基于LSTM-RF模型的城市道路交通流预测

摘要：

交通流预测作为信号协调和出行时间预测等任务的基础， 成为了交通领域的研究点。对于交通流预测问题， 研究人员提出了多种方法， 但这些方法大多只使用交通流数据的时域信息进行交通流预测， 忽略了空间相关性对于预测目标路段流的影响， 导致预测精度不理想。基于组合模型的思想提出了一种称为LSTM-RF的交通流预测模型。 在交通流预测过程中， 首先使用LSTM模型提取预测目标路段的时序特征， 再将其预测值与采集的相邻上下游路段信息同时作为随机森林模型的输入特征， 进行交通流时空相关性分析， 获得最终的预测结果。 并通过贵阳市车牌识别系统采集的城区132条路段的交通流数据进行实验验证。 结果表明： 该方法在预测精度上优于单一模型， 并且预测误差相比单一模型有明显减少。

Abstract： Traffic flow prediction, as the basis of signal coordination and travel time prediction, has become a research point in the field of transportation. For traffic flow prediction, researchers have proposed a variety of methods, but most of these methods only use the time domain information of traffic flow data to predict the traffic flow, ignoring the impact of spatial correlation on the prediction of target road segment flow, which leads to poor prediction accuracy. In this paper, a traffic flow prediction model called as long short time memory and random forest (LSTM-RF) was proposed based on the combination model. In the process of traffic flow prediction, the long short time memory (LSTM) model was used to extract the time sequence features of the predicted target road segment. Then, the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow, so as to obtain the final prediction results. The traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and verified. The results show that the method is better than the single model in prediction accuracy, and the prediction error is obviously reduced compared with the single model.