Abstract
Traffic prediction plays an extremely important role in the intelligent transportation system. The accuracy of traffic prediction helps to reduce traffic jams and helps intelligent transportation system (ITS). However, this problem is also a difficult challenge to solve because of complicated and dynamic spatio-temporal dependencies between different regions in the road network. In this study, we contribute the following suggestions: First, a multivariate approach using feature extraction techniques to increase the performance of the model. Second, we perform a comparative experimental study to evaluate different models, identifying the most effective component. Models are built on distributed and parallel computing platforms.
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Acknowledgment
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1G1A1008105).
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Vo, HT. et al. (2023). Real-Time Traffic Prediction Using Distributed Deep Learning Based Multivariate Time-Series Models. In: Braubach, L., Jander, K., Bădică, C. (eds) Intelligent Distributed Computing XV. IDC 2022. Studies in Computational Intelligence, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-031-29104-3_7
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DOI: https://doi.org/10.1007/978-3-031-29104-3_7
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