LC-RNN: A Deep Learning Model for Traffic Speed Prediction
1.背景 下面所介紹是2018年發表在IJCAI上的一論文，其主要解決的問題是預測接下來幾個時間間隔內，某條道路上車輛行駛的平均速度。其關鍵點在於作者提出了用"look-up"這一方法來構造由每條道路及其相連接道路所形成的速度矩陣，接着用CNN來提取相鄰「道路速度」的空間依賴關係，然後進一步採用LSTM從時間序列的角度來提取特徵；最後融合天氣和週期信息等最爲整個網絡的輸出。 我們知道在實際情況中，
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相關文章
- 1. A deep tree-based model for software defect prediction
- 2. 【論文筆記】A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction
- 3. A Vision for Making Deep Learning Simple
- 4. Wide & Deep Learning Model
- 5. STGCN:Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastin
- 6. Deep learning: prevent overfitting && speed up training
- 7. A Deep Reinforced Model for Abstractive Summarization
- 8. [Reinforcement Learning] Model-Free Prediction
- 9. Long short-term memory neuralnetwork for traffic speed prediction using remote microwave sensor data
- 10. 《Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting》