Discovering and Explaining Driver Behaviour under HoS Regulations

TL;DR

This paper presents a system that summarizes driver activity logs, explains driver behavior under Hours of Service regulations, and clusters drivers based on similar patterns, aiding in compliance and monitoring.

Contribution

It introduces a novel application combining planning, constraint, and clustering techniques to interpret driver logs and identify infractions in a human-readable way.

Findings

Recurring driving patterns can be effectively clustered.

The system can analyze both short sequences and full working days.

Application on real data demonstrates practical utility.

Abstract

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.