Open Access
| Issue |
E3S Web of Conf.
Volume 531, 2024
Ural Environmental Science Forum “Sustainable Development of Industrial Region” (UESF-2024)
|
|
|---|---|---|
| Article Number | 02012 | |
| Number of page(s) | 7 | |
| Section | Electric Mobility, Decarbonizing Energy Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202453102012 | |
| Published online | 03 June 2024 | |
- Lv, Yisheng, et al. “Traffic flow prediction with big data: A deep learning approach.” Ieee transactions on intelligent transportation systems 16.2 (2014): 865-873. [Google Scholar]
- Kaffash, Sepideh, An Truong Nguyen, and Joe Zhu. “Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis.” International journal of production economics 231 (2021): 107868. [CrossRef] [Google Scholar]
- Shengdong, Mu, Xiong Zhengxian, and Tian Yixiang. “Intelligent traffic control system based on cloud computing and big data mining.” IEEE Transactions on Industrial Informatics 15.12 (2019): 6583-6592. [CrossRef] [Google Scholar]
- Rizwan, Patan, K. Suresh, and M. Rajasekhara Babu. “Real-time smart traffic management system for smart cities by using Internet of Things and big data.” 2016 international conference on emerging technological trends (ICETT). IEEE, 2016. [Google Scholar]
- Zhu, Li, et al. “Big data analytics in intelligent transportation systems: A survey.” IEEE Transactions on Intelligent Transportation Systems 20.1 (2018): 383-398. [Google Scholar]
- Wang, Chao, et al. “Soft computing in big data intelligent transportation systems.” Applied Soft Computing 38 (2016): 1099-1108. [CrossRef] [Google Scholar]
- Boukerche, Azzedine, and Jiahao Wang. “Machine learning-based traffic prediction models for intelligent transportation systems.” Computer Networks 181 (2020): 107530. [CrossRef] [Google Scholar]
- Bentéjac, Candice, Anna Csörgő, and Gonzalo Martínez-Muñoz. “A comparative analysis of gradient boosting algorithms.” Artificial Intelligence Review 54 (2021): 1937-1967. [CrossRef] [Google Scholar]
- Zhang, Yanru, and Ali Haghani. “A gradient boosting method to improve travel time prediction.” Transportation Research Part C: Emerging Technologies 58 (2015): 308-324. [CrossRef] [Google Scholar]
- Abdullaev, Eldor, Vakhid Zakirov, and Farrukh Shukurov. “Assessment of the distance learning server's operation strategies and service capacity in advance.” E3S Web of Conferences. Vol. 420. EDP Sciences, 2023. [Google Scholar]
- Zakirov, Vakhid, and Eldor Abdullaev. “Enhancing the efficiency of the remote service process.” E3S Web of Conferences. Vol. 501. EDP Sciences, 2024. [Google Scholar]
- Rasulmukhamedov, Mahamadaziz, Avaz Boltaev, and Adham Tukhtakhodjaev. “Modeling of the electronic document circulation and record keeping system in the processes of cargo transportation in railway transport.” E3S Web of Conferences. Vol. 458. EDP Sciences, 2023. [Google Scholar]
- Abdullaev, Eldor, et al. “Transformer oils testing laboratories development of electronic process recording system.” AIP Conference Proceedings. Vol. 2789. No. 1. AIP Publishing, 2023. [Google Scholar]
- Li, Zili, Zuduo Zheng, and Simon Washington. “Short-term traffic flow forecasting: A component-wise gradient boosting approach with hierarchical reconciliation.” IEEE Transactions on Intelligent Transportation Systems 21.12 (2019): 5060-5072. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.