An Edge-Assisted Genetic Algorithm for Dynamic Multi-Objective Urban Routing

Suhail Odeh; Mahmoud Obaid; Rafik Lasari; Murad Al-Rajab; Hebatullah Khattab; Ammar Qarariyah; Djemel Ziou

doi:10.48084/etasr.18502

Authors

Suhail Odeh Technology Department, Bethlehem University, Bethlehem, Palestine https://orcid.org/0000-0003-4186-8327
Mahmoud Obaid Computer System Engineering Department, Arab American University, Jenin, Palestine https://orcid.org/0000-0003-1980-2778
Rafik Lasari 2TED, AEEP Research Group, FPL, Abdelmalek Essaadi University, Tetouan, Morocco
Murad Al-Rajab Computer Science and Information Technology Department, College of Engineering, Abu Dhabi University, Abu Dhabi, UAE
Hebatullah Khattab Computer Science Department, Zarqa University, Zarqa, Jordan
Ammar Qarariyah Technology Department, Bethlehem University, Bethlehem, Palestine
Djemel Ziou Departement d’Informatique, Faculte des Sciences, Universite de Sherbrooke, Quebec, Canada

Volume: 16 | Issue: 3 | Pages: 36716-36724 | June 2026 | https://doi.org/10.48084/etasr.18502

Received: 2 March 2026 | Revised: 24 March 2026, 21 April 2026, and 8 May 2026 | Accepted: 10 May 2026 | Online: 6 June 2026

Corresponding author: Mahmoud Obaid

Abstract

Congestion in urban areas continues to pose a challenge for rapidly developing cities, resulting in longer travel times, fuel consumption, and environmental degradation. Traditional shortest-path algorithms, although computationally efficient, are not very adaptable to dynamically changing congestion patterns. This study proposes a congestion-aware multi-objective Genetic Algorithm (GA) framework grounded on NSGA-II with Rolling Horizon Optimization (RHO) to improve the adaptability of routing to congestion patterns in urban transportation networks. The model was developed in the SUMO simulation framework and tested with real-world traffic data in Bethlehem City. The experimental findings show that the proposed strategy can reduce travel time by up to 16.7% in high congestion situations and intersection waiting time by up to 19.8% under high-traffic conditions. The stability and scalability of the framework were confirmed by experimental results in the presence of stochastic disturbances such as accidents, demand surges, and poor weather conditions. In contrast to most of the current GA-based methods tested on artificial data, this study focused on real-world validation, dynamic congestion integration, and multi-objective trade-off analysis through Pareto optimization. The results indicate the potential of evolutionary optimization methods for scalable data-driven intelligent transportation systems.

Keywords:

congestion, genetic algorithm, intelligent transportation systems, smart cities, SUMO

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