K. , Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res., 1992.28(4): p. 1015-1031.
[47] Duan Q. Y., G., V. K., and Sorooshian, S., Shuffled complex evolution approach for effective and??efficient minimization. J Optim. Theory Appl., 1993.76(3): p. 501-521.
[48] Kuczera, G., Efficient subspace probabilistic parameter optimization for catchment models. Water Resources Research, 1997.33(1): p. 177-185.
[49] Ben-Akiva, M., Lerman, S.R., Discrete Choice Analysis: Theory and Application to Travel Demand. 1985: MIT Press, Cambridge, Mass.
[50] DeLurgio, S.A., Forecasting principles and applications. 1998: McGraw-Hill, New York. 1998,
[51] Stephanedes, Y.J., Kwon, E., Michalopoulos, P., On-Line Diversion Prediction for Dynamic Control And Vehicle Guidance In Freeway Corridors. Transportation Research Record, 1990. 1287: p. 11-19.
[52] Smith, B.L., Demetsky, M. J, Short-Term Traffic Flow Prediction:Neural Network Approach Transp. Res. Rec., 1995. 1453: p. 98-104.
[53] Dailey, D., Maclean, S., Cathey, F., Wall, Z., Transit Vehicle Arrival Prediction: Algorithm And Large-Scale Implementation,. Journal of the Transportation Research Board, 2001.1771: p. 46-51.
[54] Okutani, I., Stephanedes, Y. J., Dynamic Prediction of Traffic Volume through Kalman Filtering Theory. Transportation Research Record, 1984. 18B(1): p. 1-11.
[55] Shalaby, A., Farhan, A. Bus Travel Time Prediction Model for Dynamic Operations Control And Passenger Information Systems. in The 82nd Annual Meeting of the Transportation Research Board. Washington, DC. 2003.
[56] Park, D., Rilett, L. R, Forecasting Freeway Link Travel Times With A Multilayer Feedforward Neural Network Computer-Aided Civil and Infrastructure Engineering., 1999.14(5): p. 357-367.
[57] Ding, Y., Chien, S. The Prediction Of Bus Arrival Times With Link-Based Artificial Neural Networks. in Proceedings of the International Conference on Computational Intelligence & Neurosciences (CI&N)—Intelligent Transportation Systems. Atlantic City, New Jersey. 2000: p. 730-739.
[58] Chien, I.J., Ding, Y., Wei, C., Dynamic Bus Arrival Time Prediction With Artificial Neural Networks. Journal of Transportation Engineering, ASCE, 2002. 128(5): p. 429-438.
[59] Chen, M., Liu, X.B., Xia, J.X., Chien S.I., A Dynamic Bus-Arrival Time Prediction Model Based on APC Data. Computer-Aided Civil and Infrastructure Engineering, 2004. 19: p. 364-376.
[60] Lawrence, S., Giles, C.L., Tsoi, A.-C. Lessons In Neural Network Training: Overfitting May Be Harder Than Expected. in Proceedings of the fourteenth national conference on artificial intelligence. Mento Park: AAA1 Press. 1997: p. 540-545.
[61] Moody, J.E., The Effective Number Of Parameters: An Analysis Of Generalization And Regularization In Nonlinear Learning Systems. NIPS, 1992.4: p. 847-854.
[62] Sarle, W.S. Stopped Training And Other Remedies For Overfitting. in Proceedings of the twenty-seventh symposium on the interface of computing science and statistics. 1995: p. 352-360.
[63] Weigend, A. On Overfitting And The Effective Number Of Hidden Units, in Proceedings of the 1993 connectionist models summer school. 1994.
[64] Vapnik, V.N., The Nature of Statistical Learning Theory. 1995: Springer.
[65] Vapnik, V.N., An Overview Of Statistical Learning Theory. IEEE Transactions on Neural Networks, 1999. 10 (5): p. 988-999.
[66] Vapnik, V.N., The Nature of Statistical Learning Theory. Springer, New York, 2000.
[67] Vapnik, V.N., Statistical Learning Theory. Springer, 1998.
[68] Dong, B., Cao,C., Lee,S.E.; , Applying Support Vector Machines To Predict Building Energy??Consumption In Tropical Region. Energy and Buildings, 2005.37: p. 545-553.
[69] 王定成，方廷健，高理富等，支持向量机回归在线建模及应用．控制与决策，2003．18(1)：p．89-91．
[70] Cao, L.J., Francis, E.H., Tay, Support Vector Machine With Adaptive Parameters In Financial Time Series Forecasting, IEEE Transactions on Neural Networks, 2003.14(6).
[71] Grewal, M.S., Andrews, A. P., Kalman Filtering: Theory and Practice Using MAL TAB (2nd Ed.). 2001, New York John Wiley & Sons.
[72] 王田，车载组合导航系统误差修正方法研究 2002，国防科学技术大学；．
[73] Hsu, C.W., Chang, C.C., and Lin, C.J., A Practical Guide to Support Vector Classification, Technical report, in Department of Computer Science and Information Engineering. 2003, National Taiwan University.
[74] Turnquist, M.A., Strategies for improving reliability of bus transit service. Transportation Research Record 1978.818: p. 7-13.
[75] Turnquist, M.A., Blume, S.W., Evaluating potential effectiveness of headway control strategies for transit systems. Transportation Research Record 1980. 746: p. 25-29.
[76] Adamski, A. Optimal Dispatching Control of Bus Lines. in Proceedings of the 9th IFIP Conference on Optimization Techniques, Lecture Notes in Control and Information Science: Springer Verlag, New York. 1980: p. 334-344.
