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Research Article

A rule-based model for Seoul Bike sharing demand prediction using weather data

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Pages 166-183 | Received 11 Dec 2019, Accepted 01 Feb 2020, Published online: 13 Feb 2020

References

  • Altman, N.S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175–185.
  • Barnes, G., & Krizek, K. (2005). Estimating bicycling demand. Transportation Research Record, 1939(1), 45–51. doi:10.1177/0361198105193900106
  • Borgnat, P., Abry, P., Flandrin, P., & Rouquier, J.-B. (2009). Studying Lyon’s Vélo’V: A statistical cyclic model. European Conference on Complex Systems, University of Warwick in UK (vol. 2009).
  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. doi:10.1023/A:1010933404324
  • Breiman, L. (2017). Classification and regression trees. Routledge, Taylor and Francis, FL.
  • Chen, T. (2003). A fuzzy back propagation network for output time prediction in a wafer fab. Applied Soft Computing, 2(3), 211–222. doi:10.1016/S1568-4946(02)00066-2
  • Chen, T. (2007). An intelligent hybrid system for wafer lot output time prediction. Advanced Engineering Informatics, 21(1), 55–65. doi:10.1016/j.aei.2006.10.002
  • Chen, X., & Ishwaran, H. (2012). Random forests for genomic data analysis. Genomics, 99(6), 323–329. doi:10.1016/j.ygeno.2012.04.003
  • Corcoran, J., Li, T., Rohde, D., Charles-Edwards, E., & Mateo-Babiano, D. (2014). Spatio-temporal patterns of a public bicycle sharing program: The effect of weather and calendar events. Journal of Transport Geography, 41, 292–305. doi:10.1016/j.jtrangeo.2014.09.003
  • DeMaio, P. (2009). Bike-sharing: History, impacts, models of provision, and future. Journal of Public Transportation, 12(4), 3.
  • Deng, H., & Runger, G. (2013). Gene selection with guided regularized random forest. Pattern Recognition, 46(12), 3483–3489. doi:10.1016/j.patcog.2013.05.018
  • El-Assi, W., Mahmoud, M.S., & Habib, K.N. (2017). Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation, 44(3), 589–613. doi:10.1007/s11116-015-9669-z
  • Erdoğan, G., Battarra, M., & Calvo, R.W. (2015). An exact algorithm for the static rebalancing problem arising in bicycle sharing systems. European Journal of Operational Research, 245(3), 667–679. doi:10.1016/j.ejor.2015.03.043
  • Feng, C., Hillston, J., & Reijsbergen, D. (2017). Moment-based availability prediction for bike-sharing systems. Performance Evaluation, 117, 58–74. doi:10.1016/j.peva.2017.09.004
  • Ferlito, S., Adinolfi, G., & Graditi, G. (2017). Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production. Applied Energy, 205, 116–129.
  • Fishman, E. (2016). Bikeshare: A review of recent literature. Transport Reviews, 36(1), 92–113. doi:10.1080/01441647.2015.1033036
  • Gao, X., & Lee, G.M. (2019). Moment-based rental prediction for bicycle-sharing transportation systems using a hybrid genetic algorithm and machine learning. Computers & Industrial Engineering, 128, 60–69.
  • García-Palomares, J.C., Gutiérrez, J., & Latorre, M. (2012). Optimizing the location of stations in bike-sharing programs: A GIS approach. Applied Geography, 35(1–2), 235–246. doi:10.1016/j.apgeog.2012.07.002
  • Gast, N., Massonnet, G., Reijsbergen, D., & Tribastone, M. (2015). Probabilistic forecasts of bike-sharing systems for journey planning. Proceedings of the 24th ACM international on conference on information and knowledge management held at Melbourne, Australia (pp. 703–712). ACM.
  • Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15(3), 651–674. doi:10.1198/106186006X133933
  • Kadri, A.A., Kacem, I., & Labadi, K. (2016). A branch-and-bound algorithm for solving the static rebalancing problem in bicycle-sharing systems. Computers & Industrial Engineering, 95, 41–52. doi:10.1016/j.cie.2016.02.002
  • KAGGLE BIKE SHARING DEMAND (2014). Retrieved from https://www.kaggle.com/c/bike-sharing-demand/overview
