AI Summary & Key Takeaways
AI InsightThe findings indicate that the proposed alternative scheduling strategy outperforms the existing system in terms of congestion reduction, capacity utilization, and overall operational efficiency
📌 3 Key Takeaways
Abstract
In Istanbul, a metropolitan city characterized by intense traffic congestion, it is critically important to effectively meet passenger demand in order to provide a more comfortable and efficient transportation service. The Marmaray Line, as one of the most significant alternative rail systems designed to alleviate urban traffic congestion, has experienced a continuous increase in passenger capacity. Therefore, optimizing its operational performance has become essential. In this study, as an alternative to the existing timetable of the Marmaray Line, station congestion rates, train loading ratios, and passenger volume parameters were calculated using the indices defined within the study framework. Based on the projections obtained from these calculations, a new timetable was developed by incorporating fuzzy alighting and boarding rates instead of deterministic passenger flow assumptions. Subsequently, the timetable was improved through the formulation of an appropriate objective function and relevant constraints in order to obtain a superior alternative strategy vector. The proposed model was further solved using a Genetic Algorithm approach to enhance solution quality and robustness. The findings indicate that the proposed alternative scheduling strategy outperforms the existing system in terms of congestion reduction, capacity utilization, and overall operational efficiency