Transit Network

Transit network design aims to optimize public transportation systems for efficiency, fairness, and cost-effectiveness. Current research emphasizes the use of advanced algorithms, including evolutionary algorithms, deep reinforcement learning with graph neural networks, and integer linear optimization, to improve network planning and ridership prediction. These methods leverage data from automatic passenger counting (APC) and automated fare collection (AFC) systems to enhance model accuracy and inform decisions about route optimization and resource allocation. Improved transit network design has significant implications for urban planning, reducing travel times, improving accessibility, and lowering operational costs.