Abstract
Nowadays building a green and efficient public transportation system for the expanding urban population is undoubtedly a big challenge. In recent years, public bicycle system has been widely appreciated and researched worldwide. Unlike traditional public transportation system, public bicycle system doesn’t need to follow fixed schedule. This flexibility brings high efficiency as well as uncertainty-we don’t know whether there are available bikes or bike stands when they are indeed needed. This paper aims to predict the number of available bikes at given future time point so as to optimize the user’s travel choices. In this article, we propose a new prediction model and use generalized regression neural network as our prediction algorithm to optimize prediction accuracy. Experimental results show that in this way we can properly handle this nonlinear problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
DeMaio, P.: Bike-sharing: History, impacts, models of provision, and future. J. Public Transp. 12(4), 41–56 (2009)
Shaheen, S.A., Guzman, S., Zhang, H.: Bikesharing in europe, the americas, and asia. Transp. Res. Rec. J. Transp. Res. Board 2143(1), 159–167 (2010)
Chen, B., et al.: Uncertainty in urban mobility: Predicting waiting times for shared bicycles and parking lots. In: Proceedings of ITSC, vol. 13 (2013)
Froehlich, J., Neumann, J., Oliver, N.: Sensing and predicting the pulse of the city through shared bicycling. In: IJCAI (2009)
Kaltenbrunner, A., et al.: Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive Mob. Comput. 6(4), 455–466 (2010)
Yoon, J.W., Pinelli, F., Calabrese, F.: Cityride: a predictive bike sharing journey advisor. In: 2012 IEEE 13th International Conference on Mobile Data Management (MDM), IEEE (2012)
Hastie, T.J., Tibshirani, R.J.: Generalized additive models. CRC Press, Boca Raton, Florida (1990)
Lin, J.-R., Yang, T.-H.: Strategic design of public bicycle sharing systems with service level constraints. Transp. Res. Part E: Logistics Transp. Rev. 47(2), 284–294 (2011)
Specht, D.F.: A general regression neural network. Neural Netw. IEEE Trans. 2(6), 568–576 (1991)
Girardin, F., et al.: Digital footprinting: Uncovering tourists with user-generated content. Pervasive Comput. IEEE 7(4), 36–43 (2008)
Chen, S., Colin, F.N.C., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. Trans. Neural Netw. IEEE 2(2), 302–309 (1991)
Krykewycz, G.R., et al.: Defining a primary market and estimating demand for major bicycle-sharing program in Philadelphia, Pennsylvania. Transp. Res. Rec. J. Transp. Res. Board 2143(1), 117–124 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yang, M., Guang, Y., Zhang, X. (2015). Public Bicycle Prediction Based on Generalized Regression Neural Network. In: Hsu, CH., Xia, F., Liu, X., Wang, S. (eds) Internet of Vehicles - Safe and Intelligent Mobility. IOV 2015. Lecture Notes in Computer Science(), vol 9502. Springer, Cham. https://doi.org/10.1007/978-3-319-27293-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-27293-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27292-4
Online ISBN: 978-3-319-27293-1
eBook Packages: Computer ScienceComputer Science (R0)