TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data
- PDF / 3,536,161 Bytes
- 28 Pages / 439.37 x 666.142 pts Page_size
- 14 Downloads / 196 Views
TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data Shiva Asadianfam 1 & Mahboubeh Shamsi 2
& Abdolreza Rasouli Kenari
2
Received: 19 January 2020 / Revised: 13 August 2020 / Accepted: 25 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords MapReduce-based . Deep learning . Distributed processing . Drivers’ behavior detection . Hadoop . Unsafe behaviors
* Mahboubeh Shamsi [email protected] Shiva Asadianfam [email protected] Abdolreza Rasouli Kenari [email protected]
1
Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
2
Faculty of Electrical & Computer Engineering, Qom University of Technology, Qom, Iran
Multimedia Tools and Applications
1 Introduction Drivers, as human beings, commit violations through various behaviors and subsequently cause various kinds of damage. In fact, drivers’ risky behavior brings about unforeseen incidents and consequently irreparable damage. In general, potential risks pertain to uncontrolled conditions or activities that can cause damage. Identifying risky conditions and eliminating or controlling them enables us to avoid damage. The social costs of injuries deaths and casualties are too much. Mostly drivers are blamed for causing accidents and injuries [33]; Therefore, modifying drivers’ behavior is considered as one of the most important and challenging issues in transportation. In order to control drivers’ traffic violations, traffic policemen are present at the entry points to fine violating vehicles. In this method, due to the
Data Loading...
