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Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model

  • Transportation Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Metro passenger flow forecasting is an essential component of intelligent transportation system. To enhance the forecasting accuracy and explainable of traditional models, a hybrid model combining symbolic regression and Autoregressive Integrated Moving Average Model (ARIMA) was proposed in this paper. It can take unique strength of each single model to capture the complexity patterns beneath data structure. Using the real data from Xi’an metro line 1, the performance of the hybrid model was compared with the ARIMA model and Back Propagation (BP) neural networks. The results show that the hybrid model outperforms other two models. Mean Absolute Percentage Error (MAPE) of hybrid models have an extra 54.24%, 58.98% increase over the BP neural networks and an extra 64.44%, 68.27% increase over the ARIMA models for entrance and exit respectively. In addition, the t-test of MAPE during workday and holiday reflects the hybrid model possesses comparable forecasting ability under different conditions. Moreover, with the increase of the prediction steps, the superiority of the proposed model is more significant.

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References

  • Anvari, S., Tuna, S., Canci, M., and Turkay, M. (2016). “Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems.” Journal of Advanced Transportation, Vol. 50, No. 1, pp. 25–49, DOI: 10.1002/atr.1332.

    Article  Google Scholar 

  • Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time series analysis: Forecasting and control, John Wiley & Sons.

  • Bustillos, B. and Chiu, Y.-C. (2011). “Real-time freeway-experienced travel time prediction using N-curve and k nearest neighbor methods.” Transportation Research Record: Journal of the Transportation Research Board, No. 2243, pp. 127–137, DOI: 10.3141/2243-15.

    Article  Google Scholar 

  • Cai, C., Yao, E., Wang, M., and Zhang, Y. (2014). “Prediction of urban railway station’s entrance and exit passenger flow based on multiply ARIMA model.” Journal of Beijing Jiaotong University, Vol. 38, No. 2, pp. 135–140.

    Google Scholar 

  • Chen, C., Wang, Y., Li, L., Hu, J., and Zhang, Z. (2012). “The retrieval of intra-day trend and its influence on traffic prediction.” Transportation Research Part C: Emerging Technologies, Vol. 22, pp. 103–118, DOI: 10.1016/j.trc.2011.12.006.

    Article  Google Scholar 

  • Chen, K.-Y. and Wang, C.-H. (2007). “A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan.” Expert Systems with Applications, Vol. 32, No. 1, pp. 254–264, DOI: 10.1016/j.eswa.2005.11.027.

    Article  MathSciNet  Google Scholar 

  • Deng, W., Li, W., and Yang, X.-h. (2011). “A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction.” Expert Systems with Applications, Vol. 38, No. 4, pp. 4198–4205, DOI: 10.1016/j.eswa.2010.09.083.

    Article  Google Scholar 

  • Draper, N. R. and Smith, H. (2014). Applied regression analysis, John Wiley & Sons.

  • Elhenawy, M., Chen, H., and Rakha, H. A. (2014). “Dynamic travel time prediction using data clustering and genetic programming.” Transportation Research Part C: Emerging Technologies, Vol. 42, pp. 82–98, DOI: 10.1016/j.trc.2014.02.016.

    Article  Google Scholar 

  • Ghosh, B., Basu, B., and O’Mahony, M. (2009). “Multivariate shortterm traffic flow forecasting using time-series analysis.” IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 2, pp. 246–254, DOI: 10.1109/TITS.2009.2021448.

    Article  Google Scholar 

  • Jajarmizadeh, M., Lafdani, E. K., Harun, S., and Ahmadi, A. (2015). “Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran.” KSCE Journal of Civil Engineering, Vol. 19, No. 1, pp. 345–357, DOI: 10.1007/s12205-014-0060-y.

    Article  Google Scholar 

  • Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection, MIT press.

    MATH  Google Scholar 

  • Kumar, S. V. and Vanajakshi, L. (2015). “Short-term traffic flow prediction using seasonal ARIMA model with limited input data.” European Transport Research Review, Vol. 7, No. 3, pp. 1–9, DOI: 10.1007/s12544-015-0170-8.

    Article  Google Scholar 

  • Li, L., He, S., Zhang, J., and Ran, B. (2017). “Short-term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information.” Journal of Advanced Transportation, in print, DOI: 10.1002/atr.1443.

    Google Scholar 

  • Lim, S. H., Kim, Y., and Lee, C. (2016). “Real-time travel-time prediction method applying multiple traffic observations.” KSCE Journal of Civil Engineering, First online, pp. 1–8, DOI: 10.1007/s12205-016-0239-5.

    Google Scholar 

  • Lu, J., Chen, S., Wang, W., and van Zuylen, H. (2012). “A hybrid model of partial least squares and neural network for traffic incident detection.” Expert Systems with Applications, Vol. 39, No. 5, pp. 4775–4784, DOI: 10.1016/j.eswa.2011.09.158.

    Article  Google Scholar 

  • Meier, A., Gonter, M., and Kruse, R. (2014). “Symbolic regression for precrash accident severity prediction.” Hybrid Artificial Intelligence Systems, Springer, pp. 133–144.

