Deep Learning Approaches for Traffic Prediction Forecasting in Multi-Level Cloud Architecture for Autonomous Vehicle Services
https://doi.org/10.31854/1813-324X-2022-8-4-89-99
Abstract
Autonomous vehicle (AV) is one of the most new use cases and a technology for fifth-generation (5G) and next-generation mobile networks in numerous applications., the use of AVs has exponentially worldwide due to the rapidly growing awareness and use of artificial intelligence (AI) methods in various fields. Predicting data flows is essential for AVs to improve data transmission and decrease delays through more efficient use of appropriate capabilities, monitoring, management, and control of the traffic system. This paper proposes a deep learning approach (DL) with the bidirectional long-short-term memory model (BI-LSTM) for predicting the traffic rates of AVs with multi-cloud services. In terms of prediction accuracy, a comparison is conducted between the BI-LSTM and the unidirectional LSTM based on the number of batch sizes used. The prediction accuracy is computed using the root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and processing time. The results show that the prediction accuracy with BI-LSTM outperforms the LSTM model. Besides, the prediction accuracy using 8 batch sizes outperforms the competitors and offers outstanding performance.
About the Author
M. Alsweity
The Bonch-Bruevich Saint-Petersburg State University of Telecommunications
Russian Federation
Russian Federation
Malik Alsweity
St. Petersburg, 193232
References
1. Abdellah A.R., Alshahrani A., Muthanna A., Koucheryavy A. Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers. Symmetry. 2021;13(11);2207. DOI:10.3390/sym13112207
2. Chochliouros I.P., Spiliopoulou A.S., Lazaridis P., Dardamanis A., Zaharis Z., Kostopoulos A. Dynamic Network Slicing: Challenges and Opportunities. Proceedings of the International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020, 5–7 June 2020, Neos Marmaras, Greece. IFIP WG 12.5 International Workshops. IFIP Advances in Information and Communication Technology, vol.585. Cham: Springer; 2020. DOI:10.1007/978-3-030-49190-1_5
3. Miglani A., Kumar N. Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges. Vehicular Communications. 2019;20:100184. DOI:10.1016/j.vehcom.2019.100184
4. Gillani M., Niaz H.A., Farooq M.U., Ullah A. Data collection protocols for VANETs: a survey. Complex & Intelligent Systems. 2022;8(3):2593‒2622. DOI:10.1007/s40747-021-00629-x
5. Grigorescu S, Trasnea B, Cocias T, Macesanu G. A survey of deep learning techniques for autonomous driving. Journal of Field Robotics. 2020:37(3):362–386. DOI:10.1002/rob.21918
6. Pfülb B., Hardegen C., Gepperth A., Rieger S. A Study of Deep Learning for Network Traffic Data Forecasting. Proceedings of the 28th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2019, 17–19 September 2019, Munich, Germany. Lecture Notes in Computer Science, vol.11730. Cham: Springer; 2019. p.497–512. DOI:10.1007/978-3-030-30490-4_40
7. Ateeq M., Ishmanov F., Afzal M.K., Naeem M. Predicting Delay in IoT Using Deep Learning: A Multiparametric Approach. IEEE Access. 2019;7:62022‒62032. DOI:10.1109/ACCESS.2019.2915958
8. Abdellah A.R., Koucheryavy A. Deep Learning with Long Short-Term Memory for IoT Traffic Prediction. Proceedings of the 20th International Conference on Next Generation Networks and Systems, NEW2AN 2020, and 13th Conference on Internet of Things, Smart Spaces, ruSMART 2020, 26–28 August 2020, St. Petersburg, Russia. Lecture Notes in Computer Science, vol.12525. Cham: Springer; 2020. p.267–280. DOI:10.1007/978-3-030-65726-0_24
9. Almutairi M.S. Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction. Mathematical Problems in Engineering. 2022;2022:6855435. DOI:10.1155/2022/6855435
10. Kaur J., Khan M.A., Iftikhar M., Imran M., Haq Q.E.U. Machine Learning Techniques for 5G and Beyond. IEEE Access. 2021;9:23472‒23488. DOI:10.1109/ACCESS.2021.3051557
11. Abdellah A.R., Volkov A., Muthanna A., Gallyamov D., Koucheryavy A. Deep Learning for IoT Traffic Prediction Based on Edge Computing. Proceedings of the 23rd International Conference on Distributed Computer and Communication Networks: Control, Computation, Communications, DCCN 2020, 14‒18 September 2020, Moscow, Russia. Communications in Computer and Information Science, vol.1337. p.18‒29. Cham: Springer, 2020. DOI:10.1007/978-3-030-66242-4_2
12. Khedkar S.P., Canessane R.A., Najafi M.L. Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms. Wireless Communications and Mobile Computing. 2021;2021:5366222. DOI:10.1155/2021/5366222
13. Selvamanju E., Shalini V.B. Machine Learning Based Mobile Data Traffic Prediction in 5G Cellular Networks. Proceedings of the 5th International Conference on Electronics, Communication and Aerospace Technology, ICECA, 02‒04 December 2021, Coimbatore, India. IEEE; 2021. p.1318‒1324. DOI:10.1109/ICECA52323.2021.9675887
14. Choi D., Yim J., Baek M., Lee S. Machine Learning-Based Vehicle Trajectory Prediction Using V2V Communications and On-Board Sensors. Electronics. 2021;10(4):420. DOI:10.3390/electronics10040420
15. Rasouli A., Tsotsos J.K. Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice. IEEE Transactions on Intelligent Transportation Systems. 2020;21(3):900‒918. DOI:10.1109/TITS.2019.2901817
16. Badicu A., Suciu G., Balanescu M., Dobrea M., Birdici A., Orza O., Pasat A. PMs concentration forecasting using ARIMA algorithm. Proceedings of the 91-st Vehicular Technology Conference, VTC2020-Spring, 25–28 May 2020, Antwerp, Belgium. IEEE; 2020. DOI:10.1109/VTC2020-Spring48590.2020.9129390
17. Shang Q., Tan D., Gao S., Feng L. A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis. Journal of Advanced Transportation. 2019;2019:4202735. DOI:10.1155/2019/4202735
18. Kucheryavy A. Network Delay Communications Networks. Trudy NIIR. 2019;1:69‒74. (in Russ.)
19. Muthanna A.S. Distributed Intelligent Communication Network Architecture for Unmanned Vehicles. Electrosvyaz. 2020;7:29‒34. (in Russ.) DOI: 10.34832/ELSV.2020.8.7.004
20. Vladyko A.G., Muthanna A.S., Koucheryavy A.Eu. Traffic Offloading Method for V2X/5G Networks Based on the Edge Computing System. Electrosvyaz. 2020;8:24‒30. (in Russ.) DOI: 10.34832/ELSV.2020.9.8.004
21. Shrestha R., Bajracharya R., Nam S.Y. Challenges of Future VANET and Cloud-Based Approaches. Wireless Communications and Mobile Computing. 2018;2018:5603518. DOI:10.1155/2018/5603518
22. Plotnikov P., Vladyko A. Minimizing Delays in the Interaction of Edge Devices Using Clustering in VANETs. Proceedings of Telecommunication Universities. 2022;8(2):6‒13. (in Russ.) DOI:10.31854/1813-324X-2022-8-2-6-13
Review
For citations:
Alsweity M. Deep Learning Approaches for Traffic Prediction Forecasting in Multi-Level Cloud Architecture for Autonomous Vehicle Services. Proceedings of Telecommunication Universities. 2022;8(4):89-99. (In Russ.) https://doi.org/10.31854/1813-324X-2022-8-4-89-99
Views:
479
Email this article 