Traffic Flow Occasional Anomaly Detection Based on Attention-LSTM Model

Abstract:

This paper proposes an long short-term memory （LSTM） traffic anomaly detection model based on the attention mechanism， which uses the overall traffic grid point data to predict the future traffic flow at various points in order to achieve the purpose of anomaly detection. By evaluating the simulation data， the Attention-LSTM model has a better detection effect. Furthermore， this paper uses the SKAB dataset to test the anomaly detection ability of the model， and obtains good results. Finally， this paper conducts experiments on the actual GPS signal data of Shanghai taxis， empirically analyzes some of the actual traffic anomalies detected， and proves the effectiveness of the model in detecting actual traffic anomalies.