两种短时交通流混沌预测方法分析

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。

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

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 result...