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Deformable Dynamic Convolution Improves Traffic Prediction Efficiency

Deformable Dynamic Convolution Improves Traffic Prediction Efficiency

The Deformable Dynamic Convolutional Network (DDCN) matches or exceeds graph models on four traffic datasets and retains inference speed similar to standard CNNs. Read more: getnews.me/deformable-dynamic-convo... #trafficprediction #deeplearning

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Fig. 1. Architecture of the deep learning model used in this study for traffic forecasting.

Fig. 1. Architecture of the deep learning model used in this study for traffic forecasting.

#COMMTR online: Exciting new research on making traffic forecasting models more interpretable! 🚗
Rushan Wang, Yanan Xin, and their team developed a framework using counterfactual explanations to enhance #DLmodels.
👉 Check it out: buff.ly/1YI3ygG
#AI #TrafficPrediction

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Self-driving startup Waabi introduces Copilot4D, a generative AI model trained on extensive lidar data to accurately forecast traffic flow 5-10 seconds ahead. Revolutionize the future of transportation with predictive technology. #Waabi #Copilot4D #SelfDriving #AI #TrafficPrediction

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