基于计算实验的城市道路行程时间预测与建模

唐少虎; 刘小明; 陈兆盟; 张金金

doi:10.16383/j.aas.2015.c140846

基于计算实验的城市道路行程时间预测与建模

doi: 10.16383/j.aas.2015.c140846

1.
北方工业大学电气与控制工程学院北京 100144;
2.
北方工业大学城市道路交通智能控制技术北京市重点实验室北京 100144;
3.
交通运输部公路科学研究所北京 100088

基金项目:

国家自然科学基金(61374191), 国家科技支撑计划(2014BAG03B01)资助

详细信息

作者简介:
刘小明北方工业大学电气与控制工程学院教授.2004年获得中国科学院自动化所博士学位.主要研究方向为交通流理论,智能交通控制.E-mail:tslxm@sina.com

计量
- 文章访问数: 1862
- HTML全文浏览量: 55
- PDF下载量: 1886
- 被引次数: 0
出版历程
- 收稿日期: 2014-12-04
- 修回日期: 2015-04-28
- 刊出日期: 2015-08-20

Urban Road Travel Time Prediction and Modeling via Computational Experiments

1.
College of Electrical and Control Engineering, North China University of Technology, Beijing 100144;
2.
Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144;
3.
Research Institute of Highway Ministry of Transport, Beijing 100088

Funds:

Supported by National Natural Science Foundation of China (61374191) and National Science and Technology Support Program (2014BAG03B01)

摘要

摘要: 城市道路行程时间预测对于提高交通管控效果具有重要意义. 本文综合应用平行系统、集散波、误差反馈修正、多模型自适应控制及模型库动态优化策略等方法与技术对间断流行程时间预测问题进行了研究. 首先,介绍了平行系统理论的基本原理及计算实验的基本方法; 然后,给出了基于平行系统理论的路段行程时间的预测模型, 设计了基于集散波的行程时间计算实验方法, 提出了多模型自适应行程时间预测并给出了模型动态优化策略. 最后,通过实验证明了本方法的有效性. 结果表明, 本文方法预测精度较高, 且能够对行程时间预测值进行持续优化, 可为后续的间断流行程时间预测研究提供借鉴.
- 行程时间预测 /
- 平行系统 /
- 计算实验 /
- 集散波 /
- 多模型自适应控制
Abstract: Urban road travel time prediction is of great significance to improve the effect of traffic management and control. In this paper, integrated applications of methods and technologies are studied such as parallel systems, traffic waves, error feedback amendment, multi-models adaptive control, and the dynamic optimization strategies for multi-models to the prediction problems of disconnect traffic flow. Firstly, the basic principles of parallel systems theory and the methods of computational experiments are introduced. Then, the prediction model of link travel time based on the theory of parallel systems is given, and the methods of computational experiments of travel time based on the traffic waves are designed. More over, multi-models adaptive travel time prediction models as well as dynamic optimization strategies are put forward. Finally, the effectiveness of the proposed method is shown through the analysis of experimental results. The proposed method provides a higher precision and continuous optimization of travel time prediction, it also provides a reference for the following travel time prediction of disconnect traffic flow.
- Travel time prediction /
- parallel systems /
- computational experiments /
- traffic waves /
- multi-models adaptive control

HTML全文

参考文献(31)

