【作者】 彭栋栋；

【导师】 曹凯；

【作者基本信息】 山东理工大学 ， 交通运输规划与管理， 2012， 硕士

【摘要】 获取有关交通流的实时信息是实现智能交通的重要步骤之一。在智能交通系统中,道路上的动态交通流预测是交通监控系统的基本组成部分。拥堵管理、车辆动态路线引导、公交管理、出行者信息系统、突发事件检测系统等都需要参考指定路段交通流状况。一种好的交通流量预测方法可以为交通控制提供真实可靠的数据信息。一般的交通流预测分为短期预测和长期预测,短期预测具有实时性,它很容易适应即时数据发生的改变,长期预测主要体现交通流在一个较长时段内的规律性,并不是所有状态改变都是永久的。本文基于已有的预测方法,提出一种短期、长期组合的预测方法,以交通流量预测为依托,利用从交通监控系统中得到的数据信息,在具体路段进行预测方法的验证。本文先利用双指数平滑进行短期预测,在双指数平滑预测中利用Levenberg-Marquardt算法优化平滑参数,以减小人工选择参数的误差；然后利用傅立叶级数对前期预测序列和实际观测序列之间的残差进行修正；最后基于马尔可夫状态转移模型在长期进行调整。本文将长、短期策略综合考虑提出一种组合的方法,对交通流数据进行预测并修正,使得预测结果既能很好的反映实时性,又能体现准确性和适应性。实例验证时,基于交通流数据具有随机性、自组织、类周期性等特征,在执行预测前对实际测量数据进行预处理去噪平滑。实验证明该模型既利用实时数据提供的当前方式变化特征信息,也能把握历史数据所提供的方式变化总体规律信息。模型验证对比结果表明,本文提出的模型有较高的预测精度。更多还原

【Abstract】 Getting the real-time information of traffic flows was one of the important steps to achieve the intelligent transportation. In the Intelligent Transportation Systems, the forecasting of dynamic traffic flow on the road was an essential part of the Traffic Monitoring System. The traffic flow conditions of specified sections were the necessary reference for congestion management, vehicle’s dynamic route guidance, traffic management, traveler information system and incident detection system. A good traffic flow forecasting methods could provide true and reliable data for traffic control. Prediction of traffic flow was divided into short-term forecasting and long-term forecasting. The short-term forecasting had the real-time which made it adapt to the change of the real-time data easily. The long-term forecasting mainly reflected the regularity of traffic flow in a longer period of time, for the reason that not all state changes were permanent. This paper put forward the methods combinated the short-term and long-term prediction based on the existing prediction methods. It relied on the traffic projections, and used the data obtained from the Traffic Monitoring System to make validation of prediction methods.Firstly, this article used the double exponential smoothing for the short-term forecasting. In the double exponential smoothing forecasting, it used Levenberg-Marquardt algorithm to optimize the smoothing parameters so that the error of artificial selection parameters could be reduced. Then the residuals between the pre-prediction sequence and observation sequence could be corrected by the applying of the Fourier series. Last, after long-term adjustment based on the Markov state transition model, it proposed a combination method that could predict and correct the traffic flow data. This made the forecasted results not only reflecting real-time, but also reflecting the accuracy and adaptability. In the process of the instance validation, because of the characteristics of the traffic flow data such as randomness, self-organization and class cyclical, we must make denoising smooth for preprocessing over the actual measurement data before the implementation of forecasting. Experiments show that this model used both information of the current way to change the characteristics provided by real-time data and the overall law information of the way to change provided by historical data. The comparison result of model validation shows that the proposed model of the paper has better prediction accuracy.更多还原