ACO-Based Dynamic Decision Making for Connected Vehicles in IoT System

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 10; pp. 5648 - 5655
• Main Authors: Bui, Khac-Hoai Nam, Jung, Jason J.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-10-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization; Computer simulation; Connected vehicles; decentralized management; Decision making; dynamic decision making; Heuristic algorithms; intelligent transportation system; Intelligent transportation systems; Internet of Things; Roundabouts; Routing; Swarm intelligence; Traffic flow; Traffic information; Traffic models; Vehicle dynamics; Vehicles
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2906886

With the rapid development of the internet of things (IoT), connected vehicles are set to become a huge industry over the next few years. In this study, we take an investigation of the distributed intelligent traffic system by pushing intelligence into connected vehicles in terms of dynamic decision making for traversing a certain area (e.g., roundabout and intersection). In particular, we propose a model for the next generation of intelligent transportation system, which focuses on dynamic decision making of connected vehicles based on Ant Colony Optimization, a typical Swarm Intelligence (SI)-based algorithm. Specifically, we first present a communication framework among connected vehicles for sharing information of traffic flow. Then, by applying the concept of SI, connected vehicles are regarded as artificial ants which are able to self-calculate to make an adaptive decision following the dynamics of traffic flow. Furthermore, for evaluating the effectiveness of the proposed approach, we have constructed a framework to model and simulate the traffic system in IoT environment. Simulations with different scenarios of transportation systems indicate promising results comparing with previous works.