Optimizing Subway Timetables and Headways Using Image Processing Techniques

This paper investigates the optimization of subway timetables and headways through the integration of image processing and real-time data analytics, aiming to transition from static to dynamic scheduling systems. As urbanization accelerates, traditional static timetables fail to adapt to fluctuating passenger demand, resulting in inefficiencies such as prolonged waiting times, platform overcrowding, and excessive energy consumption. To address these challenges, this study proposes a dynamic scheduling framework that leverages advanced technologies, including image processing and machine learning to monitor real-time crowd density patterns on subway platforms. By combining historical and real-time data, the system predicts short-term demand fluctuations and dynamically adjusts train frequencies to minimize passenger delays, reduce energy waste, and enhance operational efficiency. The proposed framework not only improves passenger experience by reducing waiting times and overcrowding but also contributes to broader sustainability goals by optimizing energy use and reducing the environmental footprint of urban transit systems. This research highlights the potential of dynamic scheduling to create smarter, more adaptive, and sustainable public transportation networks.

Keywords:

Dynamic Scheduling , Image Processing , Real-Time Optimization , Crowd Density Monitoring , Energy Efficiency