AI-Driven Dynamic Subway Timetable Management: Integrating Real -Time Data and Predictive Analytics for Operational Efficiency

Urban subway systems face mounting challenges in meeting the dynamic demands of growing populations while ensuring efficiency and passenger satisfaction. This paper presents an innovative platform that dynamically adjusts subway timetables using real-time passenger data and advanced analytics. By continuously gathering e-ticket information, the system monitors entry flows and identifies patterns in ridership. This data is processed using artificial intelligence to predict passenger densities, anticipate congestion, and factor in external influences such as weather and special events. A robust backend integrates these insights with platform-specific policies, historical patterns, and socioeconomic considerations to adjust train intervals automatically. The system also incorporates adaptive strategies like demand-based ticket pricing and energy-efficient scheduling to optimize operations further. Case studies demonstrate significant improvements in reducing wait times, preventing overcrowding, and conserving energy. This scalable, data-driven approach exemplifies the potential of intelligent transit systems to transform urban mobility and address the complexities of modern city life.

کلیدواژه ها:

Subway timetable optimization ، Dynamic scheduling ، Real-time passenger data ، Travel pattern analysis