Smart City Gnosys

Smart city article details

Title	Long-Term Traffic Flow Prediction Using Hybrid Deep Learning Technique
ID_Doc	35595
Authors	Anjaneyulu M.; Kubendiran M.
Year	2023
Published	International Journal of Engineering Trends and Technology, 71, 5
DOI	http://dx.doi.org/10.14445/22315381/IJETT-V71I5P216
Abstract	Smart city traffic regulation relies heavily on advanced traffic management systems (ATMS), a key component of the broader intelligent transportation system (ITS). Traffic flow forecasting is a crucial aspect of transportation that aids in traffic planning, control, management, and information dissemination. Although there are a great variety of models whose primary focus is on the development of short-term traffic flow predictions, making credible long-term traffic flow (LTTF) forecasts has become an increasingly difficult topic in recent years. To solve this problem, this paper proposed a novel hybrid model called the autoencoder gated recurrent unit (AEGRU) that can accurately predict long-term traffic flow for the next 24 hours. Firstly, the autoencoder (AE) will take the raw data and pick out the most important features before doing dimensionality reduction. Secondly, the gated recurrent unit (GRU) uses the information given by the AE to make predictions about how much traffic volume there will be in the future. The outcomes of the evaluation show that the proposed AEGRU model is much better than compared approaches in terms of root mean square error (RMSE) of 1.6% mean absolute percentage error (MAPE) of 2.3% and mean absolute error (MAE) of 1.9%. © 2023 Seventh Sense Research Group®.
Author Keywords	Autoencoder; Deep learning; Gated recurrent unit; Long-term traffic flow prediction; Neural networks; Traffic flow prediction

Similar Articles

Id	Similarity	Authors	Title	Published
42767	0.921	Srivastava N.; Devarakonda R.; Ruthwik; Krishna V.; Bharadwaj B.; Gohil B.N.	Predicting Traffic Flow With Deep Learning	Lecture Notes in Networks and Systems, 995 (2024)
7345	0.912	Sawah M.S.; Taie S.A.; Ibrahim M.H.; Hussein S.A.	An Accurate Traffic Flow Prediction Using Long-Short Term Memory And Gated Recurrent Unit Networks	Bulletin of Electrical Engineering and Informatics, 12, 3 (2023)
27727	0.907	Saini K.; Sharma S.	Gated Recurrent Unit (Gru) In Rnn For Traffic Forecasting Based On Time-Series Data	Proceedings - 2022 2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022 (2022)
60227	0.906	Jang H.-C.; Chen C.-A.	Urban Traffic Flow Prediction Using Lstm And Gru †	Engineering Proceedings, 55, 1 (2024)
40920	0.905	Abdullah S.M.; Periyasamy M.; Kamaludeen N.A.; Towfek S.K.; Marappan R.; Kidambi Raju S.; Alharbi A.H.; Khafaga D.S.	Optimizing Traffic Flow In Smart Cities: Soft Gru-Based Recurrent Neural Networks For Enhanced Congestion Prediction Using Deep Learning	Sustainability (Switzerland), 15, 7 (2023)
13624	0.901	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)
48574	0.898	Zhang W.; Li X.; Li A.; Huang X.; Wang T.; Gao H.	Sgru: A High-Performance Structured Gated Recurrent Unit For Traffic Flow Prediction	Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS (2023)
58567	0.897	Wang Y.	Traffic Flow Forecasting In Smart Cities With Deep Learning	Proceedings of SPIE - The International Society for Optical Engineering, 13421 (2024)
1395	0.896	Tripathi A.N.; Sharma B.	A Deep Review: Techniques, Findings And Limitations Of Traffic Flow Prediction Using Machine Learning	Lecture Notes in Mechanical Engineering (2023)
7669	0.894	Jawad-Ur-Rehman C.; Ul Haq I.; Muneeb M.	An Attention-Based Recurrent Learning Model For Short-Term Travel Time Prediction	PLoS ONE, 17, 12 December (2022)