Numerous vehicles exist worldwide such as cars, motorcycles and bicycles. Although parking for such vehicles is available in many places, parking problems still always exist, such as full lots or a lack of lots. Commuters seeking a parking space expend time when spaces are occupied, and resources are wasted when parking spaces are empty. On the other hand, biking is a green vehicle in a fuel-shortage situation and also a good exercise for people. The Ubike system is a popular short-distance transit vehicle system in Taipei City that also has the parking problem. Therefore, this study uses two common regression schemes – linear regression and support vector regression (SVR) to predict the number of bicycles in Ubike stations to determine the number of available parking spaces. It also uses the proportional selection method to increase accuracy and reduce training time for SVR. Some evaluations are conducted to validate the feasibility of the two regression-based service availability prediction schemes for the Ubike system.

References

1. 1)
  - A.J. Smola , B. Schölkopf . A tutorial on support vector regression. Stat. Comput. , 3 , 199 - 222
2. 2)
  - 3. Weisberg, S.: ‘Applied linear regression’ (Wiley, 2005).
3. 3)
  - 20. Babyak, M.A.: ‘What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models’, Psychosom. Med., 2004, 66, (3), pp. 411–421.
4. 4)
  - 18. http://www.velobleu.org/.
5. 5)
  - 14. Yang, H., Chan, L., King, I.: ‘Support vector machine regression for volatile stock market prediction’. Intelligent Data Engineering and Automated Learning—IDEAL 2002, 2002, pp. 391–396.
6. 6)
  - 6. Gunn, S.R.: ‘Support vector machines for classification and regression’. ISIS Technical Report, 1998, 14.
7. 7)
  - 16. http://www.youbike.com.tw/home.php?eng=1.
8. 8)
  - 12. Sansom, D.C., Downs, T., Saha, T.K.: ‘Evaluation of support vector machine based forecasting tool in electricity price forecasting for Australian national electricity market participants’, J. Electr. Electron. Eng. Australia, 2003, 22, (3), pp. 227–234.
9. 9)
  - 15. http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
10. 10)
  - O. Chapelle , P. Haffner , V.N. Vapnik . Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. , 5 , 1055 - 1064
11. 11)
  - 23. Junshui, M.A., Theiler, J., Perkins, S.: ‘Accurate on-line support vector regression’, Neural Comput., 2003, 15, (11), pp. 2683–2703 (doi: 10.1162/089976603322385117).
12. 12)
  - 21. Van Der Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., van Dongen, B.F., Kindler, E., Günther, C.W.: ‘Process mining: a two-step approach to balance between underfitting and overfitting’, Softw. Syst. Model., 2010, 9, (1), pp. 87–111 (doi: 10.1007/s10270-008-0106-z).
13. 13)
  - 2. Cook, R.D.: ‘Influential observations in linear regression’, J. Am. Stat. Assoc., 1979, 74, (365), pp. 169–174 (doi: 10.1080/01621459.1979.10481634).
14. 14)
  - 19. https://www.bicing.cat/.
15. 15)
  - B.-J. Chen , M.-W. Chang , C.-J. Lin . Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Trans. Power Syst. , 4 , 1821 - 1830
16. 16)
  - 9. Bahlmann, C., Haasdonk, B., Burkhardt, H.: ‘Online handwriting recognition with support vector machines-a kernel approach’. Eighth Int. Workshop on Frontiers in Handwriting Recognition, 2002 Proc., 2002, pp. 49–54.
17. 17)
  - V.N. Vapnik . An overview of statistical learning theory. IEEE Trans. Neural Netw. , 988 - 999
18. 18)
  - 7. Kotsiantis, S.B., Zaharakis, I.D., Pintelas, P.E.: ‘Supervised machine learning: a review of classification techniques, 2007.
19. 19)
  - 8. Joachims, T.: ‘Text categorization with support vector machines: learning with many relevant features’ (Springer, Berlin, Heidelberg, 1998).
20. 20)
  - 35. Everett, H.: ‘Generalized Lagrange multiplier method for solving problems of optimization allocation of resources’, Oper. Res., 1963, 11, (3), pp. 399–417 (doi: 10.1287/opre.11.3.399).
21. 21)
  - 25. http://www.cwb.gov.tw/eng/.
22. 22)
  - 1. Cook, R.D.: ‘Detection of influential observations in linear regression’, Technometrics, 1977, 19, (1), pp. 15–18 (doi: 10.2307/1268249).
23. 23)
  - 4. Vapnik, V.: ‘The nature of statistical learning theory’ (Springer, 2000).
24. 24)
  - 17. http://www.en.velib.paris.fr/.
25. 25)
  - 26. Chang, C.-C., Lin, C.-J.: ‘LIBSVM—A Library for Support Vector Machines, version 3.17, http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
26. 26)
  - 24. Boyd, S.P., Vandenberghe, L.: ‘Convex optimization’ (Cambridge University Press, 2004).

Regression-based parking space availability prediction for the Ubike system

References

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