Abstract
The techniques to forecast available parking space (APS) are indispensable components for parking guidance systems (PGS). According to the data collected in Newcastle upon Tyne, England, the changing characteristics of APS were studied. Thereafter, aiming to build up a multi-step APS forecasting model that provides richer information than a conventional one-step model, the largest Lyapunov exponents (largest LEs) method was introduced into PGS. By experimental tests conducted using the same dataset, its prediction performance was compared with traditional wavelet neural network (WNN) method in both one-step and multi-step processes. Based on the results, a new multi-step forecasting model called WNN-LE method was proposed, where WNN, which enjoys a more accurate performance along with a better learning ability in short-term forecasting, was applied in the early forecast steps while the Lyapunov exponent prediction method in the latter steps precisely reflect the chaotic feature in latter forecast period. The MSE of APS forecasting for one hour time period can be reduced from 83.1 to 27.1 (in a parking building with 492 berths) by using largest LEs method instead of WNN and further reduced to 19.0 by conducted the new method.
Similar content being viewed by others
References
CAICEDO F, BLAZQUEZ C, MIRANDA P. Prediction of parking space availability in real time [J]. Expert Systems with Applications, 2012, 39: 7281–7290.
BURNS M, FAUROT D. An econometric forecasting model of revenues from urban parking facilities [J]. Journal of Economics and Business, 1992, 44(2): 143–150.
DUNNING A. Method and system for projecting dynamic parking availability based on an ongoing survey for remote lots with high demand. United States Patent 7049979 [P]. 2006.
JI Yan-jie, WANG Wei, DENG Wei. Available parking space occupancy change characteristics and short-term forecasting model [J]. Journal of Southeast University (English Edition), 2007, 23(4): 604–608.
LIU Shi-xu, GUAN Hong-zhi, YAN Hai, YIN Huan-huan. Unoccupied parking space prediction of chaotic time series [C]// ICCTP 2010, Integrated Transportation Systems, ASCE: 2010: 2122–2131.
YANG Zhao-sheng, LIU Hong-hong, WANG Xin-yue. The research on the key technologies for improving efficiency of parking guidance system. [C]//Proceedings of IEEE Intelligent Transportation Systems Shanghai, China: IEEE press, 2003: 1177–1182.
ZHU Sun-ying, WANG Hong. XIANG Hong-yan. Dynamic prediction method of traffic flow parameters and traffic events [M]. Nanjing: Southeast University Press, 2008: 85–97. (in Chinese)
HONG Wei-chiang. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm [J]. Neurocomputing, 2011, 74(12/13): 2096–2107.
ABDI J, MOSHIRI B, ABDULHAI B, SEDIGH A K. Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm [J]. Engineering Applications of Artificial Intelligence, 2012, 25(5): 1022–1042.
DOUGHERTY M S, COBBETT M R. Short-term inter-urban traffic forecasts using neural networks [J]. International Journal of Forecasting, 1997, 13(1): 21–31.
SORJAMAA A, HAO Jin, REYHANI N, JI Yong-nan, LENDASSE A. Methodology for long-term prediction of time series [J]. Neurocomputing, 2007, 70(16/18): 2861–2869.
ANDALIB A, ATRY F. Multi-step ahead forecasts for electricity prices using NARX: A new approach, a critical analysis of one-step ahead forecasts [J]. Energy Conversion and Management, 2009, 50: 739–747.
GUO Zhen-hai, ZHAO Wei-gang, LU Hai-yan, WANG Jian-zhou. Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model [J]. Renewable Energy, 2012, 37(1): 241–249.
AMENDOLA A, NIGLIO M, VITALE C. Multi-step SETARMA predictors in the analysis of hydrological time series [J]. Physics and Chemistry of the Earth, Parts A/B/C, 2006, 31(18): 1118–1126.
TAIEB S B, BONTEMPI G, ATIYA A, SPRJAMAA A. A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition [J]. Expert Systems with Applications, 2012, 39(8): 7067–7083.
TAKENS F. Detecting strange attractors in turbulence [M]. D.A. Rand, L.S. Young (Eds.), Lectures Notes in Mathematics, New York: Springer-Verlag, 1981: 366–381.
ABARBANEL H D I. Analysis of observed chaotic data [M]. New York: Springer-Verlag, 1996: 39–58.
KENNEL M, BROWN R, ABARBANEL H D I. Determining embedding dimension for phase-space reconstruction using a geometrical construction [J]. Phys. Rev, 1992, 45: 3403–3411.
ROSENSTEIN M T, COLLINS J J, DELUCA C J. A practical method for the calculating largest Lyapunov exponents from small datasets [J]. Physica D, 1993, 65: 117–134.
PINDORIYA N M, SINGH S N, SINGH S K. Application of adaptive wavelet neural network to forecast operating reserve requirements in forward ancillary services market [J]. Applied Soft Computing, 2011, 11:1811–1819.
BENESTY J, CHEN Jing-dong, HUANG Yi-teng, COHEN I. Pearson correlation coefficient [J]. Noise Reduction in Speech Processing, 2009, 2: 1–4.
WOLF A, SWIFT J B, SWINNEY H L, VASTANO J A. Determining Lyapunov exponents from a time series [J]. Physica D: Nonlinear Phenomena, 1985, 16(3): 285–317.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Project(2012CB725402) supported by the National Key Basic Research Program of China; Projects(51338003, 50908051) supported by the National Natural Science Foundation of China
Rights and permissions
About this article
Cite this article
Ji, Yj., Tang, Dn., Guo, Wh. et al. Forecasting available parking space with largest Lyapunov exponents method. J. Cent. South Univ. 21, 1624–1632 (2014). https://doi.org/10.1007/s11771-014-2104-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11771-014-2104-3