Enhancing LSTM Prediction of Vehicle Traffic Flow Data via Outlier Correlations

Abstract

Accurate traffic flow prediction is an important tool to allow for more efficient use of traffic networks. Current traffic flow prediction algorithms such as LSTM RNNs can be very successful in predicting regular traffic flows, but often fail to accurately predict the more interesting irregularities in the traffic flows. We propose OE-LSTM (Outlier-Enriched LSTM), a novel framework for traffic flow prediction that focuses mainly on these irregular traffic flows. We consider the irregularities as outliers within each traffic flow stream and assume that for these traffic outliers to occur, a certain set of circumstances is present to cause these deviations from the regular traffic flow pattern. After detecting these outliers in traffic flow data, we measure the relation between them by performing a spatiotemporal correlation analysis. We use these correlations to trace the context of the outliers and determine as such which elements are interesting to include in our OE-LSTM prediction model. In addition to this, we also draw the foundations of a reference Cloud-based architecture for supporting big traffic data prediction based on OE-LSTM. The experimental results prove the effectiveness of the OE-LSTM framework in a real world traffic flow dataset. Both the dataset and the implementation of our framework are provided.