基于混沌理论的交通流短时预测模型

交通流预测是交通系统可行性分析、交通设计和交通管控的基础,短时预测是交通流预测的难点.论文在分析现有交通流预测方法的基础上,提出了一种基于混沌理论的交通流短时预测方法,利用基于小数据量的W olf改进算法计算了流率序列的最大Lyapunov指数.将基于Lya-punov指数的一维预测模式具体化,建立了交通流短时预测模型,并对模型进行了改进,改进后的预测结果具有较高的精度.该模型在智能交通系统(ITS)的交通控制与诱导方面具有广阔的应用前景.

A Chaos Theory - Based Model of Short - Time Forecasting of Traffic Flow

Traffic flow forecasting is the basis of feasibility analysis of traffic system, traffic design,traffic management and control,in which the short-time forecasting is more difficult.The existing traffic forecasting models are analyzed,and a new short-time forecasting model based on chaos theory is developed.The Largest Lyapunov Exponent of flow rate series is estimated with the improved Wolf arithmetic method based on small data sets.A short-time forecasting model is developed from the one-dimensional forecasting mode based on the Largest Lyapunov Exponent.The forecasting precision of the improved model is relatively high and the model has a wide potential in traffic control and inducement in ITS field.