An Adaptive Framework for Optimized Passenger Flow in Urban Public Transportation Networks Using Deep Learning

Intelligent transportation system (ITS) plays a critical role in modernizing the transportation network. However, practical applications of ITS itself pose a multitude of technological hurdles to address. Especially with the rapid rate of worldwide urbanization, the volume of vehicles has increased significantly. Transport operators always seek out solutions to cope with this issue without affecting their timely service and minimizing their operating costs simultaneously. This research aims to design an adaptive and optimized passenger flow framework to achieve both objectives, which comprises three phases: passenger counting, passenger flow forecasting, and vehicle scheduling.