Highway Cluster Density and Average Speed Prediction in Vehicular Ad Hoc Networks (VANETs)

Abstract

With huge amounts of data being generated from almost everywhere, our universe has become data-driven. Decision making, risk prevention and mitigation, and systems assessment will not be as effective as desired without having the right data. The projected impacts and benefits of Vehicular Ad Hoc Networks (VANETs) are the driving forces for researchers to develop and further enhance VANET technology. One of the challenging and imperative issues in VANETs research is the unavailability of data. To the best of our knowledge, in this research, we are the first to create a VANET traffic dataset by using real-life traffic data. We massage the data by applying VANET human behavioral model. We experiment and validate our dataset by focusing on traffic congestion prediction. Traffic congestion can be determined by traffic density and average speed at any given point. Highly dense roads are the basic definition of congestion resulting in lower speeds of moving vehicles. We develop three time-series models ARIMA, BATS, TBATS, and a neural network model and apply them to our created VANET data to analyze and predict the total number of nodes in a cluster (density) and the average speed of the nodes. We have validated these time series prediction models by comparing the four developed models in terms of MSE, MAE, MAPE, and MASE. The created dataset and developed models can assist in predicting cluster density and average node speed to detect congestion, which will enhance route navigation.