Sensors 2016, 16(5), 599; doi:10.3390/s16050599
Traffic Congestion Detection System through Connected Vehicles and Big Data
Néstor Cárdenas-Benítez 1
, Raúl Aquino-Santos 1, Pedro Magaña-Espinoza 2,*
, José Aguilar-Velazco 1, Arthur Edwards-Block 1 and Aldo Medina Cass 3
, Raúl Aquino-Santos 1, Pedro Magaña-Espinoza 2,*
, José Aguilar-Velazco 1, Arthur Edwards-Block 1 and Aldo Medina Cass 31
School of Telematics, University of Colima, 333 Universidad Avenue, C.P. 28040 Colima, Col., Mexico
2
Department of Innovation and Technological Development, Siteldi Solutions S.A. de C.V., 111 Canario Street, C.P. 28017 Colima, Col., Mexico
3
Department of Innovation and Technological Development, Corporativo STR S.A. de C.V., 111 Canario Street, C.P. 28017 Colima, Col., Mexico
*
Author to whom correspondence should be addressed.
Academic Editors: Mihai Lazarescu and Luciano Lavagno
Received: 29 February 2016 / Revised: 13 April 2016 / Accepted: 22 April 2016 / Published: 28 April 2016
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
Abstract
This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO2 and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility) traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur. View Full-TextKeywords:
connected vehicles; big data; traffic congestion detection system; IoT
▼
Figures
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Scifeed alert for new publications
Never miss any articles matching your research from any publisher- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Share & Cite This Article
MDPI and ACS Style
Cárdenas-Benítez, N.; Aquino-Santos, R.; Magaña-Espinoza, P.; Aguilar-Velazco, J.; Edwards-Block, A.; Medina Cass, A. Traffic Congestion Detection System through Connected Vehicles and Big Data. Sensors 2016, 16, 599.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.