The Short-Term Exit Traffic Prediction of a Toll Station Based on LSTM

Short-term exit traffic flow prediction at a toll station is an important part of the intelligent traffic system. Accurate and real-time traffic exit flow forecast of toll stations can help people predict congestion situation in advance and then take corresponding measures. In this paper, we propose a traffic flow prediction model (LSTM_SPLSTM) based on the long short-term memory networks. This model predicts the exit traffic flow of toll stations by combining both the sequence characteristics of the exit traffic flow and the spatial-temporal characteristics with the associated stations. This LSTM_SPLSTM is experimentally verified by using real datasets which includes data collected from six toll stations. The MAEs of LSTM_SPLSTM are respectively 2.81, 4.52, 6.74, 7.27, 5.71, 7.89, while the RMSEs of LSTM_SPLSTM are respectively 3.96, 6.14, 8.77, 9.79, 8.20 10.45. The experimental results show that the proposed model has better prediction performance than many traditional machine models and models trained with just a single feature.