Bike sharing usage prediction with deep learning: a survey
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07380-5.pdf
Reference94 articles.
1. Albuquerque V, Sales Dias M, Bacao F (2021) Machine learning approaches to bike-sharing systems: a systematic literature review. ISPRS Int J Geo Inf 10(2):62
2. Bai L, Yao L, Kanhere SS, et al (2019) Stg2seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting. In: Proceedings of the 28th international joint conference on artificial intelligence, AAAI Press, pp 1981–1987
3. Banet K, Naumov V, Kucharski R (2021) Using city-bike stopovers to reveal spatial patterns of urban attractiveness. Current Issues in Tourism pp 1–18
4. Beairsto J, Tian Y, Zheng L, et al (2021) Identifying locations for new bike-sharing stations in Glasgow: an analysis of spatial equity and demand factors. Annals of GIS pp 1–16
5. Billhardt H, Fernández A, Ossowski S (2021) Smart recommendations for renting bikes in bike-sharing systems. Appl Sci 11(20):9654
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