Volume 14 , Issue 1 , PP: 218-226, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
R. Logesh Babu 1 * , Jagannadha Naidu K. 2 , V. Jeya Ramya 3 , Regan D. 4
Doi: https://doi.org/10.54216/JCIM.140115
Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using real-world VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices.
Vehicular Ad-Hoc Networks (VANETs) , traffic congestion prediction , Extreme Deep Learning Machines (EDRLM) , intelligent transportation systems (ITS) , deep neural networks
[1] “IEEE Standard for Local and metropolitan area networks, Amendment 6: Wireless Access in Vehicular Environments,” IEEE Std 802.11p-2010, pp. 1–51, 2010.
[2] C.K.MoorthyandB.G.Ratcliffe,“Shorttermtrafficforecasting usingtimeseriesmethods,”Transportation PlanningandTechnology,vol.12,no.1,pp.45–56,1988.
[3] J. Ma, X.-D. Li, and Y. Meng, “Research of urban traffic flow forecasting based on neural network,” Acta Electronica Sinica, vol.37,no.5,pp.1092–1094,2009.
[4] B. L. Smith and M. J. Demetsky, “Short-term traffic flow prediction: neural network approach,” Transportation Research Record,vol.1453,1994.
[5] M. Castro-Neto, Y.-S. Jeong, M.-K. Jeong, and L. D. Han, “Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions,” Expert Systems with Applications,vol.36,no.3,pp.6164–6173,2009.
[6] Chen A, Leung MT: Regression neural network for error correction in foreign exchange forecasting and trading. Comput Oper Res 31(7):1049-1068 (2004).
[7] Nag AK, Mitra A: Forecasting daily foreign exchange rates using genetically optimized neural networks. Journal of Forecasting 21(7):501-511 (2002).
[8] Sermpinis G, Stasinakis C, Theofilatos K, Karathanasopoulos A: Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations. Eur J Oper Res 247(3):831-846(2015).
[9] Perez-Murueta, Pedro; Gómez-Espinosa, Alfonso; Cardenas, Cesar; Gonzalez-Mendoza, Miguel, Jr., "Deep Learning System for Vehicular Re-Routing and Congestion Avoidance," Applied Sciences, Vol.9, No. 13, 2019. https://doi.org/10.3390/app9132717.
[10] Singh, H., V. Laxmi, A. Malik and Isha Batra. “Current Research on Congestion Control Schemes in VANET: A Practical Interpretation.” International Journal of Recent Technology and Engineering, Vol.8, No.4, pp. 4336-4341, 2019.
[11] Wei Kuang Lai, Mei-Tso Lin, Yu-Hsuan Yang, “A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks,” International Journal of Distributed Sensor Networks, Vol.11, No.3, pp.1-13, 2015.
[12] Ahmed, Muhammad & Iqbal, Saleem & Awan, Khalid & Sattar, Kashif & Khan, Zuhaib Ashfaq & Sherazi, Husnain, “A Congestion Aware Route Suggestion Protocol for Traffic Management in Internet of Vehicles,”. Arabian Journal for Science and Engineering, 2019. https://doi.org/10.1007/s13369-019-04099-9
[13] M. de Souza, R. S. Yokoyama, G. Maia, A. Loureiro and L. Villas, "Real-time path planning to prevent traffic jam through an intelligent transportation system," 2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 2016, pp. 726-731, doi: 10.1109/ISCC.2016.7543822.
[14] Pan, J.; Khan, M.A.; Popa, I.S.; Zeitouni, K.; Borcea, C. Proactive Vehicle Re-Routing Strategies for Congestion Avoidance. In Proceedings of the 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems, Hangzhou, China, 16–18 May 2012; pp. 265–272.
[15] Meneguette, R.I.; Ueyama, J.; Krishnamachari, B.; Bittencourt, L. Enhancing Intelligence in Inter-Vehicle Communications to Detect and Reduce Congestion in Urban Centers. In Proceedings of the 20th IEEE symposium on computers and communication (ISCC), Larnaca, Cyprus, 6–9 July 2015; pp. 662–667.
[16] M. de Souza, R. S. Yokoyama, L. C. Botega, R. I. Meneguette and L. A. Villas, "Scorpion: A Solution Using Cooperative Rerouting to Prevent Congestion and Improve Traffic Condition," 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK, 2015, pp. 497-503, doi: 10.1109/CIT/IUCC/DASC/PICOM.2015.71.
[17] Ma, Xiaolei & Dai, Zhuang & He, Zhengbing & Ma, Jihui & Wang, Yong & Wang, Yunpeng. “Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction,” Sensors, Vol.17.No. 818, 2017. 10.3390/s17040818.
