Testing the Forecasting Power of Statistical Models for Intercity Rail Passenger Flows in Turkey

Ekici Ü., TÜYDEŞ YAMAN H., Şendil N.

Transportation Research Record, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2024
  • Doi Number: 10.1177/03611981241242353
  • Journal Name: Transportation Research Record
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, CAB Abstracts, Communication Abstracts, Compendex, ICONDA Bibliographic, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Keywords: ARIMA, demand forecasting, passenger flow, railway transportation, regression, time series analysis
  • Middle East Technical University Affiliated: Yes


While going through a major rail transformation, it is important to develop reliable estimation models for rail passenger flows (RPFs) in Turkey. There are two main approaches in RPF estimation, regressions and autoregressive integrated moving-average (ARIMA) models, both of which were in this study developed using the daily RPF data for the period 2011–2015. The ARIMA models (with some variations) were used to forecast first the daily flows in 2016, during which travel restrictions for summer resulted in reduced volumes, successfully captured in the updated ARIMA model. The regression models predicted the expected demand during the restrictions, enabling evaluation of the impact of restrictions, which also showed the models’ power over the longer term. The forecasts were extended to 2017, 2018, and 2019 data. The regression results produced more reliable forecasts over the long term, whereas more accurate predictions were obtained by ARIMA-Sliding (FA-Sld) for short-term planning purposes.