【作者】 韩超；

【导师】 宋苏；

【作者基本信息】 北京工业大学 ， 控制理论与控制工程， 2004， 硕士

【摘要】 摘 要实时、准确的短时交通流量预测是智能交通系统（ＩＴＳ）中的一个关键问题。随机干扰影响大、模型不确定性强给准确的短时交通流量预测带来了许多困难。此外，交通流量数据序列的非平稳性也是要解决的一个问题。　基于采用 ＡＲ（ｐ）模型结构的时间序列分析方法，提出一种短时交通流实时自适应预测算法。在该算法中采用带遗忘因子的递推最小二乘方法进行参数估计，采用基于线性最小方差预报原理的 Ａｓｔｒｏｍ 预报算法进行预报。针对大量实测数据进行仿真实验，结果表明：减小遗忘因子可以提高一步预测的性能。此外，将该算法分别应用于工作日和双休日的数据时，仿真实验都取得了较好的预测效果，说明该算法对不同交通流状况具有较好的适应性。　在该算法的基础上提出了一种改进的多步自适应预测方法。新算法增加了误差补偿项，能较好地满足时变模型的预测要求。针对大量实测数据进行仿真实验，结果表明：改进算法在应用于时变性强的短时交通流量多步预测时具有较好的预测性能，而且其预测性能优于线性最小方差预报算法的。　将基于 ＧＭ（１，１）模型的自适应预测算法应用于短时交通流量预测，仿真实验结果表明：该预测算法也可以取得较好的预测性能，但其预测精度比基于ＡＲ（１１）模型的线性最小方差自适应预测算法的差一些。　为了能发挥这两种预测模型各自的优点，本文提出了一种基于 ＧＭ（１，１）模型和 AR(11)模型的组合模型自适应预测算法，仿真实验结果表明：其预测性能要优于基于单个 ＧＭ（１，１）模型或单个 AR(11)模型预测算法的预测性能，它是一种更好的预测模型。更多还原

【Abstract】 Real-time and accurate short-term traffic flow forecasting has become a criticalproblem in intelligent transportation systems (ITS). The strong random disturbanceand strong uncertainty of model bring much difficulity for accurate traffic flowforecasting . In addition, nonstationarity of traffic flow data series is another problemto be solved. Based on time series analysis method adopting AR(p) model, a kind of real-timeadaptive forecasting method for short-term traffic flow was presented . In this methodthe recursive forgetting factor least square method (RFFLS) was adopted forparameter estimation. The Astrom forecasting algorithm was used for forecasting,which is based on linear minimum square error of prediction. A lot of real observationdata are used for simulation tests and results show that when forgetting factor isdecreased, the one-step forecasting performance can be improved. In addition, whenthis method is respectively applied to the data at the weekday and the weekend, bothsimulation tests have good forecasting performance, which demonstrates that thismethod has good adaptability in different traffic flow circumstances. Then based on this method a kind of improving multi-step adaptive forecastingmethod was presented. The error compensation item was added to this new methodwhich could well meet the needs of forecasting for time-variant models. A lot of realobservation data are used for simulation tests and results show that when theimproving method is applied to the strong time-variant multi-step short-term trafficflow forecasting, it has good forecasting performance which is superior to the linearminimum square error forecasting method’s. Another adaptive forecasting method based on GM(1,1) model is also appliedto the short-term traffic flow forecasting and simulation results show it also has goodforecasting performance. But its forecasting performance is not superior to theadaptive forecasting method’s based on AR(11) model. In order to utilizing advantages of these two kind of models, a kind of adaptiveforecasting method based on combination model of GM(1,1) model and AR(11)model was presented. simulation results show it has good forecasting performancewhich is superior to the forecasting method’s based on the single GM(1,1) model orsingle AR(11) model and it is a kind of much better forecasting model.更多还原