Due to traffic conditions that are very context dependent, the computation of optimized or shortest paths is a very complex problem for both drivers and autonomous vehicles. In this paper, we introduce a learning mechanism that is able to efficiently evaluate path durations based on an abstraction of the available traffic information. We demonstrate that a cache data structure allows a permanent access to the results whereas a lazy politics taking new data into account is used to increase the viability of those results. Our measures highlight the performance of each mechanism, according to different learning strategies.