Abstract
With the advancement of information and communication technologies (ICTs), there has been high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase quality of services, and provide many extensive benefits to the commuters and transportation authorities. In this article, we propose a novel edge-based AI-IoT integrated energy-efficient intelligent transport system for smart cities by using a distributed multi-agent system. An urban area is divided into multiple regions, and each region is sub-divided into a finite number of zones. At each zone an optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects a huge volume of data from the various sensors, devices, and infrastructures. The edge computing device uses the collected raw data from the MAS to process, analyze, and predict. The predicted information will be shared with the neighborhood RSUs, vehicles, and cloud by using MAS with the help of IoT. The predicted information can be used by freight vehicles to maintain smooth and steady movement, which results in reduction in GHG emissions and energy consumption, and finally improves the freight vehicles’ mileage by reducing traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.
- [1] . 2016. Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. In Transportation Research Board 95th Annual Meeting. No. 16–1593.Google Scholar
- [2] 2015. Developing a framework of eco-approach and departure application for actuated signal control. In IEEE on Intelligent Vehicles Symposium. June 28–July 1.Google Scholar
- [3] . 2017. Eco-Approach and Departure (EAD) application for actuated signal in real-world traffic. Submitted to Transportation Research Board (TRB) 96th Annual Meeting for presentation, Washington D.C. 8–12.Google Scholar
- [4] . 2015. Velocity predictors for predictive energy management in hybrid electric vehicles. IEEE Transactions on Control Systems Technology 23, 3 (2015), 1197–1204.Google Scholar
- [5] . 2017. Vehicle speed prediction by two-level data driven models in vehicular networks. IEEE Transactions on Intelligent Transportation Systems 18, 7 (2017), 1793–1801.Google ScholarDigital Library
- [6] . 1994. Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems. IEEE Transactions on Neural Networks 5 (1994), 594–603.Google ScholarDigital Library
- [7] . 2012. Agent-based modeling of eco-cooperative adaptive cruise control system in the vicinity of intersection. In15th International IEEE Conference Intelligent Transportation Systems (ITSC’12). 840–845.Google Scholar
- [8] . 2015. Personalized driver/vehicle lane change models for ADAS. IEEE Transactions on Intelligent Transportation Systems 64, 10 (2015), 4422–4431.Google Scholar
- [9] . 2014. Multi-agent reinforcement learning for traffic signal control. In 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC’14). 2529–2534.Google Scholar
- [10] . 2013. Intelligent traffic light control of isolated intersections using machine learning methods. In IEEE International Conference on Systems, Man, and Cybernetics (SMC’13). 3621–3626.Google Scholar
- [11] , 2019. Survey of neural network–based models for short–term traffic state prediction. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9, 1 (2019), p.e1285.Google ScholarCross Ref
- [12] . 2019. EdgeBatch: Towards AI-empowered optimal task batching in intelligent edge systems. In 2019 IEEE Real-Time Systems Symposium (RTSS’19). IEEE, 366–379.Google ScholarCross Ref
- [13] . 2019. Intelligent edge computing for IoT-based energy management in smart cities. IEEE Network 33, 2 (2019), 111–7.Google ScholarCross Ref
- [14] . 2017. Efficient energy management for the Internet of Things in smart cities. IEEE Communications Magazine 55, 1 (2017), 84–91.Google ScholarDigital Library
- [15] . 2019. A vehicular network–based intelligent transport system for smart cities. International Journal of Distributed Sensor Networks 15, 11 (2019), 1550147719888845.Google ScholarCross Ref
- [16] . 2019. A computer vision-based roadside occupation surveillance system for intelligent transport in smart cities. Sensors 19, 8 (2019), 1796.Google ScholarCross Ref
- [17] . 2019. IoT-based context-aware intelligent public transport system in a metropolitan area. IEEE Internet of Things Journal 5, 3 (2019), 426–441.Google Scholar
