Discovering and Explaining Driver Behaviour under HoS Regulations
This addresses the challenge for transport companies in efficiently monitoring driver behavior to avoid sanctions under complex regulations, though it appears incremental as it applies existing planning, constraint, and clustering techniques to this domain-specific data.
The paper tackles the problem of monitoring driver compliance with Hours of Service regulations by developing a system that summarizes raw driver activity logs and explains behavior in a human-readable format, identifying infractions and grouping drivers based on similar patterns, with experimentation showing that recurring driving patterns can be clustered from short sequences to whole working days.
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.