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TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

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Abstract

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.

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Authors and Affiliations

  1. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

    Shiva Asadianfam

  2. Faculty of Electrical & Computer Engineering, Qom University of Technology, Qom, Iran

    Mahboubeh Shamsi & Abdolreza Rasouli Kenari

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  1. Shiva Asadianfam
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  2. Mahboubeh Shamsi
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  3. Abdolreza Rasouli Kenari
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Correspondence to Mahboubeh Shamsi.

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Asadianfam, S., Shamsi, M. & Kenari, A.R. TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data. Multimed Tools Appl 80, 2489–2516 (2021). https://doi.org/10.1007/s11042-020-09714-8

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