Abstract: The disclosed computer-implemented method may include matching transportation requests to transportation providers using a dynamic transportation matching system. Matching transportation requestors which have varying preferences for waiting times and transportation pricing with transportation providers having varying levels of available supply may involve matching methods that satisfy the transportation requestors while maintaining an appropriate level of supply. A method which provides immediate service for transportation requestors that may be willing to pay a premium fare while placing transportation requestors not willing to pay a premium fare and may be willing to wait may provide a balance of supply and demand of transportation providers. Accordingly, immediately matching transportation requestors not willing to wait while placing the transportation requestors willing to wait in a queue may provide the balanced approach to managing transportation provider availability.

Abstract: The disclosed computer-implemented method may include matching transportation requests to transportation providers using a dynamic transportation matching system. Matching transportation requestors which have varying preferences for waiting times and transportation pricing with transportation providers having varying levels of available supply may involve matching methods that satisfy the transportation requestors while maintaining an appropriate level of supply. A method which provides immediate service for transportation requestors that may be willing to pay a premium fare while placing transportation requestors not willing to pay a premium fare and may be willing to wait may provide a balance of supply and demand of transportation providers. Accordingly, immediately matching transportation requestors not willing to wait while placing the transportation requestors willing to wait in a queue may provide the balanced approach to managing transportation provider availability.

Abstract: The present disclosure relates assigning ride requests to providers based on the probability that a provider will accept the request. For example, one or more embodiments identify a first provider to assign the ride request. The system then generates a probability of acceptance for that provider. The system then determines an estimated time-to-arrival for the first provider and an alternate estimated time-to-arrival associated with a re-assigned provider. Based on the acceptance probability, the estimated time-to-arrival, and the alternate estimated time-to-arrival, the system then determines an expected time-to-arrival associated with the first provider. The system assigns ride requests to providers based on optimizing the expected time-to-arrival across multiple potential providers for a ride request.

Abstract: The disclosed computer-implemented method may include matching transportation requests to transportation providers using a dynamic transportation matching system. Matching transportation requestors which have varying preferences for waiting times and transportation pricing with transportation providers having varying levels of available supply may involve matching methods that satisfy the transportation requestors while maintaining an appropriate level of supply. A method which provides immediate service for transportation requestors that may be willing to pay a premium fare while placing transportation requestors not willing to pay a premium fare and may be willing to wait may provide a balance of supply and demand of transportation providers. Accordingly, immediately matching transportation requestors not willing to wait while placing the transportation requestors willing to wait in a queue may provide the balanced approach to managing transportation provider availability.

Abstract: The present disclosure relates assigning ride requests to providers based on the probability that a provider will accept the request. For example, one or more embodiments identify a first provider to assign the ride request. The system then generates a probability of acceptance for that provider. The system then determines an estimated time-to-arrival for the first provider and an alternate estimated time-to-arrival associated with a re-assigned provider. Based on the acceptance probability, the estimated time-to-arrival, and the alternate estimated time-to-arrival, the system then determines an expected time-to-arrival associated with the first provider. The system assigns ride requests to providers based on optimizing the expected time-to-arrival across multiple potential providers for a ride request.