Smart City Gnosys

Smart city article details

Title	A Deep Review: Techniques, Findings And Limitations Of Traffic Flow Prediction Using Machine Learning
ID_Doc	1395
Authors	Tripathi A.N.; Sharma B.
Year	2023
Published	Lecture Notes in Mechanical Engineering
DOI	http://dx.doi.org/10.1007/978-981-99-2921-4_3
Abstract	The evolution of machine learning techniques has generated creative solutions for smart cities, by which a life of human life can be easier. Amid the growing transportation data, the accurate traffic flow prediction has become a great requirement and hence regarded as an important for so many cites of reasonably a good size, which is a matter of worry which creates obstacle to continuous urban development. Nowadays, transportation data are exploding in the nature of big data. Presently, available traffic flow prediction models are less effective for many real-world applications. A short time ago, Intelligent Traffic System using deep learning has surfaced as a constructive and fruitful tool to lessen urban congestion and accurate traffic flow forecast. This study’s objective is to provide a thorough, well-organized assessment of the literature, which will include 29 publications from 2014 that were pulled from Web of Science, Scopus, and ScienceDirect. The extracted information includes the gaps, limitations, and future scopes for accurate and effective traffic movement prediction. Our research reflects that Convolutional Neural Network (CNN), Stacked Autoencoder (SAE), Long Short-Term Memory or hybrid are ML techniques that have been used frequently for the better and improved performance. In this paper, the proposed techniques are compared with shallow and traditional models. The Authors believe that this study provides an efficient manner for traffic estimation in smart cities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	And Hybrid machine learning; Connected vehicles; Deep learning; Predicting traffic jams; Traffic flow

Similar Articles

Id	Similarity	Authors	Title	Published
51592	0.955	Pritha A.; Fathima G.	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review	IET Conference Proceedings, 2024, 23 (2024)
8075	0.946	Zheng G.; Chai W.K.; Katos V.	An Ensemble Model For Short-Term Traffic Prediction In Smart City Transportation System	Proceedings - IEEE Global Communications Conference, GLOBECOM (2019)
13624	0.943	Uddin Gilani S.A.; Al-Rajab M.; Bakka M.	Challenges And Opportunities In Traffic Flow Prediction: Review Of Machine Learning And Deep Learning Perspectives; [Desafíos Y Oportunidades En La Predicción Del Flujo De Tráfico: Revisión De Las Perspectivas De Aprendizaje Automático Y Aprendizaje Profundo]	Data and Metadata, 3 (2024)
58592	0.94	Cenni D.; Han Q.	Traffic Flow Prediction Using Uber Movement Data	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 594 LNICST (2024)
58620	0.94	Almukhalfi H.; Noor A.; Noor T.H.	Traffic Management Approaches Using Machine Learning And Deep Learning Techniques: A Survey	Engineering Applications of Artificial Intelligence, 133 (2024)
58567	0.938	Wang Y.	Traffic Flow Forecasting In Smart Cities With Deep Learning	Proceedings of SPIE - The International Society for Optical Engineering, 13421 (2024)
32606	0.938	Kumar A.; Ranjan R.	Intelligent Traffic Identification System Powered Byconvolutional Neural Networks	ACM International Conference Proceeding Series (2023)
48697	0.938	Bilotta S.; Collini E.; Nesi P.; Pantaleo G.	Short-Term Prediction Of City Traffic Flow Via Convolutional Deep Learning	IEEE Access, 10 (2022)
42773	0.936	Zhu W.; Kong H.; Cai W.; Zhu W.	Predicting Urban Traffic Flow Based On Deep Meta-Learning	ACM International Conference Proceeding Series (2024)
61010	0.934	Almeida A.; Brás S.; Oliveira I.; Sargento S.	Vehicular Traffic Flow Prediction Using Deployed Traffic Counters In A City	Future Generation Computer Systems, 128 (2022)