{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:06:19Z","timestamp":1742918779687,"version":"3.40.3"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031291036"},{"type":"electronic","value":"9783031291043"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-29104-3_7","type":"book-chapter","created":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T05:02:47Z","timestamp":1680930167000},"page":"61-68","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-Time Traffic Prediction Using Distributed Deep Learning Based Multivariate Time-Series Models"],"prefix":"10.1007","author":[{"given":"Hoang-Thong","family":"Vo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quang-Linh","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gia-Huy","family":"Lam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ngan-Linh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trong-Hop","family":"Do","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen Thi","family":"Lieu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nhu-Ngoc","family":"Dao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,9]]},"reference":[{"issue":"8","key":"7_CR1","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"G Van Houdt","year":"2020","unstructured":"Van Houdt, G., Mosquera, C., N\u00e1poles, G.: A review on the long short-term memory model. Artif. Intell. Rev. 53(8), 5929\u20135955 (2020)","journal-title":"Artif. Intell. Rev."},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1109\/PAAP.2018.00037"},{"issue":"8","key":"7_CR3","doi-asserted-by":"publisher","first-page":"876","DOI":"10.3390\/electronics8080876","volume":"8","author":"R Wan","year":"2019","unstructured":"Wan, R., et al.: Multivariate temporal convolutional network: a deep neural networks approach for multivariate time series forecasting. Electronics 8(8), 876 (2019)","journal-title":"Electronics"},{"key":"7_CR4","unstructured":"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)"},{"key":"7_CR5","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)"},{"key":"7_CR6","unstructured":"Yin, X., et al.: A comprehensive survey on traffic prediction. arXiv preprint arXiv:2004.08555 (2020)"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Dai, J.J., et al.: BigDL: a distributed deep learning framework for big data. In: Proceedings of the ACM Symposium on Cloud Computing (2019)","DOI":"10.1145\/3357223.3362707"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"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\u2013715. IEEE(2017)","DOI":"10.1109\/MTITS.2017.8005605"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"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\u20136. IEEE (2019)","DOI":"10.1109\/ICESE46178.2019.9194613"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"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\u20135. IEEE (2020)","DOI":"10.1109\/ic-ETITE47903.2020.97"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, W., Luo, J.: Big data for traffic estimation and prediction: a survey of data and tools. arXiv preprint arXiv:2103.11824 (2021)","DOI":"10.3390\/asi5010023"}],"container-title":["Studies in Computational Intelligence","Intelligent Distributed Computing XV"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-29104-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T05:03:43Z","timestamp":1680930223000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-29104-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031291036","9783031291043"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-29104-3_7","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Intelligent and Distributed Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"idc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.idc2022.de\/#!\/welcome","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}