World wide transport authorities are imposing complex Hours of Service (from now on, HoS) regulations to drivers (Meyer 2011, Goel and Vidal 2013), which constraint the amount of working, driving and resting time when delivering a service. As a consequence, transport companies are responsible not only of scheduling driving plans aligned with laws that define the legal behaviour of a driver, but also of monitoring and identifying as soon as possible problematic patterns that can incur in costs due to sanctions. Fortunately, the widespread adoption of onboard IoT devices in vehicle fleets enables recording of the driver activities in event logs, but the large amount of data ingested makes difficult for transport experts to understand what happened and to make actions that forestall illegal behaviour. For this reason, an important technical challenge is to come up with easily interpretable descriptive models that help understand the huge amount of information stored in such event logs. The main objective not only consists of finding out if drivers workplan complies with the HoS regulation, but also summarising their activities in a concise but representative way. Additionally, these underlying patterns in the event log could be analysed in order to discover driving styles which could make possible the suggestion of routes or tasks more aligned to the driver preferences. The creation of driver profiles based on driving styles with HoS can be extremely useful for managers, as they could assign transport routes to the most appropriate drivers, given the length of the route and the proximity of the deadline. For example, drivers who maximise their driving hours could be preferred for long distance routes and drivers who tend to take split rest to on-city deliveries.

This paper presents the PlanMiner-N algorithm, a domain learning technique based on the PlanMiner domain learning algorithm. The algorithm presented here improves the learning capabilities of PlanMiner when using noisy data as input. The PlanMiner algorithm is able to infer arithmetic and logical expressions to learn numerical planning domains from the input data, but it was designed to work under situations of incompleteness making it unreliable when facing noisy input data. In this paper, we propose a series of enhancements to the learning process of PlanMiner to expand its capabilities to learn from noisy data. These methods preprocess the input data by detecting noise and filtering it and study the learned action models learned to find erroneous preconditions/effects in them. The methods proposed in this paper were tested using a set of domains from the International Planning Competition (IPC). The results obtained indicate that PlanMiner-N improves the performance of PlanMiner greatly when facing noisy input data.