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US 2020/0394740 A1Published application
Identifying high risk trips using continuous call sequence analysis
AssigneeDiDi Research America, LLC
InventorsZihan YI, Xin CHEN, Dong LI, Jing CHEN
Life of this patent
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Abstract
After receiving a trip request, a ridesharing platform system can determine scores for each prior trip request within a time window from features associated with the prior trip request. The ridesharing platform system can determine patterns from corresponding scores and determine a risk for the trip request using on the patterns. The ridesharing platform system can determine whether to accept or decline the trip request based on the risk for the trip request.
Claims20
A system for identifying a high risk trip, the system comprising:
- one or more processors;
- and
- a memory storing instructions that, when executed by the one or more processors, cause the system to perform:
- receiving a current trip request by a passenger, wherein the passenger is associated with a plurality of prior trip requests within a prior time window, wherein each of the plurality of prior trip requests is associated with a plurality of features comprising a plurality of time window-related features, a plurality of prior driver features, a plurality of passenger features, and a prior trip outcome, and wherein the prior trip outcome indicates a positive trip outcome status or a negative outcome status;
- generating a plurality of scores for each of the plurality of prior trip requests from the plurality of features associated with the prior trip request;
- determining a plurality of patterns from the corresponding scores of the plurality of prior trip requests using a time series technique;
- determining a risk for the current trip request from the plurality of patterns using a machine learning model, wherein the machine learning model is trained using a training dataset generated from an original dataset, and wherein the training dataset comprises a plurality of training trip requests having a higher occurrence rate of training trip requests with positive trip outcome status than the original dataset;
- determining the risk for the current trip request is below a trip risk threshold;
- and
- assigning a driver to the current trip request.
7 more elements
A system for identifying a high risk trip, the system comprising:
- one or more processors;
- and
- a memory storing instructions that, when executed by the one or more processors, cause the system to perform:
- receiving a current trip request by a passenger, wherein the passenger is associated with a plurality of prior trip requests within a prior time window, wherein each of the plurality of prior trip requests is associated with a plurality of features comprising a time window profile, a prior driver profile, a passenger profile, and a prior trip outcome;
- generating a plurality of scores for each of the plurality of prior trip requests from the plurality of features associated with the prior trip request;
- determining a plurality of patterns from the corresponding scores of the plurality of prior trip requests;
- determining a risk for the current trip request from the plurality of patterns;
- and
- assigning a current driver to the current trip request based on the risk of the current trip request relative to a trip risk threshold.
6 more elements
A method for identifying a high risk trip, the method comprising:
- under control of one or more processors:
- receiving a current trip request by a passenger, wherein the passenger is associated with a plurality of prior trip requests within a prior time window, wherein each of the plurality of prior trip requests is associated with a plurality of features comprising a time window profile, a prior driver profile, a passenger profile, and a prior trip outcome;
- generating a plurality of scores for each of the plurality of prior trip requests from the plurality of features associated with the prior trip request;
- determining a plurality of patterns from the corresponding scores of the plurality of prior trip requests;
- determining a risk for the current trip request from the plurality of patterns;
- and
- assigning a current driver to the current trip request based on the risk of the current trip request relative to a trip risk threshold.
4 more elements
Dependencies are inferred from the claim text.