Lyft, Inc.

Snir Kodesh

Dor Levi

Jatin Chopra

Asif Haque

Limin Shen

Charlie Lin

Zeynep Erkin Baz

Kevin Fan

Yuanyuan Pao

Ricky Chachra

Thaddeus Insuk Hwang

Andrew Li

Naomi Yarin

Desmond Mawuko Torkornoo

Rob Farmer

Matthew Ezra Leventi

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.

Dynamic geolocation optimization of pickup locations using location scores

Disclosed is a method for identifying, in real time, a transportation arrangement between a requestor and a provider that could benefit from a re-matching of the requestor with another provider. A system may match a provider with a requestor to complete a request for transportation from the requestor. The system may monitor a progress of the provider to a pickup location as specified in the request. Based on the monitored progress, the system may determine if the provider is making sufficient progress towards the pickup location. In some examples, the system may determine that the matching of the provider with the requestor is eligible for cancellation because the provider is not making sufficient progress towards the pickup location. The system may cancel the matching and then match another provider with the requestor to continue to make progress towards completing the transportation request.

Systems and methods for transport cancellation using data-driven models

Embodiments provide techniques, including systems and methods, for determining an estimated target pickup location for a corresponding transport request at a particular location, such as associated with a particular geohash. A requestor may send a request that is associated with a location that does not reflect the requestor's intent regarding where they would like to be met by the provider (i.e., 'picked up'). GPS inaccuracies may cause the request location to inaccurately indicate where the requestor will be; for example, the request location may be inside a building while the requestor is waiting on a curb around a far side of the building. The target pickup location allows for a requestor and a provider to meet more efficiently, reducing delay for the provider and improving the efficiency of the system by preventing provider system resources from being taken from other service areas and decreasing provider downtime upon matching.

Geohash-related location predictions

Embodiments provide techniques, including systems and methods, for determining curb segments within a threshold boundary of a transport request location, which when evaluated with respect to travel time, among other factors, result in travel efficiencies over the original transport request location. For example, a curb identification system uses building perimeter edges and road parameters to determine curb segments, which are stored as curb segment data. The curb segment data is used to identify alternate request locations corresponding to an original request location, such as being with a radius or boundary. A time for a requestor to travel from the original request location to the alternate request location is evaluated with regard to travel time.

Dynamic geolocation optimization of pickup paths using curb segment data

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San Francisco, CA, United States of America

Snir Kodesh