A Synchronized Optimization Method of Frequency Setting, Timetabling, and Train Circulation Planning for URT Networks with Overlapping Lines: A Case Study of the Addis Ababa Light Rail Transit Service

Wenliang Zhou (), Addishiwot Alemu and Mehdi Oldache
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Wenliang Zhou: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Addishiwot Alemu: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Mehdi Oldache: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China

Mathematics, 2025, vol. 13, issue 16, 1-28

Abstract: Urban rail transit (URT) systems are essential to ensuring efficient and sustainable urban mobility. However, the core components of operational planning, service frequency setting, train timetabling, and train allocation are often optimized separately, leading to fragmented decision-making and suboptimal system performance. This study addresses that gap by proposing an integrated optimization framework that simultaneously considers all three planning layers under time-dependent passenger demand conditions. The problem is formulated as a bi-objective Integer Nonlinear Programming (INLP) model, aiming to jointly minimize passenger waiting time and total operational cost. To solve this large-scale, combinatorial problem, a tailored Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed. The algorithm incorporates discrete variable handling, constraint-preserving mechanisms, and a customized encoding scheme that aligns with the structural characteristics of URT operations. The proposed framework is applied to real-world data from the Addis Ababa Light Rail Transit (AALRT) system. The results demonstrate that the MOPSO-based approach offers a more diverse and operationally feasible set of trade-off solutions compared to a widely used benchmark algorithm, NSGA-II. Specifically, it provides transit planners with a flexible decision-support tool capable of identifying schedules that balance service quality and cost, based on varying strategic or budgetary priorities. By integrating interdependent planning decisions into a unified model and leveraging the strengths of a customized metaheuristic algorithm, this study contributes a scalable, adaptable, and practically relevant methodology for improving the performance of urban rail systems.

Keywords: light railway transit; multi-objective optimization; particle swarm optimization (MOPSO); timetabling and frequency scheduling; operational cost minimization; passenger waiting time reduction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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