Development of a prototype, self-trained truck driver coaching system based on geolocation, IoT and BI: Preliminary results and analyses

The volume of freight transport on roads has grown steadily over the years and is expected to increase significantly over the next decade. Truck driver coaching systems have proved a key element in boosting efficiency, profitability and safety of the haulage sector whilst contributing to infrastructure asset management objectives. Notwithstanding new, commercial driver coaching systems have shown up recently in the market, still they operate as "black boxes", whilst their functionality and automation level is very limited. Given the enormous numbers of truck fleets currently operating worldwide, the necessity for an adaptable driver coaching system, capable of providing real-time functionality is evident. This study reports the design characteristics and first results from PEGASUS research project aiming at developing a self-trained, unified, driver-coaching system based on geolocation, IoT (Internet of Things) and BI (Business Intelligence) techniques. The system by design aims at reducing operational costs for the truck fleet, improving road safety, as well as contributing to green transportation. In effect, the system has been designed to produce user-friendly, two-way advice to lorry drivers tailored to the road conditions (e.g., geometry, road markings, neighboring vehicles) and operational (e.g., weather, traffic) conditions in real time and in a dynamic manner.