Abstract: The present application discloses systems, methods, and computer-readable media that utilize computing devices to model multi-outcome transportation-value metrics that account for spatio-temporal trajectories across locations, times and other contextual features, and then utilize computer networks to dispatch provider devices to locations based on the multi-outcome transportation-value metrics. Moreover, the disclosed systems can utilize these multi-outcome transportation-value metrics and/or other models to manage and utilize dynamic transportation dispatch modes to more efficiently align provider devices and requestor devices across computer networks. For instance, the disclosed system can dispatch a provider device based on a discounted multi-outcome transportation-value metric.

Abstract: The present application discloses systems, methods, and computer-readable media that utilize computing devices to model multi-outcome transportation-value metrics that account for spatio-temporal trajectories across locations, times and other contextual features, and then utilize computer networks to dispatch provider devices to locations based on the multi-outcome transportation-value metrics. Moreover, the disclosed systems can utilize these multi-outcome transportation-value metrics and/or other models to manage and utilize dynamic transportation dispatch modes to more efficiently align provider devices and requestor devices across computer networks. For instance, the disclosed system can dispatch a provider device based on a discounted multi-outcome transportation-value metric.

Abstract: Methods and systems for correcting errors in transportation routes are provided. In one embodiment, a method is provided that includes receiving a transportation request that includes at least two locations. A first route prediction may be generated based on a first set of previously-completed routes associated with the at least two locations. A first predictive model may compare the first route prediction with route information associated with a second set of previously-completed routes identified based on the first route prediction. A second route prediction may be generated based on the comparison and may be sent to a mobile device for presentation to a user.

Abstract: Methods and systems for correcting errors in transportation routes are provided. In one embodiment, a method is provided that includes receiving a transportation request that includes at least two locations. A first route prediction may be generated based on a first set of previously-completed routes associated with the at least two locations. A first predictive model may compare the first route prediction with route information associated with a second set of previously-completed routes identified based on the first route prediction. A second route prediction may be generated based on the comparison and may be sent to a mobile device for presentation to a user.

Abstract: The present application discloses systems, methods, and computer-readable media that utilize computing devices to model multi-outcome transportation-value metrics that account for spatio-temporal trajectories across locations, times and other contextual features, and then utilize computer networks to dispatch provider devices to locations based on the multi-outcome transportation-value metrics. Moreover, the disclosed systems can utilize these multi-outcome transportation-value metrics and/or other models to manage and utilize dynamic transportation dispatch modes to more efficiently align provider devices and requestor devices across computer networks. For instance, the disclosed system can dispatch a provider device based on a discounted multi-outcome transportation-value metric.

Abstract: Methods and systems for correcting errors in transportation routes are provided. In one embodiment, a method is provided that includes receiving a transportation request that includes at least two locations. A first route prediction may be generated based on a first set of previously-completed routes associated with the at least two locations. A first predictive model may compare the first route prediction with route information associated with a second set of previously-completed routes identified based on the first route prediction. A second route prediction may be generated based on the comparison and may be sent to a mobile device for presentation to a user.