Abstract: Customize safe speeds for vehicles are determined from information that is collected from multiple trips and multiple drivers across many road segments. The information collected includes the speed traveled along the road segment, possibly along with additional information, such as (for example) driver, time of day, day of week, weather and road conditions, angle of the sun or other factors. Given information from a new trip, the driver's speed can be compared on each road segment with relevant data from previous trips (from e.g., similar roads, weather conditions, drivers). The comparison yields a score representing the safety of the driver's speed.

Abstract: Customize safe speeds for vehicles are determined from information that is collected from multiple trips and multiple drivers across many road segments. The information collected includes the speed traveled along the road segment, possibly along with additional information, such as (for example) driver, time of day, day of week, weather and road conditions, angle of the sun or other factors. Given information from a new trip, the driver's speed can be compared on each road segment with relevant data from previous trips (from e.g., similar roads, weather conditions, drivers). The comparison yields a score representing the safety of the driver's speed.

Abstract: An approach to telematics using mobile devices provides battery-efficient trajectory and mileage inference from inaccurate and intermittent location data. Accurate trajectories of how users or vehicles move in the physical world are formed by processing raw position estimates obtained from noisy, inaccurate, and error-prone position sensors on mobile devices, where the position data may also arrive intermittently with long time gaps. The trajectory is formed using the process of map matching, which determines the trajectory on a map that best explains the sequence of position observations.

Abstract: An approach to telematics using mobile devices provides battery-efficient trajectory and mileage inference from inaccurate and intermittent location data. Accurate trajectories of how users or vehicles move in the physical world are formed by processing raw position estimates obtained from noisy, inaccurate, and error-prone position sensors on mobile devices, where the position data may also arrive intermittently with long time gaps. The trajectory is formed using the process of map matching, which determines the trajectory on a map that best explains the sequence of position observations.