Research Article
Level-6 Automated IoT integrated with Artificial Intelligence Based Big Data-Driven Dynamic Vehicular Traffic Control System
@ARTICLE{10.4108/eai.13-7-2018.164176, author={Maria Michael Visuwasam L and Ashwin Balakrishna and Nikitha Keerthana S R and Kowsalyaa V}, title={Level-6 Automated IoT integrated with Artificial Intelligence Based Big Data-Driven Dynamic Vehicular Traffic Control System}, journal={EAI Endorsed Transactions on Energy Web}, volume={7}, number={29}, publisher={EAI}, journal_a={EW}, year={2020}, month={4}, keywords={RFID, Energy Efficient Device-to-Device (D2D) Communications, active, Energy Efficient Routing Protocols, round robin}, doi={10.4108/eai.13-7-2018.164176} }- Maria Michael Visuwasam L
Ashwin Balakrishna
Nikitha Keerthana S R
Kowsalyaa V
Year: 2020
Level-6 Automated IoT integrated with Artificial Intelligence Based Big Data-Driven Dynamic Vehicular Traffic Control System
EW
EAI
DOI: 10.4108/eai.13-7-2018.164176
Abstract
The current traffic control system (TCS) is not the most efficient system present for regulating traffic. Hoping to solve this we come up with dynamic data recording systems which encompasses of RFID tag and reader. The traffic density at each lane is calculated based on count of RFID’s apprehended. Depending on the density, the TCS is assigned a value of 15 to 70 seconds in round robin method for control of vehicular congestion. This proposed model also uses image processing for the detection of ambulances and also an active RFID for tracking the real-time location of these assets or in high-speed environments such as that of tolling. This allows the passage of ambulances through dense traffic. This system hopes to achieve to reduce the needless wait of crowded side, to reduce the long traffic chains, and to allow emergencies (medical/ vigilante) quickly through the traffic. The major merits of the system are it prevents unnecessary waiting time when no cars are present at the opposite route, gives the commuting passengers a better and more comfortable driving experience through their journey.
Copyright © 2020 Maria Michael Visuwasam L et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
