This chapter explores the integration of Artificial Intelligence (AI) and the
Internet of Things (IoT) for advanced road traffic surveillance and real-time
monitoring, addressing the limitations of traditional traffic management systems. The
proposed framework leverages IoT-enabled devices, such as smart traffic signals,
embedded road sensors, and Vehicle to Infrastructure (V2I) communication, to
facilitate continuous, high-frequency data collection. AI-driven statistical models,
including regression analysis, neural networks, anomaly detection, and reinforcement
learning, process these data streams to enable predictive traffic modeling, incident
detection, congestion management, and adaptive control. Case studies demonstrate
significant enhancements in traffic flow, reduced congestion, and improved safety
through solutions like smart traffic signals, IoT-enabled sensors, and V2I-based hazard
alerts. For example, smart signals reduced average wait times by 25%, while V2I
communication improved incident response times by 63%. Anomaly detection via
video analytics showcased a 92% accuracy rate, enabling swift responses to
disruptions, reducing secondary accidents, and improving traffic efficiency across
dense urban networks. Challenges include managing vast real-time data, ensuring
system scalability, minimizing latency, and addressing privacy, ethical concerns, and
interoperability with legacy systems. Future directions emphasize adopting edge
computing for real-time processing, enhancing AI model accuracy with diverse
datasets, and developing sustainable, energy-efficient IoT devices. Transparent data
usage policies, public engagement, and advancements in quantum computing, together
with robust ethical frameworks, can further refine AI-powered traffic systems. This
chapter highlights the transformative potential of AI and IoT in creating proactive,
efficient, and sustainable urban traffic ecosystems, offering a comprehensive roadmap
for future smart city infrastructure and intelligent transportation system development.
Keywords: Advanced traffic management, IoT, Predictive modelling, Road traffic surveillance, Real-time monitoring, Statistical modelling.
