References
- [1] Y.Z. Wong, D.A. Hensher, and C. Mulley, “Mobility as a service (MaaS): Charting a future context”, Transp. Res. Part A Policy Pract., vol. 131, pp. 5-19, 2020.10.1016/j.tra.2019.09.030
- [2] D.A. Hensher, C. Mulley, C. Ho, Y. Wong, G. Smith, and J. D. Nelson, Understanding Mobility as a Service (MaaS): Past, present and future. Elsevier, 2020.
- [3] S. Heikkilä et al., “Mobility as a service-a proposal for action for the public administration, case Helsinki”, 2014.
- [4] S. Hietanen, “Mobility as a Service”. The new transport model, vol. 12, no. 2, pp. 2-4, 2014.
- [5] A. Kaltenbrunner, R. Meza, J. Grivolla, J. Codina, and R. Banchs, “Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system”, Pervasive Mob. Comput., vol. 6, no. 4, pp. 455-466, 2010.10.1016/j.pmcj.2010.07.002
- [6] Priyavrat, N. Sharma, and G. Sikka, “Autoregressive Techniques for Forecasting Applications”, in 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC), 2021.10.1109/ICSCCC51823.2021.9478161
- [7] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators”, Neural Netw., vol. 2, no. 5, pp. 359-366, 1989.10.1016/0893-6080(89)90020-8
- [8] S. Hochreiter and J. Schmidhuber, “Long short-term memory”. Neural computation vol. 9, no. 8, pp. 1735-1780, 1997.10.1162/neco.1997.9.8.1735
- [9] K.L. Clarkson, “Algorithms for closest-point problems (computational geometry)”. Ph.D. dissertation. Stanford University, 1985.
- [10] D. Ljubenkov, F. Kon, and C. Ratti, “Optimizing bike sharing system flows using graph mining, convolutional and recurrent neural networks”, in 2020 IEEE European Technology and Engineering Management Summit (E-TEMS), 2020.10.1109/E-TEMS46250.2020.9111707
- [11] K. Chen, Y. Zhou, and F. Dai, “A LSTM-based method for stock returns prediction: A case study of china stock market”, 2015 IEEE International Conference on Big Data (Big Data), 2015.10.1109/BigData.2015.7364089
- [12] R. Fu, Z. Zhang, and L. Li, “Using LSTM and GRU neural network methods for traffic flow prediction”, in 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2016.10.1109/YAC.2016.7804912
- [13] C. Xu, J. Ji, and P. Liu, “The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets”, Transp. Res. Part C Emerg. Technol., vol. 95, pp. 47-60, 2018.10.1016/j.trc.2018.07.013
- [14] K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation”, arXiv [cs.CL], 2014.10.3115/v1/D14-1179
- [15] S. Ruffieux, E. Mugellini, and O. Abou Khaled, “Bike usage forecasting for optimal rebalancing operations in bike-sharing systems”, in 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018.10.1109/ICTAI.2018.00133
- [16] “Local weather forecast, news and conditions”, Wunder-ground.com. [Online]. Available: http://www.wunder-ground.com. [Accessed: 05-Dec-2021].
- [17] “CalendarDate.com”, Calendardate.com. [Online]. Available: http://www.calendardate.com. [Accessed: 05-Dec-2021].
- [18] S.H. Ewaid, S.A. Abed, and S.A. Kadhum, “Predicting the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis”, Environ. technol. innov., vol. 11, pp. 390-398, 2018.10.1016/j.eti.2018.06.013
- [19] P.-F. Tsai et al., “A classification algorithm to predict chronic pain using both regression and machine learning – A stepwise approach”, Appl. Nurs. Res., vol. 62, no. 151504, p. 151504, 2021.10.1016/j.apnr.2021.151504
- [20] M.A. Efroymson, “Multiple regression analysis”, A. Ralston and H. S. Wilf, Eds. New York: Wiley, 1960.
- [21] Sathishkumar and Y. Cho, “A rule-based model for Seoul Bike sharing demand prediction using weather data”, Eur. J. Remote Sens., vol. 53, no. sup1, pp. 166-183, 2020.10.1080/22797254.2020.1725789
- [22] G.E.P. Box and E. Al, Time series analysis: Forecasting and control. Hoboken, New Jersey: John Wiley & Sons, 2015.
- [23] D.P. Kingma and J. Ba, “Adam: A method for stochastic optimization”. arXiv preprint arXiv:1412.6980, 2014.
- [24] S.V.J. Park, and Y. Cho, “Using data mining techniques for bike sharing demand prediction in metropolitan city”, Comput. Commun., vol. 153, pp. 353-366, 2020.10.1016/j.comcom.2020.02.007
- [25] R.C. Team, “A language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing”, URL http://www.R-project.org, 2013.
- [26] Y. Fourcade, A.G. Besnard, and J. Secondi, “Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics”, Glob. Ecol. Biogeogr., vol. 27, no. 2, pp. 245-256, 2018.10.1111/geb.12684