Improving transit network resilience against disruptions through path redundancy

Public transport systems are vulnerable to natural disasters, accidents, or deliberate attacks, that can cause infrastructure damage and service disruptions. Disruption impacts depend on the network structure and the availability of alternative travel paths, highlighting the importance of path redundancy in public transport network planning. Addressing the associated research gap, we propose a practice-oriented path redundancy indicator and integrate it within a novel hybrid metaheuristic solution framework to design more resilient route structures from scratch. The approach combines Reinforcement Learning, Local Search operators and Particle Swarm Optimization and is validated using an established benchmark from the literature and a real-world network from Uruguay, generating more resilient networks that serve up to 25 % and 40 % more passengers in random and targeted attacks, respectively. Results show that resilience against link failures can be enhanced through path redundancy without adversely impacting average travel times.