Given the rapid increase in urbanisation and a change in the mobility patterns of humans, traffic on our road networks is expanding at an exponential rate. Governments across the globe are investing a vast amount of money on expanding road networks to cater for this ever increasing demand. This, however adds to the cognitive burden of the driver as the moving parts on the road network are also increased. Motivated by this observation, in this paper, we hypothesise that advances in technology-be it connected cars or smart road infrastructures- could play a key role in effectively and efficiently utilising the road network, thus reducing the cognitive burden on drivers. In order to investigate our hypothesis, we have implemented a set of technologies that can seamlessly harness the power of driver specific information and correlate this information with road network features such as properties of the road itself (e.g., roads with high curvature) or traffic information (e.g., traffic flow) such that daily activities of road users can be satisfied. In this paper, we present our initial work to realise this end-to-end framework and present results on contextualising the driving environment by means of feature analysis on the road network.