Artificial Intelligence for Truck Fleet Management**

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Today's transportation services are moving faster than ever, and companies are challenged to distribute their products more quickly and with increasingly competitive costs while maintaining safety on the road. With the emergence of data collection systems in trucks, tremendous opportunities for data intelligence are created, related to the creation of explanatory and predictive models for various events. The goal of this project is (1) to develop data-driven models to predict road accidents and infractions occurring in the truck fleet, and (2) to identify potential counter-measures to reduce the incidence of detrimental events. To build the predictive models, we pan to rely on Random Forests, a state- of-the-art classification and regression technique that has been successfully applied to many problems including in supply-chain modeling. To interpret the results and identify counter measures, we will use data mining techniques, for instance Association Rules. We expect this project to provide Groupe Robert with predictive models and possible actions to improve the **safety of its employees on the road, which will improve its competitiveness inside Canada.