Transit Network Timetabling: A Literature Review

Transit Network Timetabling (TNT) optimizes public transportation operations by increasing service efficiency, passenger satisfaction, and environmental sustainability. Recent complex synchronization requirements and rapid developments in electrification and autonomous vehicle technologies, however, have made it important to perform an updated analysis of this vital step of transit planning. To this end, a structured examination was conducted of 34 peer-reviewed papers published after 2021 that focus on novel optimization methodologies, constraint formulations, and dynamic modeling techniques advancements. The literature was categorized by objective functions, modeling approaches, and employed solution methods, and the results showed that although the use of Mixed-Integer Linear Programming (MILP) remains prevalent due to its effectiveness in modeling discrete operational constraints, there is a growing interest in metaheuristic, hybrid, and reinforcement learning-based approaches for navigating computations complexity and facilitating real-time decision-making. This review identifies critical research gaps, particularly in relation to integrating real-time scheduling adjustments and comprehensive stakeholder considerations, and recommends that future studies explore scalable, data-driven frameworks that simultaneously address dynamic passenger demands, vehicle fleet optimization, and sustainability targets. The results of this review provide a roadmap for policymakers and transport planners, to help them implement robust, efficient, and environmentally responsive timetabling practices.