Forecasting Public Transportation Capacity Utilisation Considering External Factors

Fabian Ohler, Karl-Heinz Krempels, Sandra Möbus

2017

Abstract

Using a forecast of the public transportation capacity utilisation, the buses can be adapted to the demand to avoid overfull buses leading to delays. An efficient utilisation of the buses at disposal can improve customer satisfaction as well as economic efficiency. The basis for our forecasts provide fragmentary measurements of passengers boarding and alighting buses at stops over the year 2015. In an attempt to improve the accuracy of the forecast, several external factors (e. g. weather, holidays, cultural events) were incorporated. We tackle the problem of forecasting public transportation capacity utilisation by forecasting the number of boarding and alighting passengers. Then we use these to adjust previous passenger count and the result as input for next forecast. Using multiple linear regression, support vector regression, and neural networks we evaluate different ways to model the external factors. Best results were achieved by neural networks with a median absolute error of ≈4.16 in the forecast passenger count. They were able to keep more than 80% of the forecasts within a tolerance of 10 passengers. Since the error in the forecasts does not accumulate along the trips, chaining the forecasts in the described way is a viable approach.

References

  1. Adamy, J. (2007). Fuzzy Logik, Neuronale Netze und Evolutionäre Algorithmen. Shaker.
  2. Alfares, H. K. and Nazeeruddin, M. (2002). Electric load forecasting: Literature survey and classification of methods. Int. J. Systems Science, 33(1):23-34.
  3. Arlot, S. and Celisse, A. (2010). A survey of crossvalidation procedures for model selection. Statist. Surv., 4:40-79.
  4. Bastian, J. (1985). Optimale Zeitreihenprognose: empir. Probleme u. Lösungen. PhD thesis, University of Giessen, Gießen, Germany.
  5. Beutel, M. C., Gökay, S., Kluth, W., Krempels, K.-H., Ohler, F., Samsel, C., Terwelp, C., and Wiederhold, M. (2016). Information integration for advanced travel information systems. Journal of Traffic and Transportation Engineering, 4(4).
  6. Dai, W. and Wang, P. (2007). Application of pattern recognition and artificial neural network to load forecasting in electric power system. In Third International Conference on Natural Computation (ICNC 2007), volume 1, pages 381-385.
  7. Eboli, L. and Mazzulla, G. (2007). Service quality attributes affecting customer satisfaction for bus transit. Journal of public transportation, 10(3):2.
  8. Ertel, W. (2013). Grundkurs künstliche Intelligenz: eine praxisorientierte Einführung. Springer-Verlag.
  9. Friedman, M. S., Powell, K. E., Hutwagner, L., Graham, L. M., and Teague, W. G. (2001). Impact of changes in transportation and commuting behaviors during the 1996 summer olympic games in atlanta on air quality and childhood asthma. JAMA, 285(7):897-905.
  10. Fritsch, S. and Guenther, F. (2016). neuralnet: Training of Neural Networks. R package version 1.33.
  11. Guo, Y.-C., Niu, D.-X., and Chen, Y.-X. (2006). Support vector machine model in electricity load forecasting. In 2006 International Conference on Machine Learning and Cybernetics, pages 2892-2896. IEEE.
  12. Hornik, K., Meyer, D., and Karatzoglou, A. (2006). Support vector machines in r. Journal of statistical software, 15(9):1-28.
  13. Jang, J. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Systems, Man, and Cybernetics, 23(3):665-685.
  14. Mansouri, V. et al. (2014). Neural networks in electric load forecasting: A comprehensive survey. Journal of Artificial Intelligence in Electrical Engineering , 3(10):37-50.
  15. Mbamalu, G. and El-Hawary, M. (1993). Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation. IEEE Transactions on Power Systems, 8(1):343-348.
  16. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2015). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.6-7.
  17. Mo, Y. and Su, Y. (2009). Neural networks based real-time transit passenger volume prediction. In Power Electronics and Intelligent Transportation System (PEITS), 2009 2nd International Conference on, volume 2, pages 303-306. IEEE.
  18. Montgomery, D. C., Peck, E. A., and Vining, G. G. (2015). Introduction to linear regression analysis. John Wiley & Sons.
  19. Neusser, K. (2011). Die schätzung vektor-autoregressiver modelle. In Zeitreihenanalyse in den Wirtschaftswissenschaften, pages 191-195. Springer.
  20. Niu, D.-X., Wanq, Q., and Li, J.-C. (2005). Short term load forecasting model using support vector machine based on artificial neural network. In 2005 International Conference on Machine Learning and Cybernetics, volume 7, pages 4260-4265. IEEE.
  21. R Core Team (2016). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
  22. Sachdeva, S. and Verma, C. M. (2008). Load forecasting using fuzzy methods. In Power System Technology and IEEE Power India Conference, 2008. POWERCON 2008. Joint International Conference on, pages 1-4. IEEE.
  23. Salzborn, F. J. M. (1972). Optimum bus scheduling. Transportation Science, 6(2):137-148.
  24. Schölkopf, B., Smola, A. J., Williamson, R. C., and Bartlett, P. L. (2000). New support vector algorithms. Neural computation, 12(5):1207-1245.
  25. Singhal, A., Kamga, C., and Yazici, A. (2014). Impact of weather on urban transit ridership. Transportation Research Part A: Policy and Practice, 69:379 - 391.
  26. Tsai, T., Lee, C., and Wei, C. (2009). Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Syst. Appl., 36(2):3728-3736.
  27. Xue, R., Sun, D. J., and Chen, S. (2015). Short-term bus passenger demand prediction based on time series model and interactive multiple model approach. Discrete Dynamics in Nature and Society, 2015.
  28. Yang, H.-T., Huang, C.-M., and Huang, C.-L. (1995). Identification of armax model for short term load forecasting: an evolutionary programming approach. In Proceedings of Power Industry Computer Applications Conference, pages 325-330.
  29. Zhou, C., Dai, P., and Li, R. (2013). The passenger demand prediction model on bus networks. In Ding, W., Washio, T., Xiong, H., Karypis, G., Thuraisingham, B. M., Cook, D. J., and Wu, X., editors, 13th IEEE International Conference on Data Mining Workshops, ICDM Workshops, TX, USA, December 7-10, 2013, pages 1069-1076. IEEE Computer Society.
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Paper Citation


in Harvard Style

Ohler F., Krempels K. and Möbus S. (2017). Forecasting Public Transportation Capacity Utilisation Considering External Factors . In Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-242-4, pages 300-311. DOI: 10.5220/0006345703000311


in Bibtex Style

@conference{vehits17,
author={Fabian Ohler and Karl-Heinz Krempels and Sandra Möbus},
title={Forecasting Public Transportation Capacity Utilisation Considering External Factors},
booktitle={Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2017},
pages={300-311},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006345703000311},
isbn={978-989-758-242-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Forecasting Public Transportation Capacity Utilisation Considering External Factors
SN - 978-989-758-242-4
AU - Ohler F.
AU - Krempels K.
AU - Möbus S.
PY - 2017
SP - 300
EP - 311
DO - 10.5220/0006345703000311