Parking space optimization for efficient and effective ro-ro terminal operations

Efficient parking space allocation is a critical aspect of optimizing terminal operations and ensuring smooth cargo flow at ports. However, the optimization of parking space allocation in Ro-Ro terminals presents unique challenges due to the limited parking area and the inability to expand port facilities within urban environments. As demand for Ro-Ro shipping continues to increase, it becomes imperative to address the challenge of extended dwell times in order to utilize these ports more efficiently and increase terminal capacity. This Ph.D. study addresses the gap in the literature on dwell time prediction specifically for Ro-Ro ports and investigates how accurate dwell time predictions can contribute to the optimization of parking space allocation. By accurately predicting dwell times, it becomes possible to better allocate parking spaces and improve the overall efficiency of Ro-Ro terminal operations. The study begins with a comprehensive literature review, highlighting the scarcity of research on dwell time prediction in port operations. While a few studies exist for container ports and terminals, there is a notable absence of research for Ro-Ro ports. This study aims to fill this research gap and contribute to the existing literature on port operations. Real data collected from Trieste Port, encompassing various operational parameters, vessel characteristics, and temporal features, is used to develop predictive models for dwell time estimation. Multiple machine-learning algorithms are tested and compared to identify the most accurate model for dwell time prediction in the Ro-Ro terminal context. The research investigates the influence of accurate dwell time prediction on parking space optimization. Integration with optimization algorithms enables the dynamic allocation of parking spaces based on predicted dwell times, cargo characteristics, priority, and operational constraints. The proposed system aims to enhance parking space utilization, reduce congestion, and improve resource allocation efficiency at Trieste Port's Ro-Ro terminal. The study's findings contribute to the literature by addressing the scarcity of research on dwell time prediction in Ro-Ro ports. By demonstrating the importance of accurate dwell time prediction for parking space optimization, this study provides practical insights and recommendations for the optimization of parking space allocation in Ro-Ro terminals. The research outcomes serve as a valuable resource for industry professionals and researchers seeking to enhance port operations and address the unique challenges of Ro-Ro ports.