HTFM: Hybrid Traffic-Flow Forecasting Model for Intelligent Vehicular Ad hoc Networks

Increased vehicular flow on roads along with proposed deployment of autonomous vehicles has necessitated the need for accurate traffic forecasting so as to achieve effective route guidance, traffic management, public safety and congestion avoidance. Although a number of traffic forecasting algorithms have been proposed but most of these algorithms perform short term traffic predictions. However future vehicular systems also defined as intelligent VANETs will require a hybrid traffic forecasting model that predicts the vehicular traffic for varying values of time. This paper proposes a time varying forecasting model that predicts vehicular flow by utilizing Long Short-Term Memory (LSTM) and Convolutional Neural Network(CNN). The model is based on large-scale, network-wide traffic with spatio-temporal features. The temporal features learned by LSTM and spatial features learned by CNNs from the matrices are further fused with external factors to derive the final forecast. Model has been implemented on the traffic data set of Chandigarh city in India, mapped onto three two-dimensional matrices of time and space. The predicted information is then forwarded by the vehicle to all the other vehicles in their vicinity using vehicular adhoc networks. Experimental results indicate that the proposed model performs significantly better than other state-of-the-art models in terms of accuracy and efficiency.