efficient traffic congestion control using intelligent ai algorithms
Janani L,Swathi .K,Thirisha M
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 23 April,2025 Pages:2043-2049
Abstract
The change in automobile fleet size and consequently within the quantity of traffic isn't accompanied through boom within the space of the road in all of the time. This cause traffic congestion commonly in all the urban areas. In order to avoid traffic jams, we are in situation to come up with a new solution. In preceding decades many technology were evolved and designed solutions to make road transportation safer. Some amongst these techniques were conventional where as other are incorporated. New designed systems are capable of informing drivers about the traffic situations and feasible hazards of the road way with the help of Artificial Intelligent transportation system. This structure consists of one module Software program module. The device makes use of new technology for actual-time collection, employer and transmission which offer the statistics to estimate the correct site traffic density exploited by using traffic-conscious applications
Kewords
EFFICIENT TRAFFIC, CONGESTION CONTROL, AI ALGORITHMS.
Reference
[1]. W. Song, H. Liu, and E. Zio, ‘‘Long-range dependence and heavy tail characteristics for remaining useful life prediction in rolling bearing degradation,’’ Appl. Math. Model., vol. 102, pp. 268–284, Feb. 2022.. [2]. Y. Gao, C. Zhou, J. Rong, Y. Wang and S. Liu, "Short-Term Traffic Speed Forecasting Using a Deep Learning Method Based on Multitemporal Traffic Flow Volume," in IEEE Access, vol. 10, pp. 82384-82395, 2022, doi: 10.1109/ACCESS.2022.3195353 [3] T. Syum Gebre, L. Beni, E. Tsehaye Wasehun and F. Elikem Dorbu, "AI-Integrated Traffic Information System: A Synergistic Approach of Physics Informed Neural Network and GPT-4 for Traffic Estimation and Real-Time Assistance," in IEEE Access, vol. 12, pp. 65869- 65882,2024,doi:10.1109/ACCESS.2024.3399094. [4]. T. Zhang, J. Xu, S. Cong, C. Qu and W. Zhao, "A Hybrid Method of Traffic Congestion Prediction and Control," in IEEE Access, vol. 11, pp. 36471- 36491, 2023, doi: 10.1109/ACCESS.2023.3266291. [5]. P. Liu, Z. Xie and T. Li, "UCN-YOLOv5: Traffic Sign Object Detection Algorithm Based on Deep Learning," in IEEE Access, vol. 11, pp. 110039-110050, 2023, doi: 10.1109/ACCESS.2023.3322371 [6]. S. Bartoletti, B. M. Masini, V. Martinez, I. Sarris, and A. Bazzi, ‘‘Impact of the generation interval on the performance of sidelink C-V2X autonomous mode,’’ IEEE Access, vol. 9, pp. 35121–35135, 2021. [7]. X. Meng and C. G. Cassandras, ‘‘Eco-driving of autonomous vehicles for nonstop crossing of signalized intersections,’’ IEEE Trans. Autom. Sci. Eng., vol. 19, no. 1, pp. 320–331, Jan. 2022. [8]. E. Wang, F. H. Memar, S. Korzelius, A. W. Sadek, and C. Qiao, ‘‘A reinforcement learning approach to CAV and intersection control for energy efficiency,’’ in Proc. 5th Int. Conf. Connected Auto. Driving (MetroCAD), Detroit, MI, USA, Apr. 2022, pp. 81–88. [9]. Y. Junkai, S. Jinju, L. Xunyi, and Y. Kangjian, ‘‘Speed planning and energy optimal control of hybrid electric vehicles based on Internet of Vehicles,’’ IFAC-PapersOnLine, vol. 54, no. 10, pp. 169–175, 2021 [10]. Z. Bai, P. Hao, W. ShangGuan, B. Cai, and M. J. Barth, ‘‘Hybrid reinforcement learning-based eco-driving strategy for connected and automated vehicles at signalized intersections,’’ IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 15850– 15863, Sep. 2022
