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access icon free Predicting vacant parking space availability: an SVR method with fruit fly optimisation

© The Institution of Engineering and Technology
Received 05/03/2018, Accepted 09/08/2018, Revised 26/06/2018, Published 07/09/2018

In this study, a novel prediction model for the number of vacant parking spaces after a specific period of time is proposed based on support vector regression (SVR) with fruit fly optimisation algorithm (FOA). In the proposed model, the SVR parameters are initialised as the fruit fly population, and FOA is utilised to search the optimal parameters for SVR. Sufficient experiments within various scenarios, i.e. predicting the vacant parking space availability in parking lots with various capacities after various periods of time, have been conducted to verify the effectiveness of the proposed FOA-SVR prediction model. Three other commonly used prediction models, i.e. backpropagation neural network (NN), extreme learning machine and wavelet NN, are used as the comparison models. The experimental results show that the proposed FOA-SVR method has higher accuracy and stability in all the prediction scenarios.

Inspec keywords: neural nets; optimisation; backpropagation; learning (artificial intelligence); support vector machines; regression analysis

Other keywords: vacant parking spaces; fruit fly population; SVR parameters; optimal parameters; FOA-SVR method; prediction scenarios; commonly used prediction models; vacant parking space availability; extreme learning machine; fruit fly optimisation; parking lots; novel prediction model; FOA-SVR prediction model; support vector regression

Subjects: Knowledge engineering techniques; Optimisation techniques; Other topics in statistics; Optimisation techniques

References

    1. 1)
      • 17. Huang, G.B., Zhu, Q.Y., Siew, C.K.: ‘Extreme learning machine: a new learning scheme of feedforward neural networks’. 2004 IEEE Int. Joint Conf. Neural Networks, Budapest, Hungary, 2004, vol. 2, pp. 985–990.
    2. 2)
      • 19. Jiang, X., Adeli, H.: ‘Dynamic wavelet neural network model for traffic flow forecasting’, J. Transp. Eng., 2005, 131, (10), pp. 771–779.
    3. 3)
      • 9. Zheng, Y., Rajasegarar, S., Leckie, C.: ‘Parking availability prediction for sensor-enabled car parks in smart cities’. 2015 IEEE 10th Int. Conf. Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, Singapore, 2015, pp. 1–6.
    4. 4)
      • 5. Leu, J.S., Zhu, Z.Y.: ‘Regression-based parking space availability prediction for the Ubike system’, IET Intell. Transp. Syst., 2015, 9, (3), pp. 323–332.
    5. 5)
      • 16. Su, H., Yu, S.: ‘Hybrid GA based online support vector machine model for short-term traffic flow forecasting’. Int. Workshop on Advanced Parallel Processing Technologies, Guangzhou, China, 2007, pp. 743–752.
    6. 6)
      • 3. Ji, Y.J., Tang, D., Blythe, P., et al: ‘Short-term forecasting of available parking space using wavelet neural network model’, IET Intell. Transp. Syst., 2014, 9, (2), pp. 202–209.
    7. 7)
      • 10. Drucker, H., Burges, C.J., Kaufman, L., et al: ‘Support vector regression machines’. Advances in Neural Information Processing Systems, Denver, CO, USA, 1996, pp. 155–161.
    8. 8)
      • 12. Wu, L., Zuo, C., Zhang, H.: ‘A cloud model based fruit fly optimization algorithm’, Knowl.-Based Syst., 2015, 89, pp. 603–617.
    9. 9)
      • 11. Pan, W.T.: ‘A new fruit fly optimization algorithm: taking the financial distress model as an example’, Knowl.-Based Syst., 2012, 26, pp. 69–74.
    10. 10)
      • 14. Shen, L., Chen, H., Yu, Z., et al: ‘Evolving support vector machines using fruit fly optimization for medical data classification’, Knowl.-Based Syst., 2016, 96, pp. 61–75.
    11. 11)
      • 20. Wang, J., Shi, Q.: ‘Short-term traffic speed forecasting hybrid model based on chaos–wavelet analysis-support vector machine theory’, Transp. Res. C, Emerg. Technol., 2013, 27, pp. 219–232.
    12. 12)
      • 7. Peng, L., Li, H.: ‘Searching parking spaces in urban environments based on non-stationary Poisson process analysis’. 2016 IEEE 19th Int. Conf. Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 2016, pp. 1951–1956.
    13. 13)
      • 2. Ji, Y.J., Tang, D.N., Guo, W.H., et al: ‘Forecasting available parking space with largest Lyapunov exponents method’, J. Central South Univ., 2014, 21, (4), pp. 1624–1632.
    14. 14)
      • 15. Ma, J., Theiler, J., Perkins, S.: ‘Accurate on-line support vector regression’, Neural Comput., 2003, 15, (11), pp. 2683–2703.
    15. 15)
      • 18. Huang, G.B., Siew, C.K.: ‘Extreme learning machine with randomly assigned RBF kernels’, Int. J. Inf. Technol., 2005, 11, (1), pp. 16–24.
    16. 16)
      • 8. Yu, F., Guo, J., Zhu, X., et al: ‘Real time prediction of unoccupied parking space using time series model’. 2015 IEEE Int. Conf. Transportation Information and Safety (ICTIS), Wuhan, China, 2015, pp. 370–374.
    17. 17)
      • 6. Rajabioun, T., Ioannou, P.A.: ‘On-street and off-street parking availability prediction using multivariate spatiotemporal models’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (5), pp. 2913–2924.
    18. 18)
      • 13. Pan, Q.K., Sang, H.Y., Duan, J.H., et al: ‘An improved fruit fly optimization algorithm for continuous function optimization problems’, Knowl.-Based Syst., 2014, 62, pp. 69–83.
    19. 19)
      • 4. Ji, Y.J., Gao, L.P., Chen, X.S., et al: ‘Strategies for multi-step-ahead available parking spaces forecasting based on wavelet transform’, J. Central South Univ., 2017, 24, (6), pp. 1503–1512.
    20. 20)
      • 1. Caliskan, M., Barthels, A., Scheuermann, B., et al: ‘Predicting parking lot occupancy in vehicular ad hoc networks’. 2007 IEEE 65th Vehicular Technology Conf. (VTC), Dublin, Ireland, 2007, pp. 277–281.
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