AI-Powered Vehicle Image Recognition Model for Sustainable and Smart Urban Traffic Management

Artificial Intelligence (AI) is rapidly transforming industries worldwide, offering new opportunities for urban planning, mobility management, and strategic governance. Among its most promising applications is the use of advanced image recognition techniques to optimize traffic systems and foster sustainable smart cities. This study presents the design and implementation of an AI-driven vehicle identification model that leverages object detection and classification through Convolutional Neural Network (CNN)-based architecture. The model, trained on a diverse dataset, achieves high identification accuracy and adaptability in dynamic, real-world environments. Evaluation metrics include recognition performance, computational efficiency, and responsiveness to traffic conditions. By utilizing this technology, cities can monitor road infrastructure in real-time, identifying maintenance needs such as asphalt wear, and supporting sustainable infrastructure management. Additionally, the model can assist in alleviating traffic congestion by suggesting alternative routes for vehicles, thus reducing emissions and improving urban mobility. Beyond technical contributions, this research underscores the model’s value in supporting strategic planning, infrastructure investment, and data-driven policy design, contributing to the creation of more efficient, sustainable, and economically resilient urban ecosystems. The findings highlight the role of AI-powered technologies in enabling informed decision-making, improving public services, and fostering long-term sustainability in urban mobility. The proposed model demonstrates not only technical feasibility but also practical relevance for integration into existing urban mobility infrastructures. Its adoption can serve as a foundational step toward smarter, data-centric governance models that anticipate rather than react to urban traffic challenges.