  • Kaltenbrunner, A., Meza, R., Grivolla, J., Codina, J., & Banchs, R. (2010). Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 6(4), 455–466. doi:10.1016/j.pmcj.2010.07.002
  • Kim, K. (2018). Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations. Journal of Transport Geography, 66, 309–320. doi:10.1016/j.jtrangeo.2018.01.001
  • Kohavi, R. (1995, August 20–25). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, QC (pp. 1137–1145).
  • Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York, NY: Springer.
  • Kuhn, M., Weston, S., Keefer, C., Coulter, N., & Quinlan, R. (2014). Cubist: Rule-and instance-based regression modeling, R package version 0.0.18. Vienna, Austria: CRAN.
  • Meng, L.D. (2011). Implementing bike-sharing systems. Proceedings of the Institution of Civil Engineers, 164(2), 89.
  • Noi, P., Degener, J., & Kappas, M. (2017). Comparison of multiple linear regression, cubist regression, and randomforest algorithms to estimate daily air surface temperature from dynamic combinations of MODIS LSTdata. Remote Sensing, 9, 398. doi:10.3390/rs9050398
  • Quinlan, R. (1992, November 16–18). Learning with continuous classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Australia (pp. 343–348).
  • Quinlan, R. (1993, June 27–29). Combining instance based and model based learning. Proceedings of the Tenth International Conference on Machine Learning, Amherst, MA (pp. 236–243).
  • R Core Team. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0. Retrieved from https://www.R-project.org/
  • Raviv, T., & Kolka, O. (2013). Optimal inventory management of a bike-sharing station. Iie Transactions, 45(10), 1077–1093. doi:10.1080/0740817X.2013.770186
  • Revolution Analytics, S. (2015). Weston, doParallel: Foreach Parallel Adaptor for the ‘parallel’ Package. Retrieved from https://mran.revolutionanalytics.com/snapshot/2016-01-01/web/packages/doParallel/doParallel.pdf
  • Rodriguez, J.D., Perez, A., & Lozano, J.A. (2010). Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 569–575. doi:10.1109/TPAMI.2009.187
  • Russell, S.J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Malaysia: Pearson Education Limited.
  • Schuijbroek, J., Hampshire, R.C., & Van Hoeve, W.-J. (2017). Inventory rebalancing and vehicle routing in bike sharing systems. European Journal of Operational Research, 257(3), 992–1004.
  • SEOUL OPEN DATA PLAZA. (2017-2018) Retrieved from http://data.seoul.go.kr/
  • Shaheen, S.A., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, the Americas, and Asia: Past, present, and future. Transportation Research Record, 2143(1), 159–167. doi:10.3141/2143-20
  • Shaheen, S.A., Martin, E.W., Cohen, A.P., Chan, N.D., & Pogodzinski, M. (2014). Public Bikesharing in North America during a period of rapid expansion: Understanding business models. Industry Trends & User Impacts, MTI Report, San Jose State University, 12–29.
  • Strobl, C., Boulesteix, A.-L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8(1), 25. doi:10.1186/1471-2105-8-25
  • Tirkel, I. (2011). Cycle time prediction in wafer fabrication line by applying data mining methods. 2011 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, Saratoga Springs, NY (pp. 1–5). IEEE.
  • Vogel, P., & Mattfeld, D.C. (2011). Strategic and operational planning of bike-sharing systems by data mining–A case study. International Conference on Computational Logistics (pp. 127–141). Springer: Berlin, Heidelberg.
  • Wang, Y., & Witten, I. (1996, April 23–25). Inducing model trees for continuous classes. Proceedings of the Ninth European Conference on Machine Learning, Prague, Czech Republic (pp. 128–137).
  • Wolpert, D.H., & Macready, W.G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. doi:10.1109/4235.585893
  • Wong, T.T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit, 48, 2839–2846. doi:10.1016/j.patcog.2015.03.009
  • Zhou, J., Li, E., Wang, M., Chen, X., Shi, X., & Jiang, L. (2019). Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT Case Histories. Journal of Performance of Constructed Facilities, 33, 04019024.