Smart City Gnosys

Smart city article details

Title	Selection Of Efficient And Accurate Prediction Algorithm For Employing Real Time 5G Data Load Prediction
ID_Doc	48117
Authors	Shrivastava P.; Patel S.
Year	2021
Published	2021 IEEE 6th International Conference on Computing, Communication and Automation, ICCCA 2021
DOI	http://dx.doi.org/10.1109/ICCCA52192.2021.9666235
Abstract	In smart cities applications (i.e. intelligent transport systems, traffic management) cellular traffic load prediction is playing an essential role. The cellular data consumption can help to understand the road traffic patterns. In this context, the employment of predictive Machine Learning (ML)techniques can be useful for approximating the possible resource demands in cell towers. Therefore, cellular traffic data may very useful for finding trends and patterns of load of human activities in city traffic. In this paper, the main aim is to identify the suitable machine learning techniques, which can be used for traffic load prediction in a smart city application. The paper includes three main contributions, first providing an overview of 5G technology and their applications, second, a review on existing traffic load prediction techniques and finally, a comparative experimental study is performed among popular supervised and unsupervised learning approaches. In order to compare the performance of supervised learning algorithms, Support Vector Machine (SVM), Artificial Neural Network (ANN), Bays classifier, Linear Regression (LR), and Decision Tree (DT) are implemented. On the other hand, for comparing the performance of unsupervised learning algorithms the Self Organizing Map (SOM), Fuzzy C Means (FCM), and k-Means clustering algorithms have been involved. The experiments on publically available data on Kaggle for 4G (LTE Traffic Prediction) were used. According to the experimental analysis, SVM and ANN are accurate algorithms in the supervised learning algorithms. On the other side in unsupervised learning models, SOM shows superior accuracy. But after summarizing the results we found that the SVM and ANN algorithms are beneficial for the proposed application. © 2021 IEEE.
Author Keywords	cellular data; machine learning; smart city traffic management; supervised and unsupervised learning; traffic load prediction

Similar Articles

Id	Similarity	Authors	Title	Published
59474	0.889	Gupta S.; Suman U.	Understanding Of Cellular Network Workload And Prediction Using Machine Learning Approach	3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023 (2023)
31957	0.876	Guru Vimal Kumar M.; Jude Moses Anto Devakanth J.; Selvapandian D.; Venkatesan R.; Revathi J.; Aruna R.	Integrating Cloud-Based Data Mining Algorithms For Smart City Infrastructure Management And Decision Support Systems	Proceedings - 2024 4th International Conference on Soft Computing for Security Applications, ICSCSA 2024 (2024)
6021	0.875	Yang T.J.; Nordin R.; Abdullah N.F.B.; Fauzi M.F.A.; Hliang N.W.	Accurate Fifth Generation Mobile Network Coverage Prediction In Smart Cities With Machine Learning	2024 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024 (2024)
21773	0.872	Parveen Banu S.; Patil Y.M.; Somasundaram R.; Santhosh C.; Singh D.P.; Manikandan G.	Edge Computing-Based Short-Term Traffic Flow Forecast For The Smart City Employing 5G Internet Vehicles	Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2024 (2024)
60507	0.871	Christantonis, K; Tjortjis, C; Manos, A; Filippidou, DE; Mougiakou, E; Christelis, E	Using Classification For Traffic Prediction In Smart Cities	ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2020, PT I, 583 (2020)
37160	0.871	Ei Leen M.W.; Jafry N.H.A.; Salleh N.M.; Hwang H.J.; Jalil N.A.	Mitigating Traffic Congestion In Smart And Sustainable Cities Using Machine Learning: A Review	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13957 LNCS (2023)
22862	0.868	Jenifer J.; Jemima Priyadarsini R.	Empirical Research On Machine Learning Models And Feature Selection For Traffic Congestion Prediction In Smart Cities	International Journal on Recent and Innovation Trends in Computing and Communication, 11 (2023)
55683	0.867	Punjabi M.; Gupta R.	The Impact Of Artificial Intelligence On Energy Optimization For Cellular Network Base Stations	Intelligent Networks: Techniques, and Applications (2024)
15826	0.866	Yu M.	Construction Of Regional Intelligent Transportation System In Smart City Road Network Via 5G Network	IEEE Transactions on Intelligent Transportation Systems, 24, 2 (2023)
29829	0.865	Alsolami B.; Mehmood R.; Albeshri A.	Hybrid Statistical And Machine Learning Methods For Road Traffic Prediction: A Review And Tutorial	EAI/Springer Innovations in Communication and Computing (2020)