Abstract
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%.
Highlights
The exponential increase in traffic volume has brought many potential challenges to the traffic engineers and transportation department in the form of a massive traffic jam [1]
The intersection points are more susceptible to traffic congestion; for this reason, this paper considers the traffic congestion detection and avoidance in intersection points (i.e., area of interest (AOI))
The performance of the proposed scheme is compared with the existing IoV-based congestion detection scheme, i.e., microscopic congestion detection protocol (MCDP) [20]
Summary
Juan, et al [4] proposed five (i.e., Dynamic Shortest Path (DSP), the A∗ shortest path with repulsion (AR∗ ), the random k shortest path (RkSP), the entropy-balanced kSP (EBkSP), and the flow-balanced kSP (FBkSP)) cost-effective and to deploy traffic rerouting schemes for vehicular traffic management that reduce travel time Their proposed schemes detect congestion proactively on each road segment by periodically assessing each section with a predefined threshold value (i.e., Ki /K jam > δ), where Ki and K jam represent the current and the maximum number of vehicles, respectively. Our proposed IoV-based CDA employed rerouting to assess the traffic load of any alternate route before selection It prevents traffic jams migration from one place to another.
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