[77] Adamski, A. Optimal Adaptive Dispatching Control in an Integrated Public Transport Management System. in Proceedings of the Second Meeting of the EURO Working Group on Urban Traffic and Transportation: Paris. 1993: p. 913-938.
[78] Adamski, A. Real-time Computer-aided Control in Public Transport from the Point of View of Xchedule Reliability. in Lecture Notes in Economics and Mathematics Systems. 1995: p. 278-295.
[79] Eberlein, X.J., The Real-time Deadheading Problem in Transit Operations Control. transportation Research-B, 1998.32B: p. 77-100.
[80] Adebisi, O., A Mathematical Model for Headway Variance of Fixed Route Buses. Transportation Research-B, 1986.20B: p. 59-70.
[81] Adamski, A., Turnau, A. , Simulation Support Tool for Real-time Dispatching Control in Public Transport transportation Research-A, 1998 32: p. 73-87.
[82] Koffiman, D., A Simulation Study of Alternative Realtime Bus Headway Control Strategies. transportation Research Record, 1978 663 p. 41-46.
[83] Vanderbona, U., Richardson, A.J., Effect of Checkpoint Control Strategies in a Simulated Transit Operation transportation Research-A, 1986 20 p. 429-436.
[84] Barnett, A., On Controlling Randomness in Transit Operations Transportation Science 1974. 8: p. 102-116.
[85] Adamski, A. Expert System for dispatching control of the public transport vehicles at terminals, in Proc. International Conference TRANSSYSTEM-89. 1989.
[86] Eberlein, X.J., Wilson, N.H.M., Bernstein, D. Modeling real-time control strategies in public transit operations, in Computer Aided Transit Scheduling, Lecture Note in Economics and Mathematical Systems: Springer-Verlag, Berlin,. 1999: p. 325-346.
[87] Dessouky, M., Randolph Hall, R., Zhang, L., Singh, A., Real-time control of buses for schedule coordination at a terminal. Transportation Research Part A 2003. 37: p. 145-164.[88] Zolfaghari, S., Azizi, N., Jaber, M.Y., A model for holding strategy in public transit systems with real-time information. International Journal of Transport Management 2004. 2: p. 99-110.
[89] Fu, L.P., Liu, Q., Calamai, P., Real-Time Optimization Model for Dynamic Scheduling of Transit Operations. Transportation Research Record 2003. 1857.
[90] SUN, A., HICKMAN, M., The Real-Time Stop-Skipping Problem. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 2005.9(2): p. 91-109.
[91] Ceder, A., Designing Transit Short-turn Trips with the Elimination of Imbalanced Loads 1988: Computer-aided Transit Scheduling, Springer-Verlag.
[92] Furth, P.G., Short-turning on Transit Routes transportation Research Record, 1988 1108: p. 45-52.
[93] Chang, G.L., Adaptive Bus-preemption Control. transportation Research-C, 1996.5: p. 251-268.
[94] Cornewell, P.R., Dynamic Signal Co-ordination and Public Transport Priority IEE, Road Traffic Monitoring and Control, 1986: p. 158-161.
[95] Eberlein, X.J., Real-time Control Strategies in Transit Operations: Models and Analysis. 1995, Massachusetts Institute of Technology, Cambridge, MA.
[96] Abkowitz, M.D., Engelstein, I., Methods for maintaining transit service regularity. Transportation Research Record 1984. 961: p. 1-8.
[97] Lin, G., P. Liang, Schonfeid,P., Larson, R., Adaptive Control of Transit Operations. Final Report for Project MD-26-7002. 1995: University of Maryland.
[98] Abdulhai, B., Sheu, J.B., Recker, W.W., Simulation of ITS on the Irvine FOT area using the 'Paramics 1.5' scalable microscopic traffic simulator: Phase Ⅰ: Model calibration and validation. California PATH Research Report UCB-ITS-PRR-99-12, 1999. 12: p. 1055-1425.
[99] Sheu, J.B., Chou, Y.H., Chen, A., Stochastic modeling and real-time prediction of incident effects on surface street traffic congestion. Applied Mathematical Modelling, 2004. 28: p. 445-468.
[100] Holland, J.H., Adaptation in Natural and Artificial Systems.. 1975: University of Michigan Press, Ann Arbor, MI.
[101] 陈国良，王熙法，庄镇泉等，遗传算法及其应用 1996，北京 人民邮电出版社
[102] Harik, G., Paz E, C., Miller B. The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations. in Proc. of the 5th Infl Conf. on Evolution Computation: IEEE Press, New York. 1997: p. 7-12.
[103] 曾国荪，丁春玲，并行遗传算法分析．计算机工程，2001．27(9)：p．53-55．
[104] Beckers, M.L.M., Derks E., Melssen, W.J. et al Using Genetic Algorithms for Conformational Analysis of Biomacro molecules Comput Chem, 1996.20(4): p. 449-457.
[105] Dengiz, B., Altiparmak, F., Smith, A.E., Local Search Genetic Algorithm for Optimal Design of Reliable Networks IEEE Transactionson Evolutionary Computation 1, 1997.1(3): p. 179-188.
[106] Easton, F.F., Mansour, N., A Distributed Genetic Algorithm for Determ Inistic and Stochastic Labor Scheduling Problems European J Of Operational Research 1999. 118(3): p. 505-523.
[107] Mayer, M.K., A Network Parallel Genetic Algorithm for The One Machine Sequencing Problem Computers & Mathematics with Applications 1999.37(3): p. 71-78.