  World wide transport authorities are imposing complex Hours of Service
regulations to drivers, 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. Transport experts are frequently in charge of many drivers and lack
time to analyse the vast amount of data recorded by the onboard sensors, and
companies have grown accustomed to pay sanctions rather than predict and
forestall wrongdoings. This paper exposes an application for summarising raw
driver activity logs according to these regulations and for explaining driver
behaviour in a human readable format. The system employs planning, constraint,
and clustering techniques to extract and describe what the driver has been
doing while identifying infractions and the activities that originate them.
Furthermore, it groups drivers based on similar driving patterns. An
experimentation in real world data indicates that recurring driving patterns
can be clustered from short basic driving sequences to whole drivers working
days.
