Optimization of Railway Transportation Planning by Combining TST Model and Genetic Algorithm

Abstract

Railway transportation, a key long - distance freight transport method, faces challenges due to the rapid growth of global logistics demand. These challenges include high transportation costs, low punctuality rates, and inefficient resource utilization. Traditional static optimization methods cannot adapt to dynamic changes and multi-objective optimization requirements. The study proposes an integrated method that combines the Temporal-Spatial Tunnel (TST) model with the Genetic Algorithm (GA). The TST model describes railway transportation changes dynamically by integrating temporal and spatial dimensions. The GA uses its global search ability to optimize train routing and timetabling. The proposed method enhances the efficiency and flexibility of the railway transportation system. It addresses the issues of low punctuality, inefficient resource utilization, and lack of adaptability to dynamic changes and multi - objective optimization in traditional methods. Experimental results show the superiority of this approach. In urban network scenarios, it achieves a punctuality rate of 94.87%, resource utilization of 89.78%, and a response time of 280.12 seconds. In freight - priority scenarios, the maximum punctuality rate reaches 95.45%. Compared to traditional methods, it significantly improves transportation efficiency and flexibility in multi - objective optimization, offering an effective solution for railway transportation planning under dynamic demands and valuable references for logistics system scheduling optimization.