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Article

Smart Route: Internet-of-Vehicles (IoV)-Based Congestion Detection and Avoidance (IoV-Based CDA) Using Rerouting Planning

1
Robotics and IoT Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia
2
Department of Computer Science, Islamia College Peshawar, Grand Trunk Rd, Rahat Abad, Peshawar, Khyber Pakhtunkhwa 25120, Pakistan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4541; https://doi.org/10.3390/app10134541
Received: 8 May 2020 / Revised: 11 June 2020 / Accepted: 24 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
Massive traffic jam is the top concern of multiple disciplines (Civil Engineering, Intelligent Transportation Systems (ITS), and Government Policy) presently. Although literature constitutes several IoT-based congestion detection schemes, the existing schemes are costly (money and time) and, as well as challenging to deploy due to its complex structure. In the same context, this paper proposes a smart route Internet-of-Vehicles (IoV)-based congestion detection and avoidance (IoV-based CDA) scheme for a particular area of interest (AOI), i.e., road intersection point. The proposed scheme has two broad parts: (1) IoV-based congestion detection (IoV-based CD); and (2) IoV-based congestion avoidance (IoV-based CA). In the given area of interest, the congestion detection phase sets a parametric approach to calculate the capacity of each entry point for real-time traffic congestion detection. On each road segment, the installed roadside unit (RSU) assesses the traffic status concerning two factors: (a) occupancy rate and (b) occupancy time. If the values of these factors (either a or b) exceed the threshold limits, then congestion will be detected in real time. Next, IoV-based congestion avoidance triggers rerouting using modified Evolving Graph (EG)-Dijkstra, if the number of arriving vehicles or the occupancy time of an individual vehicle exceeds the thresholds. Moreover, the rerouting scheme in IoV-based congestion avoidance also considers the capacity of the alternate routes to avoid the possibility of moving congestion from one place to another. From the experimental results, we determine that proposed IoV-based congestion detection and avoidance significantly improves (i.e., 80%) the performance metrics (i.e., path cost, travel time, travelling speed) in low segment size scenarios than the previous microscopic congestion detection protocol (MCDP). Although in the case of simulation time, the performance increase depends on traffic congestion status (low, medium, high, massive), the performance increase varies from 0 to 100%. View Full-Text
Keywords: internet of vehicles; traffic congestion detection and avoidance; intelligent transportation system; route planning internet of vehicles; traffic congestion detection and avoidance; intelligent transportation system; route planning
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MDPI and ACS Style

Khan, Z.; Koubaa, A.; Farman, H. Smart Route: Internet-of-Vehicles (IoV)-Based Congestion Detection and Avoidance (IoV-Based CDA) Using Rerouting Planning. Appl. Sci. 2020, 10, 4541. https://doi.org/10.3390/app10134541

AMA Style

Khan Z, Koubaa A, Farman H. Smart Route: Internet-of-Vehicles (IoV)-Based Congestion Detection and Avoidance (IoV-Based CDA) Using Rerouting Planning. Applied Sciences. 2020; 10(13):4541. https://doi.org/10.3390/app10134541

Chicago/Turabian Style

Khan, Zahid; Koubaa, Anis; Farman, Haleem. 2020. "Smart Route: Internet-of-Vehicles (IoV)-Based Congestion Detection and Avoidance (IoV-Based CDA) Using Rerouting Planning" Appl. Sci. 10, no. 13: 4541. https://doi.org/10.3390/app10134541

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