Multi-Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm

Keartisak Sriprateep, Rapeepan Pitakaso (), Surajet Khonjun, Thanatkij Srichok, Peerawat Luesak, Sarayut Gonwirat, Chutchai Kaewta, Monika Kosacka-Olejnik and Prem Enkvetchakul
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Keartisak Sriprateep: Manufacturing and Materials Research Unit (MMR), Department of Manufacturing Engineering, Faculty of Engineering, Maha Sarakham University, Maha Sarakham 44150, Thailand
Rapeepan Pitakaso: Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
Surajet Khonjun: Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
Thanatkij Srichok: Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
Peerawat Luesak: Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai 57120, Thailand
Sarayut Gonwirat: Department of Computer Engineering and Automation, Kalasin University, Kalasin 46000, Thailand
Chutchai Kaewta: Digital Innovation, Faculty of Computer Science, Ubon Ratchathani Rajabhat University, Mueang, Ubon Ratchathani 34000, Thailand
Monika Kosacka-Olejnik: Faculty of Engineering Management, Poznan University of Technology, 60965 Poznan, Poland
Prem Enkvetchakul: Department of Information Technology, Faculty of Science, Buriram University, Buriram 31000, Thailand

Mathematics, 2024, vol. 12, issue 14, 1-35

Abstract: Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban bus route optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, and accessibility while minimizing total travel distance. H-RL-VaNSAS is evaluated against leading optimization methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), and Salp Swarm Algorithm (SSA). Using metrics such as Hypervolume and the Average Ratio of Pareto Optimal Solutions, H-RL-VaNSAS demonstrates superior performance. Specifically, H-RL-VaNSAS achieved the highest resilience index (550), sustainability index (370), safety score (480), tourist preferences score (300), and accessibility score (2300), while minimizing total travel distance to 950 km. Compared to other methods, H-RL-VaNSAS improved resilience by 12.24–17.02%, sustainability by 5.71–12.12%, safety by 4.35–9.09%, tourist preferences by 7.14–13.21%, accessibility by 4.55–9.52%, and reduced travel distance by 9.52–17.39%. This research offers a framework for designing efficient, resilient, and sustainable public transit systems that align with urban planning and transportation goals. The integration of reinforcement learning with VaNSAS significantly enhances optimization capabilities, providing a valuable tool for mathematical and urban transportation research communities.

Keywords: urban transportation; multi-objective optimization; resilience; sustainability; hybrid metaheuristic algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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