Abstract: Systems and methods are provided for predicting a traversal time for a route. A request for navigation directions is received and a route between the origin and destination is calculated. A series of route segments to be traversed on the route are identified, as well as one or more sub-series of route segments of the series of route segments, with each sub-series having route segments to be traversed consecutively on a portion of the route. For each sub-series, a sub-series traversal time is predicted based on historical traversal time data associated with historical trips where each of the route segments of the sub-series were traversed consecutively. A total route traversal time is predicted based on the sub-series traversal time. A set of navigation directions and the predicted total route traversal time are provided for presentation on a client device for navigating from the origin to the destination via the route.

Abstract: Systems and methods are provided for predicting a traversal time for a route. A request for navigation directions is received and a route between the origin and destination is calculated. A series of route segments to be traversed on the route are identified, as well as one or more sub-series of route segments of the series of route segments, with each sub-series having route segments to be traversed consecutively on a portion of the route. For each sub-series, a sub-series traversal time is predicted based on historical traversal time data associated with historical trips where each of the route segments of the sub-series were traversed consecutively. A total route traversal time is predicted based on the sub-series traversal time. A set of navigation directions and the predicted total route traversal time are provided for presentation on a client device for navigating from the origin to the destination via the route.

Abstract: A computer-implemented method for predicting speeds for a particular vehicle type includes receiving first tracking data indicative of individual speeds of first vehicles while traveling on road segments at various times, and second tracking data indicative of individual speeds of second vehicles while traveling on the same road segments at the same times. The second vehicles correspond to the particular vehicle type. The method also includes training a machine learning model to predict speeds for the particular vehicle type using a feature set based on the individual speeds indicated by the first tracking data and labels based on the individual speeds indicated by the second tracking data. The method further includes using the model to predict a speed of a vehicle (of the particular type) on a road segment, at least by processing a real-time speed estimate corresponding to other vehicles traveling on the same road segment.

Abstract: A computer-implemented method for predicting speeds for a particular vehicle type includes receiving first tracking data indicative of individual speeds of first vehicles while traveling on road segments at various times, and second tracking data indicative of individual speeds of second vehicles while traveling on the same road segments at the same times. The second vehicles correspond to the particular vehicle type. The method also includes training a machine learning model to predict speeds for the particular vehicle type using a feature set based on the individual speeds indicated by the first tracking data and labels based on the individual speeds indicated by the second tracking data. The method further includes using the model to predict a speed of a vehicle (of the particular type) on a road segment, at least by processing a real-time speed estimate corresponding to other vehicles traveling on the same road segment.