A method for improving the order completion frequency of online car-hailing platform

By analyzing and modeling the historical data of the online ride-hailing platform, predicting the completion status of online ride-hailing and obtaining supply and demand information, the problem of difficulty in analyzing the supply and demand situation in specific areas in the existing technology is solved, and the service quality and response capabilities of the online ride-hailing platform are improved.

CN118761483BActive Publication Date: 2025-03-28BEIJING BAILONG MAYUN TECH CO LTD
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Patent Information

Application Number
CN202410738984.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-06-07
Publication Date
2025-03-28
Estimated Expiration
2044-06-07

AI Technical Summary

Technical Problem

It is difficult for existing online ride-hailing platforms to analyze the supply and demand situation in specific areas in detail, and the completion frequency is difficult to capture the dynamic nature of the supply and demand ratio and the influence of external factors, resulting in unbalanced supply and demand.

Method used

By collecting and dividing the historical data of the online ride-hailing platform, using chi-square detection to determine the data correlation, grouping users and entering training models for training, predicting the completion of online ride-hailing orders, obtaining supply and demand information and making strategy adjustments.

Benefits of technology

It improves the response timeliness and service quality of online ride-hailing platforms, enhances the ability to analyze the supply and demand situation in specific regions, and can respond to changes in supply and demand in a timely manner.

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Abstract

The present invention discloses a method for improving the order completion frequency of an online car-hailing platform, and relates to the technical field of online car-hailing management. The method comprises the following steps: firstly, the historical data of the online car-hailing platform is collected, then the time period is divided, then the correlation of the time period data is determined by using the chi-square test, then the users are grouped, the quality of the clustering is evaluated by using the silhouette coefficient, and the processed data is divided into a training set and a test set according to the time series, and the training set is input into the training model for training, and after the training, the prediction and analysis of the order completion of the online car-hailing is performed through the model, the supply and demand information generated during the analysis process is obtained, and the accuracy of the prediction of the order completion of the online car-hailing is determined according to the supply and demand information, so as to make timely strategic adjustments to the online car-hailing, increase the timeliness of the response, and improve the service quality of the online car-hailing platform.
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