Intelligent Traffic Management using IoT and Machine Learning

Abstract

The continuous improvements in the Internet of Things (IoTs) and machine learning (ML) make them the key enabling technologies for intelligent traffic management (ITM).The ability to accurately predict network traffic has been demonstrated as crucial for effective network management and strategic planning. Proactive management of future congestion incidents requires access to reliable long-term forecasting models. Conventional prediction methods often fail to completely capture the spatiotemporal features of the traffic flows because of the complexity of the interdependence between the flows. To this end, we proposed to improve the management of traffic with a novel framework for the predictive modeling of traffic flows. The proposed formwork introduces an improved graph network to capture the positional information in traffic follows. It is also capable of precisely capturing temporal dynamics using an improved bidirectional learning module. An attention mechanism is presented to capture the interactions among spatial and temporal patterns to further empower the predictive power of the model. Proof-of-concept experimentations are conducted on the PeMSD7 dataset, and the results (MAE: 0.197, MSE: 0.13, RMSE: 0.36, ) demonstrate the efficiency of our model over the state-of-the-art.