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

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- 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

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