Open Access
| Issue |
E3S Web of Conf.
Volume 547, 2024
International Conference on Sustainable Green Energy Technologies (ICSGET 2024)
|
|
|---|---|---|
| Article Number | 03007 | |
| Number of page(s) | 8 | |
| Section | Energy | |
| DOI | https://doi.org/10.1051/e3sconf/202454703007 | |
| Published online | 09 July 2024 | |
- César Peláez-Rodríguez, Jorge Pérez-Aracil, Dušan Fister, Ricardo Torres-López, Sancho Salcedo-Sanz, Bike sharing and cable car demand forecasting using machine learning and deep learning multivariate time series approaches’, Expert Systems with Applications • (October 2023) [Google Scholar]
- Tae You Kim, Min Jae Park, Jiho Shin, Sungwon Oh, Prediction of Bike Share Demand by Machine Learning: Role of Vehicle Accident as the New Feature, International Journal of Business Analytics Volume 9 • Issue 1 [Google Scholar]
- Yajun Zhou, Lilei Wang, Rong Zhong, and Yulong Tan, A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing System, Hindawi Mathematical Problems in Engineering Volume (2018), Article ID 8028714, 8 pages [Google Scholar]
- Liang Yu, Tao Feng, Tie Li, Lei Cheng, Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations, Urban Rail Transit (2023). [Google Scholar]
- Yi Shi, Liumei Zhang, Shengnan Lu and Qiao Liu, Short-Term Demand Prediction of Shared Bikes Based on LSTM Network’, Electronics (2023),12, 1381. [CrossRef] [Google Scholar]
- Aliasghar Mehdizadeh Dastjerdi and Catherine Morency, Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning, Sensors (2022), 22, 1060. https://doi.org/10.3390/s22031060 [CrossRef] [PubMed] [Google Scholar]
- Akshay Patil, Karishma Musale and BVANSS Prabhakar Rao, ‘Bike Share Demand Prediction using RandomForests’, IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 4, (April 2015). [Google Scholar]
- Xin Liu, Konstantinos Pelechrinis, Excess demand prediction for bike sharing systems, (June 17, 2021). https://doi.org/10.1371/journal.pone.0252894 [Google Scholar]
- Xinyu Li, Yang Xua, Xiaohu Zhang, Wenzhong Shia, Yang Yue, Qingquan Li, Improving short-term bike sharing demand forecast through an irregular convolutional neural network, arXiv:2202.04376v2 [cs.AI] (11 Feb 2022) [Google Scholar]
- Abe Zeid, Trisha Bhatt, and Hayley A. Morris, Machine Learning Model to Forecast Demand of Boston Bike-Ride Sharing, European Journal of Artificial Intelligent and Machine Learning, Issue 3 | (May 2022)DOI: http://dx.doi.org/10.24018/ejai.2022.1.3.9 Vol 1 | [Google Scholar]
- Daniele Gammelli, Yihua Wang, Dennis Prak, Filipe Rodrigues, Stefan Minner,Francisco Camara Pereir, Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management, arXiv:2108.00858v1 [math.OC](28 Jul 2021) [Google Scholar]
- Sathishkumar V E & Yongyun Cho, A rule-based model for Seoul Bike sharing demand prediction using weather dat’,( EUROPEAN JOURNAL OF REMOTE SENSING 2020), VOL.53, NO. S1, 166–183 https://doi.org/10.1080/22797254.2020.1725789 [CrossRef] [Google Scholar]
- Guido Cantelmo, Rafał Kucharski, and Constantinos Antoniou, Low-Dimensional Model for Bike-Sharing Demand Forecasting that Explicitly Accounts for Weather Data, Transportation Research Record 2674(8) [PubMed] [Google Scholar]
- Akshay Ajagekar, Travis Humble, Fengqi You, Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems’, Version of Record: https://www.sciencedirect.com/science/article/pii/ S0098135419307665 [Google Scholar]
- Ashutosh Kumar Singh, A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads, arXiv:2211.14619v1 [cs.DC] (26 Nov 2022) [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.