An AI-Driven Intelligent Transportation System: Functional Architecture and Implementation

Huszák, Árpád and Simon, Vilmos and Bokor, László and Tizedes, László and Pekár, Adrián (2024) An AI-Driven Intelligent Transportation System: Functional Architecture and Implementation. INFOCOMMUNICATIONS JOURNAL, 16 (3). pp. 18-30. ISSN 2061-2079

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Abstract

The surge in urbanization and the concomitant growth of the urban population have exacerbated issues such as traffic congestion and air pollution across cities globally. While Intelligent Transportation Systems (ITS) offer promise for im- proving urban mobility, existing solutions predominantly exhibit limitations in scalability and adaptability, thus falling short in delivering city-wide traffic management. This unaddressed gap necessitates the development of a robust, scalable, and adaptive system that can manage the intricacies of urban traffic. Our work introduces CityAI, an automated, AI-driven framework designed to operate on a city-wide scale. The system harvests data from diverse sensing infrastructures, employing machine learning algorithms to predict future traffic states and pat terns. Furthermore, it proposes real-time interventions, including adaptive traffic light control and V2X-based solutions. The architecture and components of CityAI not only incorporate state-of-the-art techniques but are also applied in real-world environments. The CityAI framework was implemented in the city of Pécs, Hungary, as a proof-of-concept ITS system. The framework enables city authorities to implement proactive measures, thus preventing traffic issues before they manifest. The paper focuses on practical development aspects of an ITS system undertaking R&D on new technologies, applications, and techniques which may facilitate future product development.

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