Applying Bayesian Networks to Reduce Fuel Consumption in Public Transportation

In this work we analyse data collected from sensors installed on some vehicles of the local public transportation system in a European city. Our analysis is conducted by means of generation and application of Bayesian networks to describe the dependence relationships between variables and to predict the target variable of fuel consumption. We experimented with different algorithms that explore the search space of the possible alternatives guided by heuristics. We compare them with the results obtained with the technology of High Performance Computing, that allowed us to do an exhaustive search and find the optimal solution from the viewpoint of the likelihood evaluation measure. We solve the model evaluation and selection problem by application of an alternative evaluation measure: Granger causality. In addition we compared the predictive ability of the target by the obtained networks. Finally, we conducted "what- if" analysis under the form of intervention and counterfactual analysis and show which decisions policy makers and the service owners should afford to reduce costs and pollution.