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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References
Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artif. Intell. Rev. 53(8), 5929–5955 (2020)
Du, S., Li, T., Horng, S.-J.: Time series forecasting using sequence-to-sequence deep learning framework. In: 9th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), p. 2018. IEEE (2018)
Wan, R., et al.: Multivariate temporal convolutional network: a deep neural networks approach for multivariate time series forecasting. Electronics 8(8), 876 (2019)
Sen, R., Yu, H.-F., Dhillon, I.: Think globally, act locally: a deep neural network approach to high-dimensional time series forecasting. arXiv preprint arXiv:1905.03806 (2019)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Yin, X., et al.: A comprehensive survey on traffic prediction. arXiv preprint arXiv:2004.08555 (2020)
Dai, J.J., et al.: BigDL: a distributed deep learning framework for big data. In: Proceedings of the ACM Symposium on Cloud Computing (2019)
Amini, S., Gerostathopoulos, I., Prehofer, C.: Big data analytics architecture forreal-time traffic control. In: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 710–715. IEEE(2017)
Saraswathi, A., Mummoorthy, A., Anantha Raman, G.R., Porkodi, K.: Real-time traffic moni-toring system using spark. In: 2019 International Conference on Emerging Trends in Science and Engineering (ICESE), vol. 1, pp. 1–6. IEEE (2019)
Anveshrithaa, S., Lavanya, K.: Real-time vehicle traffic analysis using long short term memory networks in apache spark. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (IC-ETITE). pp. 1–5. IEEE (2020)
Jiang, W., Luo, J.: Big data for traffic estimation and prediction: a survey of data and tools. arXiv preprint arXiv:2103.11824 (2021)
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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