The invention discloses a smart road driving time prediction method based on multi-source data fusion and deep learning, and the method comprises the steps: constructing a multi-source data collection and fusion platform, developing a spatial-temporal feature deep learning model, deploying a real-time data processing and anomaly detection system, building a self-adaptive prediction optimization mechanism, and constructing a smart traffic application service platform. Comprehensive acquisition, deep analysis, real-time processing, accurate prediction and intelligent service of traffic data are realized. The method specifically comprises the following steps: deploying an Internet of Things sensor network to collect multi-source data, and designing an adaptive weighted fusion algorithm to construct a multi-dimensional dynamic traffic database; constructing an LSTM-GNN hybrid neural network architecture to extract spatio-temporal features; real-time data processing and anomaly detection are carried out by adopting a streaming computing framework and various technologies; designing a multi-objective optimization algorithm and a reinforcement learning model to realize adaptive prediction optimization; and developing a visual decision support system and a personalized navigation service. The system effectively improves the traffic management efficiency and the public travel experience.
基于多源数据融合与深度学习智慧道路行驶时间预测方法,通过构建多源数据采集与融合平台,开发时空特征深度学习模型,部署实时数据处理与异常检测系统,建立自适应预测优化机制,以及构建智慧交通应用服务平台,实现了对交通数据的全面采集、深度分析、实时处理、精准预测和智能化服务。具体包括部署物联网传感器网络采集多源数据,设计自适应加权融合算法构建多维度动态交通数据库;构建LSTM‑GNN混合神经网络架构提取时空特征;采用流式计算框架和多种技术进行实时数据处理与异常检测;设计多目标优化算法和强化学习模型实现自适应预测优化;开发可视化决策支持系统和个性化导航服务。该系统有效提升了交通管理效率和公众出行体验。
Intelligent road driving time prediction method based on multi-source data fusion and deep learning
基于多源数据融合与深度学习智慧道路行驶时间预测方法
2025-05-13
Patent
Electronic Resource
Chinese
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