A Bayesian forecasting and anomaly detection framework for vehicular monitoring networks

In this paper, we are concerned with the automated and runtime analysis of vehicular data from large scale traffic monitoring networks. This problem is tackled through localized and small-size Bayesian networks (BNs), which are utilized to capture the spatio-temporal relationships underpinning traffic data from nearby road links. A dedicated BN is set up, trained, and tested for each road in the monitored geographical map. The joint probability distribution between the cause nodes and the effect node in the BN is tracked through a Gaussian Mixture Model (GMM), whose parameters are estimated via Bayesian Variational Inference (BVI). Forecasting and anomaly detection are performed on statistical measures derived at runtime by the trained GMMs. Our design choices lead to several advantages: the approach is scalable as a small-size BN is associated with and independently trained for each road and the localized nature of the framework allows flagging atypical behaviors at their point of origin in the monitored geographical map. The effectiveness of the proposed framework is tested using a large dataset from a real network deployment, comparing its prediction performance with that of selected regression algorithms from the literature, while also quantifying its anomaly detection capabilities.