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: 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: Embodiments provide techniques, including systems and methods, for determining alternate request locations based on a pickup location score (PLoS) of a location associated with transportation request information. A pickup location score may include an objective quantitative measurement of the fitness of a location for a pickup by a provider. For example, embodiments may receive transport request information associated with a requestor computing device including a request location, determine a modified request location based at least on a location score for each of one or more alternate request locations that are within a threshold distance of the request location, and send modified transport request information associated with the modified request location and the first requestor computing device to a provider computing device associated with a matched provider for the transport request information.

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: 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: 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: Embodiments provide techniques, including systems and methods, for determining alternate request locations based on a pickup location score (PLoS) of a location associated with transportation request information. A pickup location score may include an objective quantitative measurement of the fitness of a location for a pickup by a provider. For example, embodiments may receive transport request information associated with a requestor computing device including a request location, determine a modified request location based at least on a location score for each of one or more alternate request locations that are within a threshold distance of the request location, and send modified transport request information associated with the modified request location and the first requestor computing device to a provider computing device associated with a matched provider for the transport request information.

Abstract: Embodiments provide techniques, including systems and methods, for determining alternate request locations based on a pickup location score (PLoS) of a location associated with transportation request information. A pickup location score may include an objective quantitative measurement of the fitness of a location for a pickup by a provider. For example, embodiments may receive transport request information associated with a requestor computing device including a request location, determine a modified request location based at least on a location score for each of one or more alternate request locations that are within a threshold distance of the request location, and send modified transport request information associated with the modified request location and the first requestor computing device to a provider computing device associated with a matched provider for the transport request information.

Abstract: Embodiments provide techniques, including systems and methods, for determining alternate request locations based on a pickup location score (PLoS) of a location associated with transportation request information. A pickup location score may include an objective quantitative measurement of the fitness of a location for a pickup by a provider. For example, embodiments may receive transport request information associated with a requestor computing device including a request location, determine a modified request location based at least on a location score for each of one or more alternate request locations that are within a threshold distance of the request location, and send modified transport request information associated with the modified request location and the first requestor computing device to a provider computing device associated with a matched provider for the transport request information.