Use of Regression Algorithm for Bike Ride Sharing Demand Projection

Author:

Korasawala Husain,Pangaonkar Satyajit,Gunjan Reena,Rokade Prakash

Publisher

Springer Nature Switzerland

Reference17 articles.

1. Y. Guo, J. Zhou, Y. Wu, Z. Li, Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo. China. PloS one 12(9), 1–19 (2017)

2. M. Xinwei, Y. Yuan, N. Oort, S. Hoogendoorn, Bike-sharing systems’ impact on modal shift: A case study in Delft, The Netherlands. J. Clean. Prod. 259, 1–24 (2020)

3. S. Reiss, Bogenberger.: GPS-data analysis of Munich's free-floating bike sharing system and application of an operator-based relocation strategy, in In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, (Gran Canaria, Spain, 2015), pp. 584–589

4. Y. Shi, L. Zhang, S. Lu, Q. Liu, Short-Term Demand Prediction of Shared Bikes Based on LSTM Network. Electronics 12, 1381 (2023)

5. W. El-Assi, M.N. Mahmoud, K. Habib, Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation 1, 1–40 (2017)

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