SDN-based Time Series Traffic Flow Forecasting in VANET

Abstract

Intelligent Transportation Systems(ITS) provides services for proper traffic assistance. Vehicular Ad-hoc Network(VANET) provides internet connectivity to vehicles and helps in traffic guidance. In this paper, traffic flow prediction is done using a modified transformer architecture for time-series vehicular data. Sequences are generated from the dataset for capturing temporal dependencies. The transformer model has been engineered to capture inter-feature correlations along with inter-sample correlations. Our transformer model has performed much better than other models like LSTM. We also propose a holistic networking model where the vehicles will be connected to Road-side Units(RSUs) and the backbone network will be Software Defined Network(SDN). The traditional design principles, that incorporates data, control and management planes together in a network device, are incapable to adapt with this much data growth, bandwidth, speed, security, scalability compared to SDN as it provides with centralized programmable mechanism reliably. The trained parameters learned using the transformer model will be passed throughout the network for traffic guidance. Similar sized packets are passed using a simulator to demonstrate the time required for the propagation of the parameters.