Transit Network Design

Transit network design aims to optimize public transportation routes, minimizing costs and maximizing efficiency and passenger satisfaction. Current research heavily utilizes artificial intelligence, employing algorithms like Ant Colony Optimization, deep reinforcement learning with graph neural networks, and neural-evolutionary approaches to improve route planning and scheduling, often incorporating real-time data and diverse objective functions. These advancements offer significant potential for improving urban mobility by reducing operational costs, travel times, and the number of transfers, ultimately leading to more efficient and effective public transportation systems. The integration of data-driven decision support systems further enhances the responsiveness and adaptability of transit networks to changing demands.