A Custom Deep Learning Approach for Traffic Flow Prediction in Port Environments: Integrating RCNN for Spatial and Temporal Analysis

Port congestion poses a significant challenge to maritime logistics, especially for industries dealing with perishable goods like seafood. This study presents a custom deep learning model using Transformer architecture to predict real-time traffic flow at the Port of Virginia, with a focus on optimizing the movement of fish trucks. The model integrates multimodal data from 36 sensors, capturing traffic flow, occupancy, and speed at five-minute intervals, and processes high-dimensional, time-series data for accurate predictions. The model utilizes attention mechanisms to capture spatial and temporal dependencies, significantly improving predictive performance. Evaluation results indicate that the Transformer-based model outperforms existing models like RandomForest, GradientBoosting, and Support Vector Regression, with an R-squared value of 0.89, Pearson correlation of 0.91, and a Root Mean Squared Error (RMSE) of 0.0208. These results suggest that the model can effectively manage dynamic port traffic and optimize resource allocation, ensuring the timely delivery of perishable goods.