An approach for increased supply chain security by using automatic detection of anomalous vehicle behavior

• In recent years, the development of low-cost GPS transceivers has made it possible to equip all trucks in a fleet with equipment for automatically reporting the status of the trucks to a fleet management system. The downside is that the huge amount of information that is gathered must be evaluated in real-time by an operator. We propose the use of a data-driven anomaly detection algorithm that learns "normal" vehicle behaviour and detects anomalous behaviour such as smuggling, accidents and hijacking, The algorithm is evaluated on real-world data from trucks and commuters equipped with GPS transceivers. The results give initial support to the claim that anomaly detection based on statistical learning can be used to support human descision making. This ability can increase supply chain security by alerting an operator on anomalous vehicle behaviour.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Keyword

Anomaly detection

Decision support system

Situation awareness

Statistical learning

Teknik

Technology

Publication and Content Type

ref (subject category)

kon (subject category)