@article {10.3844/jcssp.2022.801.810,
article_type = {journal},
title = {A Scalable Big Data Framework for Real-Time Traffic Monitoring System},
author = {Adoni, Wilfried Yves Hamilton and Aoun, Najib Ben and Nahhal, Tarik and Krichen, Moez and Alzahrani, Mohammed Y. and Mutombo, Franck Kalala},
volume = {18},
number = {9},
year = {2022},
month = {Sep},
pages = {801-810},
doi = {10.3844/jcssp.2022.801.810},
url = {https://thescipub.com/abstract/jcssp.2022.801.810},
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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}