Deployment and optimization of electric taxi vehicles in urban areas / Imane Kouidri

Public ISBD Titre : Deployment and optimization of electric taxi vehicles in urban areas Titre original : Déploiement et optimisation des taxis électriques dans les zones urbaines Type de document : document multimédia Auteurs : Imane Kouidri, Auteur ; Taher Bendouma, Directeur de thèse ; Omar Sami Oubbati, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2025 Importance : 76 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Networks, distributed systems and applications Langues : Anglais Mots-clés : Electric vehicles (EVs)  Electric taxis  Reinforcement learning  Deep Q-Networks (DQN)  Charging optimization  Urban mobility Résumé : Electric vehicles (EVs) are at the forefront of the transition toward sustainable urban transportation. Among them, electric taxis have emerged as an eco-friendly al- ternative to traditional internal combustion engine vehicles, offering reduced emissions and operational noise. However, integrating electric taxis into urban mobility systems introduces new challenges—ranging from limited battery capacities to inadequate char- ging infrastructure and complex service optimization needs. This thesis addresses these challenges through the development of an intelligent decision-making framework based on Deep Q-Network (DQN) reinforcement learning. We investigate methods to optimize taxi dispatching, customer service, and charging behavior while minimizing downtime and maximizing system efficiency. The proposed system architecture integrates a realistic sim- ulation environment with performance evaluation metrics tailored to electric taxi services. A comparative analysis with existing optimization methods highlights the strengths and limitations of various approaches. Experimental results demonstrate the efficacy of our proposed solution in improving energy efficiency, customer service rate, and operational sustainability. This work offers practical insights for deploying intelligent EV taxi systems and contributes to the broader goal of sustainable smart city development. note de thèses : Mémoire de master en informatique Deployment and optimization of electric taxi vehicles in urban areas = Déploiement et optimisation des taxis électriques dans les zones urbaines [document multimédia] / Imane Kouidri, Auteur ; Taher Bendouma, Directeur de thèse ; Omar Sami Oubbati, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 76 p. + 1 disque optique numérique (CD-ROM). Option : Networks, distributed systems and applications Langues : Anglais Mots-clés : Electric vehicles (EVs)  Electric taxis  Reinforcement learning  Deep Q-Networks (DQN)  Charging optimization  Urban mobility Résumé : Electric vehicles (EVs) are at the forefront of the transition toward sustainable urban transportation. Among them, electric taxis have emerged as an eco-friendly al- ternative to traditional internal combustion engine vehicles, offering reduced emissions and operational noise. However, integrating electric taxis into urban mobility systems introduces new challenges—ranging from limited battery capacities to inadequate char- ging infrastructure and complex service optimization needs. This thesis addresses these challenges through the development of an intelligent decision-making framework based on Deep Q-Network (DQN) reinforcement learning. We investigate methods to optimize taxi dispatching, customer service, and charging behavior while minimizing downtime and maximizing system efficiency. The proposed system architecture integrates a realistic sim- ulation environment with performance evaluation metrics tailored to electric taxi services. A comparative analysis with existing optimization methods highlights the strengths and limitations of various approaches. Experimental results demonstrate the efficacy of our proposed solution in improving energy efficiency, customer service rate, and operational sustainability. This work offers practical insights for deploying intelligent EV taxi systems and contributes to the broader goal of sustainable smart city development. note de thèses : Mémoire de master en informatique

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