System and method for analyzing driver behavior based on telematics data are disclosed. In an example, a probability of a user driving a vehicle is computed and a risk score is generated to develop at least one driver profile based on the probability. Further, routes taken by said user driving said vehicle are clustered to generate enhanced driver profile and using the clustered output to develop dynamic intelligent contexts for each said route and adding contextual intelligence messages to customize said risk score. Furthermore, the routes taken by the said user in real time are predicted. In addition, a missing route is identified through imputation of missed routes to compute annualized mileage, and a missing distance is imputed in an analysis of at least one trip of the driver in the vehicle. Also, independent trips are stitched based on at least one recommendation from an analytics engine.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for analyzing driver behavior by aggregating telematics data, said method comprising: computing, by one or more hardware processors, a probability of a user driving a vehicle and generating a risk score to develop at least one driver profile based on the computed probability; clustering, by the one or more hardware processors, a plurality of routes taken by said user driving said vehicle to generate enhanced driver profile and using the clustered output to develop a plurality of dynamic intelligent contexts for each said route and adding a plurality of contextual intelligence messages to customize said risk score, wherein the driver profiling is enabled and associated with a plurality of trip level data for the driver; predicting, by the one or more hardware processors, the plurality of routes taken by the said user in real time, wherein predicting includes a prediction of a type of trip based on at least one of a historical and a real time geo co-ordinate data; identifying, by the one or more hardware processors, a missing route through imputation of a plurality of missed routes to compute annualized mileage, and imputing a missing distance in an analysis of at least one trip of the driver in the vehicle; and stitching, by the one or more hardware processors, of a plurality of independent trips based on at least one recommendation from an analytics engine. 2. The computer-implemented method of claim 1, wherein a feedback loop is implemented to receive a plurality of interactions from a computing device. 3. The computer-implemented method of claim 1, further comprising hosting a plurality of components on a big data platform to generate a plurality of real time insights. 4. The computer-implemented method of claim 1, further comprising implementation of a route matching and a trip clustering application. 5. The computer-implemented method of claim 1, further comprising a plurality of interconnected modules facilitating a set of user experiences to at least one end user. 6. The computer-implemented method of claim 1, further comprising publishing and storing a plurality of scores on at least social platform of the driver. 7. The computer-implemented method of claim 1, further comprising building a plurality of driver segmentations based on at least one feature of the driver and identifying a plurality of location of events including hard braking, speeding, hard cornering during a driving trip taken by the driver, wherein identifying includes identifying the plurality of home and office parking location coordinates using historical GPS data collected from a driving pattern of the driver. 8. The computer-implemented method of claim 1, further comprising estimation of at least one of a mileage, a risk score in a trip and a plurality of anomalous driver detection probabilities. 9. A computer-implemented system for analyzing driver behavior by aggregating telematics data, said system comprising: at least one memory; and at least one processor, the at least one memory coupled to the at least one processor wherein the at least one processor is capable of executing programmed instructions stored in the at least one memory to: compute a probability of a user driving a vehicle and generate a risk score to develop at least one driver profile based on the computed probability; cluster a plurality of routes taken by said user driving said vehicle to generate enhanced driver profile and use the clustered output to develop a plurality of dynamic intelligent contexts for each said route and add a plurality of contextual intelligence messages to customize said risk score, wherein the driver profiling is enabled and associated with a plurality of trip level data for the driver; predict the plurality of routes taken by the said user in real time, wherein predicting includes a prediction of a type of trip based on at least one of a historical and a real time geo co-ordinate data; identify a missing route through imputation of a plurality of missed routes to compute annualized mileage, and impute a missing distance in an analysis of at least one trip of the driver in the vehicle; and stitch a plurality of independent trips based on at least one recommendation from an analytics engine. 10. The computer-implemented system of claim 9, wherein the at least one processor is configured to execute instructions to implement a feedback loop to receive a plurality of interactions from a computing device. 11. The computer-implemented system of claim 9, wherein at least one processor is further is configured to execute instructions to host a plurality of components on a big data platform to generate a plurality of real time insights. 12. The computer-implemented system of claim 9, wherein at least one processor is further configured to execute instructions to implement of a route matching and a trip clustering application. 13. The computer-implemented system of claim 10, wherein at least one processor is further configured to execute instructions to a plurality of interconnected modules to facilitate a set of user experiences to at least one end user. 14. The computer-implemented system of claim 9, wherein at least one processor is further capable of executing instructions to publish and store a plurality of scores on at least social platform of the driver. 15. The computer-implemented system of claim 9, wherein at least one processor is further capable of executing instructions to build a plurality of driver segmentations based on at least one feature of the driver and identify a plurality of location of events including hard braking, speeding, hard cornering during a driving trip taken by the driver, wherein identifying includes identifying the plurality of home and office parking location coordinates using historical GPS data collected from a driving pattern of the driver. 16. The computer-implemented system of claim 9, wherein at least one processor is further capable of executing instructions to estimate at least one of a mileage, a risk score in a trip and a plurality of anomalous driver detection probabilities. 17. A non-transitory computer readable medium embodying a program executable in a computing device, said program comprising: a program code for computing a probability of a user driving a vehicle and generating a risk score to develop at least one driver profile based on the computed probability; a program code for clustering a plurality of routes taken by said user driving said vehicle to generate enhanced driver profile and using the clustered output to develop a plurality of dynamic intelligent contexts for each said route and adding a plurality of contextual intelligence messages to customize said risk score, wherein the driver profiling is enabled and associated with a plurality of trip level data for the driver; a program code for predicting the plurality of routes taken by the said user in real time, wherein predicting includes a prediction of a type of trip based on at least one of a historical and a real time geo co-ordinate data; a program code for identifying a missing route through imputation of a plurality of missed routes to compute annualized mileage, and imputing a missing distance in an analysis of at least one trip of the driver in the vehicle; and a program code for stitching of a plurality of independent trips based on at least one recommendation from an analytics engine.