[1]	Yang Xiao-Guang, Cai Run-Lin, Zhuang Bin. An algorithm for predicting urban travel time based on license auto- recognition system. Computer and Communications, 2005, 23(3): 29-32 (杨晓光, 蔡润林, 庄斌. 基于车牌自动识别系统的城市道路行程时间预测算法. 交通与计算机, 2005, 23(3): 29-32)
[2]	Liu Jiang-Yong, Yun Mei-Ping, Yan Ya-Wen, Yang Xiao-Guang. A real-time travel time prediction model based on RBF neural network. Computer and Communications, 2011, 29(5): 31-35 (刘江用, 云美萍, 闫亚文, 杨晓光. 基于径向基函数神经网络的城市道路路段行程时间实时预测模型. 交通信息与安全, 2011, 29(5): 31-35)
[3]	Yang Zhao-Sheng, Sun Xi-Mei. A practical method of real-time dynamic link travel time prediction. Journal of Highway and Transportation Research and Development, 2001, 18(2): 44-46 (杨兆升, 孙喜梅. 实时动态路段行程时间预测的一种实用方法. 公路交通科技, 2001, 18(2): 44-46)
[4]	Yang Zhi-Hong, Yang Zhao-Sheng, Yu De-Xin, Chen Lin. Travel time prediction method based on Marcov queuing model. Journal of Jilin University (Engineering and Technology Edition), 2004, 34(4): 671-674 (杨志宏, 杨兆升, 于德新, 陈林. 基于马尔可夫排队模型的行程时间预测方法. 吉林大学学报, 2004, 34(4): 671-674)
[5]	Ma Y C, Chowdhury M, Sadek A, Jeihani M. Integrated traffic and communication performance evaluation of an intelligent vehicle infrastructure integration (VII) system for online travel-time prediction. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(3): 1369-1382
[6]	Nie Qing-Hui, Xia Jing-Xin, Zhang Wei-Hua. Framework and key technologies for travel time prediction based on multiple ITS data sources. Journal of Southeast University (Natural Science Edition), 2011, 41(1): 199-204 (聂庆慧, 夏井新, 张韦华. 基于多源ITS数据的行程时间预测体系框架及核心技术. 东南大学学报(自然科学版), 2011, 41(1): 199-204)
[7]	Khosravi A, Mazloumi E, Nahavandi S, Creighton D, van Lint J W C. Prediction intervals to account for uncertainties in travel time prediction. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 537-547
[8]	Li R M, Rose G. Incorporating uncertainty into short-term travel time predictions. Transportation Research Part C: Emerging Technologies, 2011, 19(6): 1006-1018
[9]	Ndoye M, Totten V F, Krogmeier J V, Bullock D M. Sensing and signal processing for vehicle reidentification and travel time estimation. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 119-131
[10]	Simroth A, Zähle H. Travel time prediction using floating car data applied to logistics planning. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 243-253
[11]	Yildirimoglu M, Geroliminis N. Experienced travel time prediction for congested freeways. Transportation Research Part B: Methodological, 2013, 53: 45-63
[12]	Jenelius E, Koutsopoulos H N. Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Research Part B: Methodological, 2013, 53: 64-81
[13]	Zheng F F, Van Zuylen H. Urban link travel time estimation based on sparse probe vehicle data. Transportation Research Part C: Emerging Technologies, 2013, 31: 145-157
[14]	Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5): 485-489, 514 (王飞跃. 平行系统方法与复杂系统的管理和控制. 控制与决策, 2004, 19(5): 485-489, 514)
[15]	Wang Fei-Yue. Computational theory and method on complex system. China Basic Science, 2004, 6(5): 3-10 (王飞跃. 关于复杂系统研究的计算理论与方法. 中国基础科学, 2004, 6(5): 3-10)
[16]	Xiong G, Wang K F, Zhu F H, Cheng C, An X J, Xie Z D. Parallel traffic management for the 2010 Asian games. IEEE Intelligent Systems, 2010, 25(3): 81-85
