A Scalable Big Data Framework for Real-Time Traffic Monitoring System
- 1 Engineering School, International University of Casablanca, Casablanca, Morocco
- 2 FDMS Research Unit, Hassan II University of Casablanca, Casablanca, Morocco
- 3 College of Computer Science and Information Technology, Al Baha University, Al Baha, Saudi Arabia
- 4 REGIM-Lab Research Groups in Intelligent Machines, University of Sfax, Sfax, Tunisia
- 5 ReDCAD Laboratory, University of Sfax, Sfax, Tunisia
- 6 Department of Mathematics, University of Lubumbashi, Lubumbashi, Democratic Republic Of Congo
Abstract
Inthis study, a scalable and real-time intelligent transportation system based ona big data framework is presented. The proposed system allows for the use ofexisting data from road sensors to better understand traffic flow, and travelerbehavior and increase road network performance. Our transportation system isdesigned to process large-scale stream data to analyze traffic events such asincidents, crashes, and congestion. The experiments performed on the publictransportation modes of the city of Casablanca in Morocco reveal that theproposed system achieves a significant gain of time, gathers large-scale datafrom many road sensors, and is not expensive in terms of hardware resourceconsumption.
DOI: https://doi.org/10.3844/jcssp.2022.801.810
Copyright: © 2022 Wilfried Yves Hamilton Adoni, Najib Ben Aoun, Tarik Nahhal, Moez Krichen, Mohammed Y. Alzahrani and Franck Kalala Mutombo. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Road Sensor
- GPS Sensor
- Intelligent Transportation System
- Big Data
- Smart City
- Traffic Monitoring
- Urban Mobility
- Hadoop
- IBM InfoSphere