Abstract
Traffic forecasting is an important component of the Intelligent Transportation System (ITS). Recently, deep learning has been introduced as a promising method for traffic forecasting to deal with the exponential growth of data in ITS. In this regard, this paper focuses on applying a deep neural network model using LSTM for traffic forecasting based on analyzing data from the Vehicle Detection System (VDS). In particular, we first try to understand the traffic condition by applying visualization techniques. Then, based on the traffic condition, we apply an appropriate deep learning model for predicting traffic flow. Experiments in a certain urban area present promising results by applying the proposed model.
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Acknowledgment
This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00494, Development of deep learning-based urban traffic congestion prediction and signal control solution system) and Korea Institute of Science and Technology Information (KISTI) grant funded by the Korea government (MSIT) (K-19-L02-C07-S01).
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Yi, H., Bui, KH.N. (2019). VDS Data-Based Deep Learning Approach for Traffic Forecasting Using LSTM Network. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_46
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DOI: https://doi.org/10.1007/978-3-030-30241-2_46
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