Design And Development Of A Big Data Architecture For Traffic Control Systems: A Comprehensive Review

Abstract

The exponential growth of urban populations and vehicular traffic has created unprecedented challenges for traditional traffic management systems. This paper presents a comprehensive review of big data architectures designed for modern traffic control systems, examining their components, implementation strategies, and performance implications. The research analyzes how emerging technologies including Internet of Things (IoT) sensors, machine learning algorithms, and distributed computing platforms are revolutionizing traffic management through real-time data processing and intelligent decision-making capabilities. The study explores architectural frameworks that handle the volume, velocity, variety, and veracity characteristics of traffic data while ensuring scalability, reliability, and cost-effectiveness. Key findings indicate that hybrid architectures combining centralized and distributed elements, leveraging technologies such as Apache Kafka, Hadoop, and cloud computing platforms, provide optimal solutions for real-time traffic control applications. The paper concludes with recommendations for future developments in intelligent transportation systems and identifies critical challenges in implementing large-scale big data traffic management solutions.