Analysis of Cloud-Based Big Data Infrastructures for Real-Time Traffic Flow Optimization in Urban Corridors

Abstract

Current strategies for optimizing urban traffic flow employ a wide range of sensor data and machine learning techniques to predict and manage congestion in real time. Rapid growth of cloud-based big data infrastructures offers a framework that can integrate massive datasets and high-speed processing for enhanced decision-making. Sensor networks installed at intersections and along highways generate continuous streams of data on vehicle counts, travel times, and incident reports. These data streams undergo preliminary cleaning and aggregation before reaching cloud servers equipped with parallel processing algorithms. Emerging architectures accommodate machine learning modules capable of short-term traffic forecasting and adaptive signal control. Additional integration with Internet of Things (IoT) devices and edge computing nodes improves latency and accelerates local analytics. Hybrid models combine centralized computing resources with decentralized decision-making, facilitating a responsive approach to changing traffic conditions. This paper analyzes the specific technical features that underpin cloud-based systems for real-time traffic management. Emphasis is placed on database scalability, data redundancy, and algorithmic efficiency for traffic optimization. Mathematical models describing traffic flow and queuing dynamics illustrate how cloud-based infrastructures empower quick reconfiguration of signal timing plans and rerouting strategies. Careful coordination between cloud resources, edge devices, and ground sensors emerges as a primary factor ensuring robust, city-scale congestion mitigation.