[17]	Ning Bin, Wang Fei-Yue, Dong Hai-Rong, Li Run-Mei, Wen Ding, Li Li. Parallel systems for urban rail transportation based on ACP approach. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(6): 22-28(宁滨, 王飞跃, 董海荣, 李润梅, 文丁, 李莉. 基于ACP方法的城市轨道交通平行系统体系研究. 交通运输系统工程与信息, 2010, 10(6): 22-28)
[18]	Sun X B, Zhang Y, Qin G Y, Dong H R, Guan F. Pedestrian transfer time optimization of urban rail transit based on ACP approach. In: Proceedings of the 2012 IEEE International Conference on Automation and Logistics (ICAL). Zhengzhou, China: IEEE, 2012. 90-95
[19]	Liu Xiao-Ming, Li Zheng-Xi. Parallel systems for urban passenger transport hub. Acta Automatica Sinica, 2014, 40(12): 2756-2765 (刘小明, 李正熙. 城市客运交通枢纽平行系统体系研究. 自动化学报, 2014, 40(12): 2756-2765)
[20]	Wang Fei-Yue, Li Le-Fei, Huang Xing, Zou Yu-Min. A discussion of fundamental theory of long period continuous production emphasizing effectiveness, safety and energy saving. Computers and Applied Chemistry, 2007, 24(12): 1711-1713 (王飞跃, 李乐飞, 黄星, 邹余敏. 关于长周期连续安全节能有效生产基础理论的探讨. 计算机与应用化学, 2007, 24(12): 1711-1713)
[21]	Wang Fei-Yue, Zeng Da-Jun, Yuan Yong. An ACP-based approach for complexity analysis of e-commerce system. Complex Systems and Complexity Science, 2008, 5(3): 1-8 (王飞跃, 曾大军, 袁勇. 基于ACP方法的电子商务系统复杂性研究. 复杂系统与复杂性科学, 2008, 5(3): 1-8)
[22]	Ning Bin, Wang Fei-Yue, Dong Hai-Rong, Wen Ding, Liu De-Rong, Gao Shi-Gen, Wang Jing. Framework of parallel control and management for high-speed railway systems. Complex Systems and Complexity Science, 2010, 7(4): 11-21 (宁滨, 王飞跃, 董海荣, 文丁, 刘德荣, 高士根, 王晶. 高速铁路平行控制与管理系统研究框架. 复杂系统与复杂性科学, 2010, 7(4): 11-21)
[23]	Huang Wen-De, Wang Wei, Xu Xin, Xi Xiao-Ning. Computational experiments for abort planning of manned lunar landing mission based on ACP approach. Acta Automatica Sinica, 2012, 38(11): 1794-1803 (黄文德, 王威, 徐昕, 郗晓宁. 基于ACP方法的载人登月中止规划的计算实验研究. 自动化学报, 2012, 38(11): 1794-1803)
[24]	Guerin S, Kunkle D. Emergence of constraint in self-organizing systems. Nonlinear Dynamics, Psychology, and Life Sciences, 2004, 8(2): 131-146
[25]	Wang Fei-Yue. Computational experiments for behavior analysis and decision evaluation of complex systems. Journal of System Simulation, 2004, 16(5): 893-897 (王飞跃. 计算实验方法与复杂系统行为分析和决策评估. 系统仿真学报, 2004, 16(5): 893-897)
[26]	Wu X K, Liu H X. A shockwave profile model for traffic flow on congested urban arterials. Transportation Research Part B: Methodological, 2011, 45(10): 1768-1786
[27]	Wu X K, Liu H X, Gettman D. Identification of oversaturated intersections using high-resolution traffic signal data. Transportation Research Part C: Emerging Technologies, 2010, 18(4): 626-638
[28]	Liu H X, Wu X K, Ma W T, Hu H. Real-time queue length estimation for congested signalized intersections. Transportation Research Part C: Emerging Technologies, 2009, 17(4): 412-427
[29]	Liu Lu-Yuan, Lv Wei-Jie, Mou Shi-Zhong. Switching multiple model adaptive control based on online optimization. Control and Decision, 2002, 17(4): 407-410 (刘鲁源, 吕伟杰, 牟世忠. 基于在线优化的切换多模型自适应控制. 控制与决策, 2002, 17(4): 407-410)
[30]	Zhai Jun-Yong, Fei Shu-Min. Multiple models adaptive control based on online optimization. Systems Engineering and Electronics, 2009, 31(9): 2185-2188 (翟军勇, 费树岷. 基于在线优化的多模型自适应控制. 系统工程与电子技术, 2009, 31(9): 2185-2188)
[31]	Middleton R H, Goodwin G C, Hill D J, Mayne D Q. Design issues in adaptive control. IEEE Transactions on Automatic Control, 1988, 33(1): 50-58