Abstract: This disclosure describes a vehicle-motion-analysis system that can align axes for a provider device and a corresponding transportation vehicle based on the provider device's location and motion data as a basis for generating driving-event scores for particular driving events. In particular, the disclosed systems can generate axes-rotation parameters that align axes of a provider device with axes of a transportation vehicle. In addition, the disclosed systems can identify motion paths, motion patterns, or other driving behaviors that occur during particular driving events. Further, the disclosed systems can generate driving-event scores for such driving events and can customize a graphical user interface based on the driving-event scores to reflect a provider rating and/or a location rating.

Abstract: The present application discloses systems, methods, and computer-readable media that can utilize a handheld-movement-detection model to detect whether a computing device is moved by hand or otherwise by a person within a vehicle. For instance, the disclosed systems can receive movement data from a computing device and generate filtered signals. Subsequently, the disclosed systems can utilize the handheld-movement-detection model to convert the filtered signals into a binary movement-classification signal (based on a signal threshold) to indicate the presence of handheld movement of a device. Furthermore, the disclosed systems can also utilize movement data from a computing device to detect whether the computing device is mounted and/or to detect vehicular movements.

Abstract: This disclosure describes a vehicle-motion-analysis system that can align axes for a provider device and a corresponding transportation vehicle based on the provider device's location and motion data as a basis for generating driving-event scores for particular driving events. In particular, the disclosed systems can generate axes-rotation parameters that align axes of a provider device with axes of a transportation vehicle. In addition, the disclosed systems can identify motion paths, motion patterns, or other driving behaviors that occur during particular driving events. Further, the disclosed systems can generate driving-event scores for such driving events and can customize a graphical user interface based on the driving-event scores to reflect a provider rating and/or a location rating.

Abstract: The present application discloses systems, methods, and computer-readable media that can utilize a handheld-movement-detection model to detect whether a computing device is moved by hand or otherwise by a person within a vehicle. For instance, the disclosed systems can receive movement data from a computing device and generate filtered signals. Subsequently, the disclosed systems can utilize the handheld-movement-detection model to convert the filtered signals into a binary movement-classification signal (based on a signal threshold) to indicate the presence of handheld movement of a device. Furthermore, the disclosed systems can also utilize movement data from a computing device to detect whether the computing device is mounted and/or to detect vehicular movements.

Abstract: Methods and systems for processing telematics data are provided. In one embodiment, a method is provided that includes obtaining telematics information from a first device associated with a vehicle. The telematics information may indicate operations of the vehicle. An anomalous operation of the vehicle may then be determined based on the telematics information. The method may further include determining whether the anomalous operation of the vehicle is a vehicle collision. If the anomalous operation of the vehicle is a vehicle collision, routing information for the vehicle may be received that identifies previous locations of the vehicle. The routing information may then be compared with the telematics information to identify a location of the vehicle collision.

Abstract: The present application discloses systems, methods, and computer-readable media that can utilize a handheld-movement-detection model to detect whether a computing device is moved by hand or otherwise by a person within a vehicle. For instance, the disclosed systems can receive movement data from a computing device and generate filtered signals. Subsequently, the disclosed systems can utilize the handheld-movement-detection model to convert the filtered signals into a binary movement-classification signal (based on a signal threshold) to indicate the presence of handheld movement of a device. Furthermore, the disclosed systems can also utilize movement data from a computing device to detect whether the computing device is mounted and/or to detect vehicular movements.

Abstract: This disclosure describes a vehicle-motion-analysis system that can align axes for a provider device and a corresponding transportation vehicle based on the provider device's location and motion data as a basis for generating driving-event scores for particular driving events. In particular, the disclosed systems can generate axes-rotation parameters that align axes of a provider device with axes of a transportation vehicle. In addition, the disclosed systems can identify motion paths, motion patterns, or other driving behaviors that occur during particular driving events. Further, the disclosed systems can generate driving-event scores for such driving events and can customize a graphical user interface based on the driving-event scores to reflect a provider rating and/or a location rating.

Abstract: Methods and systems for processing telematics data are provided. In one embodiment, a method is provided that includes obtaining telematics information from a first device associated with a vehicle. The telematics information may indicate operations of the vehicle. An anomalous operation of the vehicle may then be determined based on the telematics information. The method may further include determining whether the anomalous operation of the vehicle is a vehicle collision. If the anomalous operation of the vehicle is a vehicle collision, routing information for the vehicle may be received that identifies previous locations of the vehicle. The routing information may then be compared with the telematics information to identify a location of the vehicle collision.