  Road accidents have a high societal cost that could be reduced through
improved risk predictions using machine learning. This study investigates
whether telemetric data collected on long-distance trucks can be used to
predict the risk of accidents associated with a driver. We use a dataset
provided by a truck transportation company containing the driving data of 1,141
drivers for 18 months.
  We evaluate two different machine learning approaches to perform this task.
In the first approach, features are extracted from the time series data using
the FRESH algorithm and then used to estimate the risk using Random Forests. In
the second approach, we use a convolutional neural network to directly estimate
the risk from the time-series data. We find that neither approach is able to
successfully estimate the risk of accidents on this dataset, in spite of many
methodological attempts. We discuss the difficulties of using telemetric data
for the estimation of the risk of accidents that could explain this negative
result.

Machine Learning;Machine Learning