Naturalistic driving research with a focus on trucks has been gaining momentum in the past decade. With the advancement in sensor technology and access to big data, it becomes possible to understand driver behaviour at a more fundamental level. This can assist in mitigating the impact trucks have on the environment while enhancing safety. Several studies have worked towards examining the predictability of driving behaviour through driver profiling (i.e. scoring a driver's behaviour or classifying drivers by assigning them different categories such as aggressive/non-aggressive). However, little research still focuses on the importance and impact of individual features used to develop these models. In the current study, an analysis of driving data from 1,727 trucks recorded over one year as part of a Dutch Field Operational Test (FOT) has been performed. This FOT, to date, has not been investigated in the published academic literature. Recent studies have analysed historical location data to assess risk associated with specific routes and environments. This is being used to provide notifications to drivers around work zones to mitigate the impact of accidents. The current thesis extends the geo-specific analysis of (truck) driving data by analysing stability in truck driving behaviour with a focus on time and location (urban areas and motorways). Here correlation analysis has been used to explore stability. Correlational analysis elucidates that metrics such as the number of headway warnings, braking events and lane departure warnings are stable over space and time. A discussion reflects on the role of vehicle characteristics (i.e. mass and engine power) towards stability. It is concluded that in the case of spatial stability: Mean point speed has higher stability on motorways than in urban areas. It has been determined that trucks with higher mass and lower engine power tend to have lower mean speed than the norm. Contrary to mean point speed, headway warnings show higher stability in urban areas than on motorways. Braking events and lane departure warnings exhibit high stability. Secondly, a strong correlation between (t and t+1) hours over the entire day is observed for temporal stability. This research is a precursor to building generalised models for profiling drivers and assessing various driving patterns. An in-depth understanding of different driving patterns can help driver coaching companies better understand metrics when time and location are factored in before providing targeted feedback. Apart from this can also facilitate fleet management. The code for analysing this dataset is accessible online and may stimulate future researchers to explore this dataset further.