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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

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Publicado en:Mathematics vol. 13, no. 16 (2025), p. 2654-2682
Autor principal: Zhou Wenliang
Otros Autores: Alemu Addishiwot, Oldache Mehdi
Publicado:
MDPI AG
Materias:
Addis Ababa Ethiopia
Ethiopia
Time dependence
Particle swarm optimization
Integer programming
Collaboration
Combinatorial analysis
Light rail transportation
Optimization
Passengers
Transportation planning
Multiple objective analysis
Operating costs
Energy consumption
Customization
Nonlinear programming
Heuristic methods
Efficiency
Case studies
Schedules
Scheduling
Decision support systems
Frequency setting
Travel demand
Algorithms
Travel
Quality of service
Linear programming
Literature reviews
Cost control
Urban rail
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Resumen: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.
ISSN:2227-7390
DOI:10.3390/math13162654
Fuente:Engineering Database