Robust Optimization of Train Timetabling under Operational Disruptions to Reduce Passenger Waiting Time in Tehran Metro

Document Type : Original Article

Authors
pejman salehi
mehran khalaj
davoud jafari
parisa Mousavi Ahranjani
Department of Industrial Engineering, Parand and Robat Karim Branch, Islamic Azad University, Parand, Iran
10.22034/road.2025.551940.2436
Abstract
Operational disruptions in urban rail transit systems can significantly impair service performance, causing overcrowding, passenger confusion, and unscheduled train halts. This study introduces an innovative strategy based on short-turn operations to mitigate such impacts and enhance service quality during disruption periods. An optimization model is developed that accounts for train safety constraints, fleet capacity, and dynamic passenger demand, aiming to minimize total passenger waiting time. The model enables rapid generation of alternative train schedules under abnormal conditions, improving travel efficiency through coordinated dispatching and optimal resource allocation.

To solve the model, an Improved Genetic Algorithm (IGA) is proposed and applied to a case study on Line 4 of the Tehran Metro. Results show that the approach maintains train headways between 3–5 minutes, ensuring operational safety and better alignment with demand fluctuations. This leads to reduced platform waiting times and improved overall service levels. Comparative analysis with a conventional genetic algorithm highlights the IGA’s superior convergence speed and computational efficiency.
Keywords
Urban Rail Transit
Operational Disruptions
Genetic Algorithm
Train Timetable Rescheduling
Robust Optimization
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