    Google Scholar 

  • METROBITS.ORG “Future Metro Extensions. (2016).” http://micro. com/metro/futureextensions.html Accessed 16.04.13.

  • Nau, R. (2015). “Statistical forecasting: Notes on regression and time series analysis.” Notes and materials for an advanced elective course on statistical forecasting that is taught at the Fuqua School of Business, Duke University.

    Google Scholar 

  • Oh, C., and Park, S. (2011). “Investigating the effects of daily travel time patterns on short-term prediction.” KSCE Journal of Civil Engineering, Vol. 15, No. 7, pp. 1263–1272, DOI: 10.1007/s12205-011-1123-y.

    Article  Google Scholar 

  • Schwarz, G. (1978). “Estimating the dimension of a model.” The Annals of Statistics, Vol. 6, No. 2, pp. 461–464.

    Article  MathSciNet  MATH  Google Scholar 

  • Searson, D. P. (2015). “GPTIPS 2: An open-source software platform for symbolic data mining.” Handbook of Genetic Programming Applications, Springer, pp. 551–573.

    Google Scholar 

  • Sheela, K. G. and Deepa, S. (2013). “Review on methods to fix number of hidden neurons in neural networks.” Mathematical Problems in Engineering, Vol. 2013, DOI: 10.1155/2013/425740.

  • Shukur, O. B. and Lee, M. H. (2015). “Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA.” Renewable Energy, Vol. 76, pp. 637–647, DOI:10.1016/j.renene.2014.11.084.

    Article  Google Scholar 

  • Smith, B. L., Williams, B. M., and Oswald, R. K. (2002). “Comparison of parametric and nonparametric models for traffic flow forecasting.” Transportation Research Part C: Emerging Technologies, Vol. 10, No. 4, pp. 303–321, DOI: 10.1016/S0968-090X(02)00009-8.

    Article  Google Scholar 

  • Smits, G. F. and Kotanchek, M. (2005). “Pareto-front exploitation in symbolic regression.” Genetic Programming Theory and Practice II, Springer, pp. 283–299.

    Chapter  Google Scholar 

  • Solomatine, D., See, L., and Abrahart, R. (2009). “Data-driven modelling: Concepts, approaches and experiences.” Practical Hydroinformatics, Springer, pp. 17–30.

    Google Scholar 

  • Sun, Y., Leng, B., and Guan, W. (2015). “A novel wavelet-SVM shorttime passenger flow prediction in Beijing subway system.” Neurocomputing, Vol. 166, pp. 109–121, DOI: 10.1016/j.neucom.2015.03.085.

    Article  Google Scholar 

  • Vladislavleva, E. J., Smits, G. F., and Den Hertog, D. (2009). “Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming.” Evolutionary Computation, IEEE Transactions on, Vol. 13, No. 2, pp. 333–349, DOI: 10.1109/TEVC.2008.926486.

    Article  Google Scholar 

  • Vladislavleva, E., Friedrich, T., Neumann, F., and Wagner, M. (2013). “Predicting the energy output of wind farms based on weather data: Important variables and their correlation.” Renewable Energy, Vol. 50, pp. 236–243, DOI:10.1016/j.renene.2012.06.036.

    Article  Google Scholar 

  • Washington, S. P., Karlaftis, M. G., and Mannering, F. L. (2010). Statistical and econometric methods for transportation data analysis, CRC press.

    MATH  Google Scholar 

  • Yang, G., Li, X., Wang, J., Lian, L., and Ma, T. (2015). “Modeling oil production based on symbolic regression.” Energy Policy, Vol. 82, pp. 48–61, DOI: 10.1016/j.enpol.2015.02.016.

    Article  Google Scholar 

  • Zhang, G., Patuwo, B. E., and Hu, M. Y. (1998). “Forecasting with artificial neural networks: The state of the art.” International Journal of Forecasting, Vol. 14, No. 1, pp. 35–62, DOI: 10.1016/S0169-2070(97)00044-7.

    Article  Google Scholar 

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing, 210096, China

    Linchao Li, Gang Zhong, Jian Zhang & Bin Ran

  2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing, 210096, China

    Linchao Li, Gang Zhong, Jian Zhang & Bin Ran

  3. Research Center for Internet of Mobility, Southeast University, Nanjing, 210096, China

    Linchao Li, Gang Zhong, Jian Zhang & Bin Ran

  4. School of Highway, Chang’an University, Middle Section of South 2 Ring Rd., Xi’an, 710064, China

    Yonggang Wang

Authors
  1. Linchao Li
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  2. Yonggang Wang
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  3. Gang Zhong
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  4. Jian Zhang
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  5. Bin Ran
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Correspondence to Linchao Li.

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Li, L., Wang, Y., Zhong, G. et al. Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model. KSCE J Civ Eng 22, 1937–1945 (2018). https://doi.org/10.1007/s12205-017-1016-9

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  • DOI: https://doi.org/10.1007/s12205-017-1016-9

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