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

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%.

[1]  Anis Koubaa,et al.  A smart energy‐based source location privacy preservation model for Internet of Things‐based vehicular ad hoc networks , 2020, Trans. Emerg. Telecommun. Technol..

[2]  Ali Ghrayeb,et al.  Trajectory Planning and Resource Allocation of Multiple UAVs for Data Delivery in Vehicular Networks , 2019, IEEE Networking Letters.

[3]  Yi Chen,et al.  On the feasibility of UMTS-based Traffic Information Systems , 2010, Ad Hoc Networks.

[4]  Zhigang Xu,et al.  V2V‐based method for the detection of road traffic congestion , 2019, IET Intelligent Transport Systems.

[5]  MengChu Zhou,et al.  A Cooperative Quality-Aware Service Access System for Social Internet of Vehicles , 2018, IEEE Internet of Things Journal.

[6]  Qiang Ni,et al.  An Evolving Graph-Based Reliable Routing Scheme for VANETs , 2013, IEEE Transactions on Vehicular Technology.

[7]  Bor-Sen Chen,et al.  Noncooperative and Cooperative Strategy Designs for Nonlinear Stochastic Jump Diffusion Systems With External Disturbance: T–S Fuzzy Approach , 2020, IEEE Transactions on Fuzzy Systems.

[8]  Liang Qi,et al.  A dynamic road incident information delivery strategy to reduce urban traffic congestion , 2018, IEEE/CAA Journal of Automatica Sinica.

[9]  Haleem Farman,et al.  Designing a Smart Transportation System: An Internet of Things and Big Data Approach , 2019, IEEE Wireless Communications.

[10]  M. Vardhana,et al.  Iot based real time trafic control using cloud computing , 2018, Cluster Computing.

[11]  Fred L. Mannering,et al.  The temporal stability of factors affecting driver-injury severities in single-vehicle crashes: Some empirical evidence , 2015 .

[12]  C. Borrego,et al.  Integrating road traffic externalities through a sustainability indicator. , 2019, The Science of the total environment.

[13]  Muhammad Imran,et al.  Congestion avoidance through fog computing in internet of vehicles , 2019, J. Ambient Intell. Humaniz. Comput..

[14]  Ling Wang,et al.  A memetic algorithm with competition for the capacitated green vehicle routing problem , 2019, IEEE/CAA Journal of Automatica Sinica.

[15]  Fadi Al-Turjman,et al.  Small Cells in the Forthcoming 5G/IoT: Traffic Modelling and Deployment Overview , 2019, IEEE Communications Surveys & Tutorials.

[16]  J. McKay,et al.  Investigating ‘anywhere working’ as a mechanism for alleviating traffic congestion in smart cities , 2019, Technological Forecasting and Social Change.

[17]  Haleem Farman,et al.  Toward Integrating Vehicular Clouds with IoT for Smart City Services , 2019, IEEE Network.

[18]  Ankit Gupta,et al.  Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs , 2018, Data.

[19]  Qingchun Chen,et al.  Infrastructure-based vehicular congestion detection scheme for V2I , 2019, Int. J. Commun. Syst..

[20]  Frank Cushman.  Transportation for management , 1953 .

[21]  Azzedine Boukerche,et al.  Safety and efficiency control protocol for highways using intelligent vehicular networks , 2019, Comput. Networks.

[22]  Melanie Weber,et al.  Public Transport, Logistics and Rail Traffic Extensions in SUMO , 2018, Lecture Notes in Mobility.

[23]  S. Jamuna Devi,et al.  Machine Learning based traffic congestion prediction in a IoT based Smart City , 2017 .

[24]  Karine Zeitouni,et al.  Proactive Vehicular Traffic Rerouting for Lower Travel Time , 2013, IEEE Transactions on Vehicular Technology.

[25]  Christoph F. Mecklenbräuker,et al.  Increased Traffic Flow Through Node-Based Bottleneck Prediction and V2X Communication , 2017, IEEE Transactions on Intelligent Transportation Systems.

[26]  Pingzhi Fan,et al.  An Unsupervised Cluster-Based VANET-Oriented Evolving Graph (CVoEG) Model and Associated Reliable Routing Scheme , 2019, IEEE Transactions on Intelligent Transportation Systems.

[27]  Marcello Montanino,et al.  Do We Really Need to Calibrate All the Parameters? Variance-Based Sensitivity Analysis to Simplify Microscopic Traffic Flow Models , 2015, IEEE Transactions on Intelligent Transportation Systems.

[28]  Stefan Jakubek,et al.  Investigation of stochastic variation of parameters for a macroscopic traffic model , 2018, J. Intell. Transp. Syst..

[29]  Adian Fatchur Rochim,et al.  Location-based mobile application software development: Review of Waze and other apps , 2017 .

[30]  Pascal Lorenz,et al.  The Internet of Things for Smart Cities: Technologies and Applications , 2019, IEEE Netw..

[31]  Mohsen Guizani,et al.  iCAFE: Intelligent Congestion Avoidance and Fast Emergency services , 2019, Future Gener. Comput. Syst..

[32]  Qingchun Chen,et al.  Microscopic Congestion Detection Protocol in VANETs , 2018 .

[33]  Hongliang Guo,et al.  A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion , 2017, IEEE Transactions on Intelligent Transportation Systems.

[34]  Keke Gai,et al.  Privacy-Preserving Content-Oriented Wireless Communication in Internet-of-Things , 2018, IEEE Internet of Things Journal.

[35]  Hasan Omar Al-Sakran,et al.  Intelligent Traffic Information System Based on Integration of Internet of Things and Agent Technology , 2015 .

[36]  Meghan Winters,et al.  Using OpenStreetMap to inventory bicycle infrastructure: A comparison with open data from cities , 2019, International Journal of Sustainable Transportation.

[37]  MengChu Zhou,et al.  Integrated Production Inventory Routing Planning for Intelligent Food Logistics Systems , 2019, IEEE Transactions on Intelligent Transportation Systems.

[38]  Pingzhi Fan,et al.  On the Connectivity of Vehicular Ad Hoc Network Under Various Mobility Scenarios , 2017, IEEE Access.