A shared bicycle scheduling method based on station clustering

Through the shared bicycle scheduling method based on site clustering, the problems of vehicle shortage and surplus are solved, resource distribution is optimized, computing efficiency and scheduling efficiency are improved, and efficient resource utilization is achieved.

CN115964647BActive Publication Date: 2025-09-16THE CHINESE UNIV OF HONG KONG (SHENZHEN)
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Patent Information

Application Number
CN202211678229.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-12-26
Publication Date
2025-09-16
Estimated Expiration
2042-12-26

AI Technical Summary

Technical Problem

Existing shared bicycle scheduling methods fail to effectively solve the problems of vehicle shortage and surplus, resulting in uneven resource distribution, and the global optimization calculation cost is too high and inefficient.

Method used

A shared bicycle scheduling method based on site clustering is adopted. By dividing the system into multiple planning periods, the sites are clustered using a clustering algorithm, and cluster adjustments are made based on distance and average loss to form an independent intra-cluster scheduling plan. The vehicle scheduling is optimized by combining geographic location and user flow information.

Benefits of technology

It effectively reduces the losses caused by uneven resource distribution, improves overall computing efficiency, and improves vehicle scheduling efficiency and resource utilization.

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Abstract

The present invention discloses a shared bicycle scheduling method based on site clustering, comprising the following steps: S1. Dividing the operating time period of a shared bicycle system into multiple planning periods; S2. Constructing a shared bicycle system model for the tth planning period; S3. Clustering the sites of the shared bicycle system using a clustering algorithm within the tth planning period; S4. Performing scheduling planning for shared bicycles within each cluster formed by clustering within the tth planning period; S5. Repeating steps S2 to S4 within different planning periods to complete the scheduling planning for shared bicycles within each planning period. The present invention utilizes a dynamic vehicle scheduling planning method that considers both bicycle shortages and surpluses, and a clustering algorithm that jointly considers geographic location and user traffic, thereby reducing losses caused by uneven resource distribution while improving overall computing efficiency.
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