AI-Driven Optimization of Urban Mobility: Integrating Autonomous Vehicles with Real-Time Traffic and Infrastructure Analytics

Authors

Abhinav Balasubramanian

Abstract

Urban mobility is at a tipping point, facing challenges such as traffic congestion, inefficient infrastructure utilization, and environmental concerns. This paper explores the transformative potential of integrating artificial intelligence (AI), machine learning (ML), and autonomous vehicle (AV) technologies to optimize real-time traffic management and infrastructure analytics. Leveraging advanced AI/ML techniques—including predictive analytics, reinforcement learning, and computer vision—we propose a robust framework to enhance traffic flow, optimize vehicle-to-everything (V2X) communication, and enable adaptive routing for AVs.
The framework utilizes real-time data from IoT sensors, AVs, and urban infrastructure to power dynamic traffic signal control and adaptive routing, thereby improving urban mobility efficiency. By employing AI/ML for traffic prediction and flow optimization, it aims to significantly reduce congestion, boost commuter safety, and mitigate environmental impacts.
This research offers a fresh perspective on AI-driven AV integration, emphasizing their synergistic potential to revolutionize urban mobility. The case studies illuminate a promising pathway toward building sustainable, efficient, and equitable smart cities through intelligent analytics and automation.

Keywords

Artificial Intelligence (AI), Urban Mobility Optimization, Autonomous Vehicles Integration, Real-Time Traffic Management, Computer Vision, Machine Learning for Smart Cities, Intelligent Transportation Systems (ITS)

AI-Driven Optimization of Urban Mobility: Integrating Autonomous Vehicles with Real-Time Traffic and Infrastructure Analytics. Abhinav Balasubramanian. 2019. IJIRCT, Volume 5, Issue 5. Pages 1-13. https://www.ijirct.org/viewPaper.php?paperId=2412076