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Abstract
Due to high traffic density and traffic flow in the Marine Transportation System (MTS), vessels confront long wait times at the anchorage areas and terminals, causing various issues. In this study, the traffic conditions in waterways and channels were analyzed to investigate the long waiting times and challenges. An AIS-based algorithm was developed to extract traffic features, including average travel speed (ATS), traffic density (TD), traffic flow (TF), trip attraction (TA), trip generation (TG), and origin-destination (O-D). The methodology was examined on the Houston Ship Channel (HSC) as a real case study. As a result, traffic features for deep-draft and low-draft vessels are demonstrated, and based on these results, traffic conditions in the channel are discussed.
Varying factors, such as waterway navigation restrictions, intensify traffic delays in waterways. Quantifying the delay related to each of these restrictions helps decision-makers prioritize the expansion projects. Since width restriction significantly impacts narrow waterways’ navigation, the system’s traffic delay due to width restrictions was also quantified. First, an algorithm was developed to quantify delay times and the number of impacted vessels due to each vessel transit in a restricted section. Next, procedures were presented to determine parameters such as destination docks and vessel arrival and departure times based on the AIS and marine pilot data. Finally, the models and the solution algorithms were applied to three restricted sections of the Houston Ship Channel.
We also developed a scheduling optimization model as an improvement method to enhance waterway utilization and decrease vessels delay during their journey. Thus, an optimization model was expanded to determine the optimal vessel scheduling at waterways to optimize the channel transportation efficiency and minimize the traffic delay. A mathematical model was developed to minimize the total traffic delay considering the most critical channel restrictions. Also, a Genetic Algorithm (GA) was used to solve this optimization model. The exact results were developed for the small size of the problem to be considered as a benchmark for analyzing the quality of GA’s results. The results showed that this methodology could help the system use the best possible scheme to minimize the delay.
