Smart City Gnosys

Smart city article details

Title	An Accurate Traffic Flow Prediction Using Long-Short Term Memory And Gated Recurrent Unit Networks
ID_Doc	7345
Authors	Sawah M.S.; Taie S.A.; Ibrahim M.H.; Hussein S.A.
Year	2023
Published	Bulletin of Electrical Engineering and Informatics, 12, 3
DOI	http://dx.doi.org/10.11591/eei.v12i3.5080
Abstract	Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this study introduces two novel models. The first model uses two long-short term memory (LSTM) units that can extract the traffic flow temporal features followed by four dense layers to perform the traffic flow prediction. The second model uses two gated recurrent unit (GRU) units that can extract the traffic flow temporal features followed by three dense layers to perform the traffic flow prediction. The two proposed models give promising results on performance measurement system (PEMS), traffic and congestions (TRANCOS) dataset that is firstly used as metadata. So, the two models can do this in specific cases and are able to suddenly capture trend changes. © 2023, Institute of Advanced Engineering and Science. All rights reserved.
Author Keywords	Deep learning; Survellience camera; Traffic congestion; Traffic flow; Traffic management

Similar Articles

Id	Similarity	Authors	Title	Published
42767	0.94	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)
60227	0.936	Jang H.-C.; Chen C.-A.	Urban Traffic Flow Prediction Using Lstm And Gru †	Engineering Proceedings, 55, 1 (2024)
40920	0.923	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)
7669	0.921	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)
58544	0.921	Sravani B.; Shreyas A.V.; Abbas H.M.; Chanti Y.; Punitha S.	Traffic Congestion Prediction In Smart Cities Using Multilevel-Gated Recurrent Unit	International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024 (2024)
42842	0.914	Bartlett Z.; Han L.; Nguyen T.T.; Johnson P.	Prediction Of Road Traffic Flow Based On Deep Recurrent Neural Networks	Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 (2019)
35595	0.912	Anjaneyulu M.; Kubendiran M.	Long-Term Traffic Flow Prediction Using Hybrid Deep Learning Technique	International Journal of Engineering Trends and Technology, 71, 5 (2023)
48574	0.91	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)
27727	0.909	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)
58567	0.907	Wang Y.	Traffic Flow Forecasting In Smart Cities With Deep Learning	Proceedings of SPIE - The International Society for Optical Engineering, 13421 (2024)