A Scalable Big Data Framework for Real-Time Traffic Monitoring System

Abstract

In this study, a scalable and real-time intelligent transportation system based on a big data framework is presented. The proposed system allows for the use of existing data from road sensors to better understand traffic flow, and traveler behavior and increase road network performance. Our transportation system is designed to process large-scale stream data to analyze traffic events such as incidents, crashes, and congestion. The experiments performed on the public transportation modes of the city of Casablanca in Morocco reveal that the proposed system achieves a significant gain of time, gathers large-scale data from many road sensors, and is not expensive in terms of hardware resource consumption.