主体流量确定方法、电子设备、存储介质及程序产品
By integrating multi-source traffic data and feedback correction mechanisms, the problems of data dependence and low computational efficiency in road segment traffic flow estimation in existing technologies have been solved, enabling accurate traffic flow estimation in diverse traffic scenarios and enhancing the support for traffic management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING TRANSPORTATION RES CENT
- Filing Date
- 2026-04-02
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies suffer from problems such as sparse observation points, strong model underdeterminism, low computational efficiency, and high data dependence when determining traffic flow on road segments, making it difficult to meet the needs of real-time analysis. Furthermore, deep learning-based methods have weak generalization ability in diverse traffic scenarios.
By integrating multi-source traffic data, identifying associated paths and calculating the main body traffic flow, and combining historical travel data, mobile signaling data, and offline survey data, the total travel volume of the main body and the inter-regional departure ratio are determined using multi-source data. Combined with the route selection ratio, the accurate estimation of the main body traffic flow of associated paths is achieved, and feedback correction is performed through traffic flow monitoring.
It improves the accuracy and reliability of traffic flow estimation, reduces reliance on large-scale sample data, is suitable for data-scarce scenarios, and enhances support for traffic management.
Smart Images
Figure CN121963500B_ABST