Short-Term Traffic Congestion Prediction with Deep Learning for LoRa Networks

Abstract

Real-time prediction of traffic congestion enables intelligent transportation systems to improve traffic mobility, reduce delays, and enhance road safety. In this paper, an intelligent traffic congestion prediction system is presented to classify the traffic status across a road network using machine learning. It applies a long short term memory (LSTM) model to estimate the traffic congestion for short term future for LoRa networks. The proposed system, once trained, can efficiently predict traffic congestion using low bandwidth real-time traffic data that can be collected from roadside sensors using a low-power wide area network (LPWAN) technology such as LoRa. We use an online dataset to train and evaluate the proposed model, which shows that the proposed traffic congestion prediction model achieves high performance in terms of accuracy, precision, recall, and success rate. It reduces the error rates and the computing time for fast and accurate future predictions.