Abstract: This disclosure covers machine-learning methods, non-transitory computer readable media, and systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area. The methods, non-transitory computer readable media, and systems dynamically adjust the multiplier with machine learners to maintain a target estimated time of arrival for a provider device to fulfill a request received from a requestor device. In some embodiments, the methods, non-transitory computer readable media, and systems generate a multiplier report comprising a representation of a geographic area and an indication of the multiplier to facilitate inflow and outflow of provider devices within and without the geographic area.

Abstract: This disclosure covers machine-learning methods, non-transitory computer readable media, and systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area. The methods, non-transitory computer readable media, and systems dynamically adjust the multiplier with machine learners to maintain a target estimated time of arrival for a provider device to fulfill a request received from a requestor device. In some embodiments, the methods, non-transitory computer readable media, and systems generate a multiplier report comprising a representation of a geographic area and an indication of the multiplier to facilitate inflow and outflow of provider devices within and without the geographic area.

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: This disclosure covers machine-learning methods, non-transitory computer readable media, and systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area. The methods, non-transitory computer readable media, and systems dynamically adjust the multiplier with machine learners to maintain a target estimated time of arrival for a provider device to fulfill a request received from a requestor device. In some embodiments, the methods, non-transitory computer readable media, and systems generate a multiplier report comprising a representation of a geographic area and an indication of the multiplier to facilitate inflow and outflow of provider devices within and without the geographic area.

Abstract: This disclosure describes a transportation matching system that utilizes a combination of an offline transportation optimization model and an online transportation optimization model to generate transportation metric functions for predicted and received transportation requests based on optimization parameters. The disclosed systems utilize an offline transportation optimization model to predict transportation requests and to generate corresponding transportation metric functions for given locations over particular time intervals. The disclosed systems further utilize an online transportation optimization model to receive transportation requests and generate transportation metric functions for the received requests based at least in part on the predicted transportation requests and corresponding metric functions.

Abstract: This disclosure covers machine-learning methods, non-transitory computer readable media, and systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area. The methods, non-transitory computer readable media, and systems dynamically adjust the multiplier with machine learners to maintain a target estimated time of arrival for a provider device to fulfill a request received from a requestor device. In some embodiments, the methods, non-transitory computer readable media, and systems generate a multiplier report comprising a representation of a geographic area and an indication of the multiplier to facilitate inflow and outflow of provider devices within and without the geographic area.