A Comparative Analysis of Neuro-Fuzzy and ARIMA Models for Urban Rail Passenger Demand Forecasting

The success of strategic and detailed planning of urban rail transportation depends highly on the demand for rate information data. Forecasting is the key to the success of rail passenger operations planning, such as timetabling and seat allocation. Adaptive Neuro-Fuzzy Inference System (ANFIS) is a class of adaptive multi-layer feed forward networks, applied to nonlinear forecasting where past samples are used to forecast the sample ahead. ANFIS incorporates the self-learning ability of neural networks with the linguistic expression function of fuzzy inference. In this paper the authors make an application of ANFIS to model rail passengers flow on the Belgrade urban railway network. The performance of the neuro-fuzzy network is compared with a traditional linear model known as Autoregressive Integrated Moving Average (ARIMA). The models are evaluated for their ability to produce accurate forecasts.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: pp 570-577
  • Monograph Title: Proceedings of First International Conference on Traffic and Transport Engineering (ICTTE)

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Filing Info

  • Accession Number: 01598470
  • Record Type: Publication
  • ISBN: 9788691615307
  • Files: TRIS
  • Created Date: Apr 25 2016 7:23PM