基于混沌理论对北京二环路进行短时交通流量预测的研究

随着机动车保有量的增加，交通拥堵变成迫切需要解决的问题. 道路交通流预测可以使交通管理部门提前制订相关政策，面对即将出现的交通问题提前采取管控措施，从而可以在一定程度上缓解交通压力. 道路交通流预测预报是智能交通系统关键技术之一，短时预测是交通控制、车辆导航的技术基础. 本文在对交通系统具有耗散系统特性分析的基础上，认为交通状态中存在混沌. 本文运用混沌与分形理论恢复交通流量序列的动力学系统，并用多元局域预测法对时间序列进行预测，并实地采集数据运用模型进行分析校验. 通过分析不同时间间隔的时间序列的评价指标，比较得出此法在2至5分钟内有较高的预测精度.

Forecasting Short-time Traffic Flow for Beijing 2nd Ring Road Using Chaos Theory

With the ever-increasing motor vehicle population, traffic congestion is a severe problem of urban traffic. Traffic flow forecasting may help the traffic management branch to formulate relevant policies, optimize traffic management and solve the traffic problem, at last release the traffic pressure in a certain extent. It is one of the important issues of intelligent transportation systems. Short-time traffic flow forecasting is the main technology of traffic control and vehicle-based navigation. This paper points out that transportation system is a dissipative system and the chaos exists in traffic status. It restores dynamical systems with chaos and fractal theory and predicts the traffic flow using multivariate time series of local prediction method. The basic traffic data are collected to test the effectiveness of the model, and the evaluation index of different time interval of the traffic flow series is analyzed. The proposed method has high prediction accuracy within 2 or 5 minutes level.