考虑上下游的LSTM短时交通流量预测

Using Upstream and Downstream Traffic Information for Short-term Traffic Flow Prediction Based on LSTM Recurrent Neural Network

使用基于LSTM循环神经网络的短时交通流量预测模型分析了不同输入配置对交通流量预测精度的影响。首先,比较了同一车辆检测站点处交通流量、速度和占有率数据的不同组合对短时交通流量预测的影响。实验结果表明,在模型输入中包含速度/占有率信息整体来说可以增强模型的预测性能。为了在模型中引入空间信息,我们进一步考虑了目标车辆检测站点上下游的交通流量状况,分别测试了包含目标车辆检测站点和上下游6个车辆检测站点在内的16种不同的输入组合。实验结果表明,在模型中引入上下游交通信息可以显著提高短时交通流量预测的精度。

We employ the long/short-term memory(LSTM)recurrent neural network to analyze the impact of various input settings on short-term traffic flow prediction performance.First,we compared the short-term traffic flow prediction performance for different combinations of traffic flow,speed and occupancy data on the same vehicle detection station(VDS).The results show that the inclusion of occupancy/speed information may help to enhance the performance of the model as awhole.In order to introduce spatial information into the model,we further include as inputs traffic variables from the upstream and/or downstream vehicle detector stations and test 16 different input combinations for traffic flow prediction.The experimental results show that the inclusion of both upstream and downstream traffic information in the model is very useful for improving the accuracy of short-term traffic flow prediction.