This project is about analysis of drivers behavior by learning their significant driving patterns. The goal is to provide an end-to-end solution to identify risky versus safe drivers. Our identification process is based on extracting driving patterns and then analyzing each pattern within its context. To identify driving pattens, we leverage a novel trajectory segmentation approach which is presented at the 3rd ACM SIGSPATIAL PhD Symposium (2016). Besides, we study each pattern with respect to extrinsic causes which may have relationship with the exatracted pattern, like Traffic condition, Weather condition, Physical properties of routes, etc. The results of current project may be used for Usage Based Insurance (UBI) programs. For more information, please visit our paper which is presented at the 25th ACM SIGSPATIAL Conference (2017).