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Title	Predicting Traffic Flow With Deep Learning
ID_Doc	42767
Authors	Srivastava N.; Devarakonda R.; Ruthwik; Krishna V.; Bharadwaj B.; Gohil B.N.
Year	2024
Published	Lecture Notes in Networks and Systems, 995
DOI	http://dx.doi.org/10.1007/978-981-97-3292-0_37
Abstract	In transportation systems, a vast volume of traffic data is generated on a daily basis. The contributing factors for this traffic include expanding urban population, aging infrastructure, uncoordinated traffic timings, etc. Since traffic congestion costs valuable time and fuel every day, it needs to be monitored every day to avoid accidents and to enable the proper flow of traffic. This study aims to predict traffic flow using advanced forecasting techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and XGBoost models. Both the LSTM (Long-Short-Term Memory) and GRU (Gated Recurrent Unit) networks are used to predict the vehicle traffic flow and their prediction errors over different road junctions are compared to know which network works better. Experiments demonstrate that the proposed GRU model performs slightly superior to the LSTM model. The evaluation also shows that XGBoost-based methods perform the best in short-term and long-term traffic flow prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Author Keywords	Deep learning; GRU; Intelligent transportation systems; LSTM; Smart cities; Traffic flow prediction

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