The invention discloses an urban traffic flow prediction method based on deep learning. The technical scheme of multi-source heterogeneous traffic data fusion and multi-scale spatial-temporal feature extraction is adopted. Comprising the following steps: acquiring multi-source traffic data through a vehicle-mounted GPS, road test equipment and a traffic monitoring video, performing data cleaning, space-time alignment and feature extraction, and performing fusion by adopting methods such as Kalman filtering and the like to obtain unified traffic state characterization; constructing a network model comprising three LSTM units with different scales, and modeling short-term, medium-term and long-term change characteristics of traffic flow by taking 5-10 minutes, 1 day and 1 week as time steps respectively; an Attention mechanism is introduced to carry out adaptive fusion on the multi-scale features; mapping various external environment data into low-dimensional feature vectors through representation learning, and introducing the low-dimensional feature vectors into the model; a knowledge distillation technology compression model is adopted, online service is deployed, and traffic flow prediction of the next one hour is generated every 5-10 minutes. The method improves the accuracy and real-time performance of traffic flow prediction, and has a good engineering application value.
基于深度学习的城市交通流量预测方法,采用多源异构交通数据融合和多尺度时空特征提取的技术方案。包括以下步骤:通过车载GPS、路测设备、交通监控视频获取多源交通数据,经数据清洗、时空对齐、特征提取后,采用卡尔曼滤波等方法融合得到统一的交通状态表征;构建包含三个不同尺度LSTM单元的网络模型,分别以5‑10分钟、1天和1周为时间步长,建模交通流量的短期、中期和长期变化特征;引入Attention机制对多尺度特征进行自适应融合;将多种外部环境数据通过表示学习映射为低维特征向量并引入模型;采用知识蒸馏技术压缩模型,部署为在线服务,每5‑10分钟生成未来1小时的交通流量预测。本发明提升了交通流量预测的准确性和实时性,具有良好的工程应用价值。
Urban traffic flow prediction method based on deep learning
基于深度学习的城市交通流量预测方法
2025-03-21
Patent
Electronic Resource
Chinese
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