Accuracy of Hourly Demand Forecasting of Micro Mobility for Effective Rebalancing Strategies

• Autorzy: Kanokporn Boonjubut , Hiroshi Hasegawa
• Języki publikacji: EN

Abstrakty

EN

The imbalance in bike-sharing systems between supply and demand is significant. Therefore, these systems need to relocate bikes to meet customer needs. The objective of this research is to increase the efficiency of bike-sharing systems regarding rebalancing problems. The prediction of the demand for bike sharing can enhance the efficiency of a bike-sharing system for the operation process of rebalancing in terms of the information used in planning by proposing an evaluation of algorithms for forecasting the demand for bikes in a bike-sharing network. The historical, weather and holiday data from three distinct databases are used in the dataset and three fundamental prediction models are adopted and compared. In addition, statistical approaches are included for selecting variables that improve the accuracy of the model. This work proposes the accuracy of different models of artificial intelligence techniques to predict the demand for bike sharing. The results of this research will assist the operators of bike-sharing companies in determining data concerning the demand for bike sharing to plan for the future. Thus, these data can contribute to creating appropriate plans for managing the rebalancing process. (original abstract)

Słowa kluczowe

EN

Demand forecasting; Bicycles market; Demand analysis

PL

Prognozowanie popytu; Rynek rowerów; Analiza popytu

• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2022
• Tom: 30
• Numer: nr 3
• Strony: 246--252

Twórcy

autor

Kanokporn Boonjubut

• Shibaura Institute of Technology

autor

Hiroshi Hasegawa

• Shibaura Institute of Technology

Bibliografia

• [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.
• [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.
• [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.
• [7] K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators", Neural Netw., vol. 2, no. 5, pp. 359-366, 1989.
• [8] S. Hochreiter and J. Schmidhuber, "Long short-term memory". Neural computation vol. 9, no. 8, pp. 1735-1780, 1997.
• [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.
• [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.
• [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.
• [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.
• [14] K. Cho et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation", arXiv [cs.CL], 2014.
• [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.
• [16] "Local weather forecast, news and conditions", Wunderground.com. [Online]. Available: http://www.wunderground.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.
• [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.
• [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.
• [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.
• [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.

Identyfikatory

DOI

10.2478/mspe-2022-0031