- [18] . 2020. Prediction based traffic management in a metropolitan area. Journal of Traffic and Transportation Engineering (English edition) 7, 4 (2020), 447–466.Google ScholarCross Ref
- [19] . 2019. Prediction based traffic management in a metropolitan area. Journal of Traffic and Transportation Engineering (English Edition) 6 (2019), 1–8.Google Scholar
- [20] 2017. Intelligent traffic light control using distributed multi-agent Q learning. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC’17), IEEE, 16, 1–8.Google Scholar
- [21] 2021. An efficient context-aware vehicle incidents route service management for intelligent transport system. IEEE Systems Journal 16, 1 (2021), 487–498.Google ScholarCross Ref
- [22] . 2018. Artificial intelligence-based semantic internet of things in a user-centric smart city. Sensors 18, 5 (2018), 1341.Google ScholarCross Ref
- [23] . 2019. Artificial intelligence empowered edge computing and caching for internet of vehicles. IEEE Wireless Communications 26, 3 (2019), 12–18.Google ScholarDigital Library
- [24] . 2021. V2X communication based dynamic topology control in VANETs. In Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking. 62–68.Google ScholarDigital Library
- [25] 2018. Artificial intelligence enabled IoT: Traffic congestion reduction in smart cities. In IET 2018 Smart Cities Symposium (SCS’18). 22–23, 81–86.Google ScholarCross Ref
- [26] . 2018. Prediction-based eco-approach and departure at signalized intersections with speed forecasting on preceding vehicles. IEEE Transactions on Intelligent Transportation Systems 20, 4 (2018), 1378–1389.Google ScholarCross Ref
- [27] . 2016. Real-time path planning to prevent traffic jam through an intelligent transportation system. In 2016 IEEE Symposium on Computers and Communication (ISCC’16), IEEE, 726–731.Google ScholarCross Ref
- [28] . 2020. Privacy and security management in intelligent transportation system. IEEE Access 8 (2020), 148677–148688.Google ScholarCross Ref
- [29] . 2016. Fast model predictive control-based fuel efficient control strategy for a group of connected vehicles in urban road conditions. IEEE Transactions on Control Systems Technology 25, 2 (2016), 760–767.Google ScholarCross Ref
- [30] . 2020. A novel emergent intelligence technique for public transport vehicle allocation problem in a dynamic transportation system. IEEE Transactions on Intelligent Transportation Systems 16, 1 (2020), 487–498.Google Scholar
- [31] 2017. Eco-driving for transit: An effective strategy to conserve fuel and emissions. Applied Energy, 194 (2017), 784–97.Google ScholarCross Ref
- [32] . 2020. Dynamic eco-driving near signalized intersections: Systematic review and future research directions. Journal of Transportation Engineering, Part A: Systems 146, 4 (2020), 04020018.Google ScholarCross Ref
- [33] . 2020. Agent pseudonymous authentication-based conditional privacy preservation: An emergent intelligence technique. IEEE Systems Journal 14, 4 (2020), 5233–44.Google ScholarCross Ref
- [34] . 2018. Eco-driving for public transit in cyber-physical systems using V2I communication. International Journal of Intelligent Transportation Systems Research 16, 2 (2018), 79–89.Google ScholarCross Ref
- [35] . 2019. Emergent intelligence technique-based transport depot resource management in a metropolitan area. Journal on Vehicle Routing Algorithms 2, 1 (2019), 23–40.Google Scholar
- [36] . 2018. Real-time predictive cruise control for eco-driving taking into account traffic constraints. IEEE Transactions on Intelligent Transportation Systems 20, 8 (2018), 2858–2868.Google ScholarCross Ref
- [37] 2017. Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment. Transportation Research Part C: Emerging Technologies 79 (2017), 290–307.Google ScholarCross Ref
- [38] . 2021. Next Generation Simulation: Traffic Real Time Data Sets. https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm.Google Scholar
- [39] German Aerospace Center. 2021. Simulation of Urban MObility. https://www.eclipse.org/sumo/.Google Scholar
- [40] 2021. Open street map. Simulation of Urban MObility.Google Scholar
- [41] . 2021. Online vehicle velocity prediction using an adaptive radial basis function neural network. IEEE Transactions on Vehicular Technology 70, 4 (2021), 3113–22.Google ScholarCross Ref
Index Terms
- Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities
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