Advanced Intelligent Traffic Management System(AITMS): A Generative AI-Enhanced Model
In rapidly urbanizing cities with increasing populations, the challenges of managing traffic are becoming more pressing, highlighting the need for advanced technologies. The Advanced Intelligent Traffic Management System (AITMS) is a comprehensive solution designed to address the persistent challenges of traffic congestion, poor traffic management, and air pollution in densely populated urban areas. Current traffic management systems often rely on static algorithms and predetermined signal timings, which are insufficient to handle dynamic and unpredictable traffic patterns. This results in prolonged travel times, increased fuel consumption, and elevated levels of air pollution, posing serious threats to public health and environmental sustainability. AITMS integrates physical and digital infrastructures, leveraging Generative AI for real-time data collection, analysis, and decision-making. By incorporating sensors, IoT devices, and advanced data analytics, the system can predict traffic flow, optimize signal timings, and recommend alternate routes. It also communicates with connected vehicles to provide real-time updates. The incorporation of Generative AI enables the AITMS to continually adapt and learn from new data inputs and trends, ensuring sustainability and scalability in urban transportation. AITMS is an innovative framework enhanced by Generative AI, aimed at revolutionizing urban traffic management in smart cities. Similar frameworks demonstrate promising outcomes, including improved traffic management, reduced congestion, and enhanced driver safety. AITMS aims to transform urban transportation, elevate residents' quality of life, and foster more sustainable and resilient cities amidst urbanization challenges.
Authors
- Kariuki Pius Muriuki
- Japheth Odiwour Okello
- Joan Chepkoech
Institutions
- Technical University of Kenya (KE)
- Kenyatta University (KE)
Publication Details
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1109/powerafrica61624.2024.10759478
- Citations
- 4
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.06