System and method for recommending bidding bundle options in bidding-based ridesharing

Patent metadata

Field	Value
Publication number	US-2021201214-A1
Application number	US-202017062386-A
Country	US
Kind code	A1
Filing date	Oct 2, 2020
Priority date	Dec 31, 2019
Publication date	Jul 1, 2021
Grant date	—

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bidding-based ridesharing are described. One exemplary method includes: obtaining a price range of a trip request for a rider; determining a plurality of trip setting candidates based on the trip request, and a plurality of price candidates based on the price range; generating a plurality of bidding bundle option candidates based on a plurality of combinations of the plurality of trip setting candidates and the plurality of price candidates; determining, based on a trained machine-learning classifier, a selection probability for the rider to select each of the plurality of bidding bundle option candidates; and ranking the plurality of bidding bundle option candidates based on corresponding probabilities; and transmitting one or more of the plurality of bidding bundle option candidates with top selection probabilities to a terminal device associated with the rider.

First claim

What is claimed is:

1 . A method for bidding bundle option recommendation for ridesharing, the method comprising:
obtaining, by a computing device of a ridesharing platform, a price range of a trip request for a rider; determining, by the computing device, a plurality of trip setting candidates based on the trip request, and a plurality of price candidates based on the price range; generating, by the computing device, a plurality of bidding bundle option candidates based on a plurality of combinations of the plurality of trip setting candidates and the plurality of price candidates; determining, by the computing device based on a trained machine-learning classifier, a selection probability for the rider to select each of the plurality of bidding bundle option candidates, wherein
for each of the plurality of bidding bundle option candidates, the trained machine-learning classifier accepts input comprising at least one of the following: information of the rider, the bidding bundle option candidate, trip attributes of a hypothetical trip configured based on the bidding bundle option candidate and generates output comprising the selection probability for the rider to select the bidding bundle option candidate; and
ranking, by the computing device, the plurality of bidding bundle option candidates based on corresponding probabilities; and transmitting one or more of the plurality of bidding bundle option candidates with top selection probabilities to a terminal device associated with the rider.

2 . The method of claim 1 , wherein the obtaining a price range comprises:
obtaining the price range input from the terminal device associated with the rider.

3 . The method of claim 1 , wherein the price range is learned from historical trip requests from the rider and other riders sharing a plurality of rider features with the rider.

4 . The method of claim 1 , further comprising:
training the machine-learning classifier based on a plurality of historical trips taken by the rider, wherein each of the plurality of historical trips comprising a first historical bidding bundle option candidate selected by the rider and one or more second historical bidding bundle option candidates offered to but not selected by the rider.

5 . The method of claim 4 , wherein the training the machine-learning classifier based on a plurality of historical trips taken by the rider comprises:
training the machine-learning classifier with the first historical bidding bundle option candidate as a positive sample, and the one or more second historical bidding bundle option candidates as negative samples.

6 . The method of claim 1 , wherein information of the rider comprises at least one of the following:
origins of the rider's historical trips; destinations of the rider's historical trips; temporal information of the rider's historical trips; estimated income level; and the rider's historical preference over different trip settings.

7 . The method of claim 1 , further comprising:
determining the estimated income level of the rider with a machine learning model based on at least one of the following: one or more addresses associated with the rider and a plurality of historical trips taken by the rider.

8 . The method of claim 1 , wherein the determining a plurality of trip setting candidates based on the trip request comprises:
determining one or more settings for each of the plurality of trip options, wherein the trip options comprise at least one of the following: a carpool option, a pickup location option, a vehicle type option, a vehicle capacity option, and a car seat option.

9 . The method of claim 8 , wherein the one or more settings of the pickup location option are determined based on the origin in the trip request.

10 . The method of claim 1 , wherein the trip attributes comprise at least one of the following:
an estimated waiting time; an estimated time of arrival (ETA); a pickup distance; and temporal information.

11 . The method of claim 1 , wherein the machine-learning classifier is trained as one of the following models: Logistic Regression (LR), Random Forest (RF), and Deep Neural Network (DNN).

12 . A system comprising one or more processors and one or more non-transitory computer-readable memories coupled to the one or more processors, the one or more non-transitory computer-readable memories storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
obtaining a price range of a trip request for a rider; determining a plurality of trip setting candidates based on the trip request, and a plurality of price candidates based on the price range; generating a plurality of bidding bundle option candidates based on a plurality of combinations of the plurality of trip setting candidates and the plurality of price candidates; determining, based on a trained machine-learning classifier, a selection probability for the rider to select each of the plurality of bidding bundle option candidates, wherein for each of the plurality of bidding bundle option candidates, the trained machine-learning classifier accepts input comprising information of the rider, the bidding bundle option candidate, trip attributes of a hypothetical trip configured based on the bidding bundle option candidate and generates output comprising the selection probability for the rider to select the bidding bundle option candidate; and ranking the plurality of bidding bundle option candidates based on corresponding probabilities; and transmitting one or more of the plurality of bidding bundle option candidates with top selection probabilities to a terminal device associated with the rider.

13 . The system of claim 12 , wherein the operations further comprise:
training the machine-learning classifier based on a plurality of historical trips taken by the rider, wherein each of the plurality of historical trips comprising a first historical bidding bundle option candidate selected by the rider and one or more second historical bidding bundle option candidates offered to but not selected by the rider.

14 . The system of claim 13 , wherein the training the machine-learning classifier based on a plurality of historical trips taken by the rider comprises:
training the machine-learning classifier with the first historical bidding bundle option candidate as a positive sample, and the one or more second historical bidding bundle option candidates as negative samples.

15 . The system of claim 12 , wherein the operations further comprise:
determining the estimated income level of the rider with a machine learning model based on at least one of the following: one or more addresses associated with the rider and a plurality of historical trips taken by the rider.

16 . The system of claim 12 , wherein the trip attributes comprise at least one of the following:
an estimated waiting time; an estimated time of arrival (ETA); a pickup distance; and

17 . A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
obtaining a price range of a trip request for a rider; determining a plurality of trip setting candidates based on the trip request, and a plurality of price candidates based on the price range; generating a plurality of bidding bundle option candidates based on a plurality of combinations of the plurality of trip setting candidates and the plurality of price candidates; determinin

Assignees

• Beijing Didi Infinity Technology & Dev Co Ltd

Inventors

• Tang Liang
• Tan Bo

Classifications

• G06Q10/02 Primary

  Reservations, e.g. for tickets, services or events · CPC title

• G06F18/24323

  Tree-organised classifiers · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N20/00

  Machine learning · CPC title