异常突发事件下交通流特征分析与预测

Research on Urban Traffic Flow Characteristic and Prediction under Abnormal States

针对在异常突发事件下交通流会呈现的不同特性进行研究,以期实现在突发事件下的城市道路短期交通流精准预测。引入基于跳转的ARIMA模型来对突发事件下的交通流变化规律进行描述,在路网正常情况下和路网堵塞情况下,仿真算例表明,预测模型可以较好地进行道路区域网络交流注预测。

In order to obtain urban trafficflow accurate predict ion, different characteristics of trafficflow under abnormal state need to be studied. Considering the difference characters of the turning proportion matrix betweencongestion case and discongestion case, a switching autoregressive integrated moving average （ARIMA） model is proposed and employed to explore how traff ic flow varies with time, the simulation results show that the proposed approach is appl icable and effective.