Abstract: 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.

Abdul Basit Ali Shah, Xinglu Xu, Zheng Yongren and Zijian Guo, “A Custom Deep Learning Approach for Traffic Flow Prediction in Port Environments: Integrating RCNN for Spatial and Temporal Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160266

@article{Shah2025,
title = {A Custom Deep Learning Approach for Traffic Flow Prediction in Port Environments: Integrating RCNN for Spatial and Temporal Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160266},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160266},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Abdul Basit Ali Shah and Xinglu Xu and Zheng Yongren and Zijian Guo}
}