一种港口集疏运系统的交通流量智能预测方法
By constructing a twin framework and using deep reinforcement learning, the decision-making behavior of each participant in the port's collection and distribution system is simulated, solving the problems of accuracy and adaptability in traffic flow prediction in existing technologies, and realizing efficient traffic flow prediction and system optimization under emergencies and rule changes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHENGDU TONGGUANG NETLINK TECH CO LTD
- Filing Date
- 2025-11-26
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for predicting traffic flow in port collection and distribution systems lack modeling of decision-making mechanisms, leading to decreased accuracy of predictions during emergencies. Furthermore, they lack adaptability to changes in system operating rules and cannot simulate the traffic distribution after changes in prediction rules.
A twin framework is constructed, which acquires multi-source data through a data perception layer, trains an intelligent agent model, deploys it in a virtual operating environment, drives scenario evolution, generates optimization strategies through deep reinforcement learning, simulates the decision-making behavior of each participant and the system operating rules, and realizes intelligent prediction of traffic flow.
It improves the accuracy and adaptability of traffic flow forecasting, enabling accurate predictions under unforeseen events and changes in system rules, and optimizes the operational efficiency of the port's collection and distribution system.
Smart Images
Figure CN121565027B_ABST