Predictive Modeling for Enhanced Truck Parking Information Systems Using Machine Learning

The growing demand for truck freight in the United States has intensified the shortage of truck parking, posing safety and operational challenges. While real‐time Truck Parking Information and Management Systems (TPIMSs) offer current availability, predictive insights remain limited. This study develops hybrid machine learning and deep learning models to forecast truck parking utilization for both pretrip and en‐route decision‐making. A site‐specific gradient boosting model achieved the best pretrip performance (average root mean square error [RMSE] = 0.154), while a long short–term memory–based truck parking site utilization prediction (TPSUP) model provided accurate en‐route predictions (RMSE = 0.0429) with a one‐hour horizon. To enhance usability, a “Popular Times” panel was designed to visualize predictions through intuitive, color‐coded charts. These tools support safer and more efficient parking decisions, laying the groundwork for a more robust and predictive TPIMS.