两种短时交通流混沌预测方法分析

Study on Short-term Traffic Flow Forecasting Based on Two Chaotic Methods

短时交通流预测是智能交通系统的核心内容和交通信息服务、交通诱导的重要基础。采用符合交通流特性的混沌理论对短期交通流进行预测。在相空间重构和混沌识别的基础上,建立短期交通流加权一阶局域预测模型和基于最大Lyapunov指数的预测模型,并对一组实际的交通流数据进行预测。仿真结果表明:两种方法都能较准确的预测交通流,但最大Lyapunov指数预测模型的预测精度相对较高。

Short-term traffic flow forecasting is not only a core element of intelligent transportation system but also plays an important role in traffic information service and traffic guidance.This paper tries to forecast the short-term traffic flow based on the chaotic theory just corresponds to that character.Two chaotic short-term traffic flow forecasting approach based on a local-region forecasting model and the largest Lyapunov model are built.The methods are used in predict a real traffic flow data.The results show that the two prediction methods can be used in the prediction of traffic flow with considerably high accuracy,and the largest Lyapunov model has higher forecasting precision.