Factor Analysis of Railway Carrying Capacity Coordination Optimization Considering Energy Consumption

Abstract

Efficient utilization of railway networks is essential for sustainable transportation systems, necessitating the optimization of carrying capacity while minimizing energy consumption. This study investigates the coordination of railway carrying capacity optimization considering energy consumption through a comprehensive methodology integrating statistical analysis, optimization algorithms, and scenario analysis. Regression analysis reveals significant relationships between various factors and energy consumption, highlighting the influence of factors such as train frequency and route length. Factor analysis identifies latent factors influencing railway carrying capacity and energy consumption, providing insights into system dynamics and critical determinants of performance. Multi-objective optimization models optimize carrying capacity coordination while minimizing energy consumption, yielding Pareto-optimal solutions that balance conflicting objectives. Scenario analysis evaluates the impact of policy interventions and operational strategies on system performance, offering insights into potential pathways for enhancing efficiency and sustainability. Discussion of results emphasizes the importance of integrated planning approaches, stakeholder engagement, and knowledge exchange in addressing complex challenges facing railway systems. The study contributes to the advancement of sustainable transportation by providing actionable insights for railway management, infrastructure planning, and policy-making. By optimizing carrying capacity coordination considering energy consumption, the study aims to foster the development of resilient, resource-efficient railway networks capable of meeting the evolving demands of society while minimizing environmental impact.