A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics
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2022-02-16Referencia bibliográfica
Alsaawy, Y... [et al.]. A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics. Appl. Sci. 2022, 12, 2043. [https://doi.org/10.3390/app12042043]
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Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia 2/710Resumen
Traffic congestion is still a challenge faced by most countries of the world. However, it can
be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog
computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of
these technologies to address specific problems faced in traffic management. Unfortunately, no such
framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management
exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and
efficient solution for the problem of traffic congestion. The proposed framework has four layers.
The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate
measurement of the traffic status of the streets, and forwards these data to the second layer. The
second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards
them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics
and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for
traffic management. We also discuss the functional model of the framework and the technologies
that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for
the efficient management of congestion at intersections, tweet-classification and image-processing
algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the
functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and
the tweet classification and image-processing algorithms for their accuracy.