Distributed Big Data Architecture for Smart Transportation Using Hadoop, Kafka, and Apache Flink

Smart transportation systems generate large amounts of data from sources such as GPS devices, IoT sensors, cameras, and connected vehicles. Managing and processing this data efficiently is important for improving traffic flow, reducing congestion, and enhancing road safety. Traditional centralized systems often struggle to handle the volume, velocity, and variety of transportation data. Therefore, distributed big data technologies are required to support scalable and efficient data processing. This paper presents a distributed big data architecture for smart transportation using technologies such as Hadoop, Apache Kafka, and Apache Flink. Hadoop provides distributed storage and batch processing for large historical datasets, while Kafka enables reliable real-time data streaming from multiple sources. Apache Flink supports real-time stream processing and event detection for traffic monitoring and incident management. The proposed architecture integrates these technologies to enable efficient data collection, processing, and analysis in intelligent transportation systems. The study also discusses the role of data analytics, edge computing, and machine learning in improving traffic management. Results from the analyzed dataset show improvements in emergency detection, response time, accident reduction, and congestion management when advanced data processing techniques are applied.