Reducing Traffic Congestion by LSTM-LOF Framework

Traffic congestion is a phenomenon with which most drivers are familiar. It negatively affects drivers, city's residents and also the economic efficiency. Road pavement condition is one of the main causes of traffic congestion. Warning drivers of road pavement conditions is an effective and highly recommended solution to prevent a traffic jam before it actually occurs. Through this paper, we present a new strategy to reduce traffic congestion. We propose an unsupervised anomaly detection framework that predicts road obstacles. We suggest a well-motivated combination between the Long Short Term Memory (LSTM) and Local outlier value factor (LOF) techniques for predicting obstacles in time series. This combination can potentially offer very promising results in terms of prediction accuracy. We illustrate significant performance gains achieved by our technique with respect to the conventional methods.