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Smart city article details

Title	Traffic Forecasting With Deep Learning
ID_Doc	58601
Authors	Kundu S.; Desarkar M.S.; Srijith P.K.
Year	2020
Published	2020 IEEE Region 10 Symposium, TENSYMP 2020
DOI	http://dx.doi.org/10.1109/TENSYMP50017.2020.9230762
Abstract	Timely forecast of traffic is very much needed for smart cities, which allows travelers and government agencies to make various decisions based on traffic flow. This will result in reduced traffic congestion and carbon dioxide emission. However, traffic forecasting is a challenging task due to the highly complex traffic pattern. Standard time series techniques may not be able to capture the nonlinear and noisy nature of the traffic flow. In this paper, we investigate how the deep learning models capture these characteristics and provide better predictive performance over standard time series and regression models. We compare the performances of state-of-the-art deep learning models on two traffic flow data sets and show their effectiveness in traffic flow prediction over traditional models. © 2020 IEEE.
Author Keywords	CNN; deep learning; LSTM; smart city; Traffic prediction

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