【AAAI 2020】RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework
RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework 【前言】城市計算領域中，智能交通、智慧出行一直是一備受關注的話題，而交通事故在交通中扮演愈來愈着重要的角色，據WHO統計，已逐漸成爲人類第8大殺手。傳統的基礎交通動態元素流量、速度預測等不能徹底等同於事故預測，由於事故分佈更爲零星，影響它的因素也更爲
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相關文章
- 1. 【AAAI 2020】RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework
- 2. 【論文筆記】A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction
- 3. 《Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting》
- 4. STGCN:Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastin
- 5. 城市計算，交通預測，流量預測，需求預測彙總
- 6. A Gated Dilated Causal Convolution Based Encoder-Decoder for Network Traffic Forecasting
- 7. 論文筆記《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》
- 8. Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting總結
- 9. LC-RNN: A Deep Learning Model for Traffic Speed Prediction
- 10. 【CMU & AWS 2020】Forecasting Big Time Series: Theory and Practice（Part I）