AIoT for real-time traffic analysis and optimisation
Shubham Gupta, Rajnish Kohli · 2025
Urbanisation is gaining speed and vehicular density is rising rapidly, thereby resulting in traffic management challenges that require solutions to be deployed at an expedited pace. We explore the possible transformative capabilities of Artificial Intelligence of Things (AIoT), which integrates the data acquisition and communication capabilities offered by the Internet of Things (IoT) with the analytical and decision-making powers brought by Artificial Intelligence (AI). By using the streams of data in real-time, AIoT-driven systems can adapt and implement a predictive traffic management framework for example through an entire ecosystem of connected IoT devices (smart cameras/roadside sensors and vehicle detectors). The AIoT architecture described in this chapter consists of multiple layers, including sensing and communication, device management, edge computing, and cloud integration that enable real-time sensor data collection, and processing providing actionable insights. For example, machine learning and deep learning algorithms have been used to forecast traffic patterns, detect anomalies, and recommend optimised control strategies. Prominent use cases addressed in this study are adaptive traffic signal control, dynamic routing, and load balancing which help reduce congestion, emissions, and fatalities. The chapter also discusses the possibilities of utilising emerging technologies such as digital twins for static and dynamic traffic simulation and optimisation, next-generation communication protocols like 5G and 6G for ultra-low latency, and federated intelligence for privacy-preserving traffic data analytics. Combined, these technologies serve as pathways to resilient, scalable traffic management that is future-oriented. Besides demonstrating the ability of AIoT to address key challenges in urban mobility, this research paves the way for efficient, intelligent, and sustainable transportation networks, enabling smart to become more adaptive.