Spatio-Temporal Adaptation: A Deep Learning Framework for Traffic Prediction by Integrating Convolutional Networks and Linear Embeddings

Accurate Traffic Forecasts Are Crucial for a City. Traditional Methods Overlook Spatial and Temporal Dependencies, Rendering Them Ineffective for Precise Long-Term Traffic Prediction Given the Intricate and Complex Nature of Spatio-Temporal Traffic Flow. To Tackle This Challenge, We Introduce an Innovative Approach for Predicting Transportation Flow: a Deep Learning Framework for Traffic Prediction Integrating Convolutional Networks and Linear Embeddings. Our Model Outperforms Traditional Methods in the Three Metrics of MAE, RMSE, and Goodness of Fit, Significantly Enhancing the Accuracy of Traffic Prediction. By Employing a Fully Convolutional Structure Along the Time Axis, Our Model Fully Utilizes Spatial Information and Captures the Spatio-Temporal Dependencies Inherent in Traffic Systems. This Approach Overcomes the Inherent Limitations of Recurrent Networks, Enhancing the Performance Across Various Traffic Flow Prediction Metrics.