Telemetry devices are generating and transferring increasingly more data, with notable
potential for decision makers. In this paper we consider the accelerometer and speed data
produced by in-vehicle data recorders as a proxy for driver behaviour. Instead of extracting
harsh events to cope with the large volumes of data, we discretise the data into a tractable
and finite risk space. This novel methodology allows us to track both acceptable and
non-acceptable driving behaviour, and calculate a more comprehensive risk model using
the envelope of the data, and nota priorithresholds. We show how thresholds suggested
in literature can characterise some driving behaviour as good, even though our empirical
evidence has not even registered such extreme driving behaviour.
We demonstrate the model using accelerometer data from 124 vehicles over a one
month period. Three rules, each a combination of accelerometer and/or speed data, are
applied to the risk space to derive person-specific scores that are comparable among the
individuals. The results show that the scoring is useful to identify specific risk groups.
The proposed model is also dynamic in that it dynamically adjusts to the observed records,
instead of data having to abide by a limited model specification.