Estimation of hospital trip characteristics in terms of transportation planning

Kara Ç., Bilgiç Ş.

Journal of Transport and Health, vol.20, 2021 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jth.2020.100987
  • Journal Name: Journal of Transport and Health
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Psycinfo
  • Keywords: Transportation planning, Hospital trip, Trip production, Outliers, Multicollinearity
  • Eskisehir Osmangazi University Affiliated: Yes


© 2021 Elsevier LtdIntroduction: Increasing urban population and traffic density on one side and rising hospital demands on the other has underlined the importance of transportation modeling. In this study, Four-Step Transportation Model (FSTM), which focuses primarily on home-based work and school trips, is used to evaluate the increasing share of home-based hospital trips among all from a transportation planning perspective. The aims of this study are to 1) examine the hospital trip behaviors and the parameters affecting it within the framework of ‘home-based hospital trips’, 2) evaluate the effectiveness of different robust and biased estimation techniques to be used which could be an alternative to Ordinary Least Square (OLS) in FSTM. Method: OLS, Ridge Regression (RR), Least Trimmed Squares (LTS), and Least Trimmed Squares-Ridge (LTS-Ridge) techniques were used for the comprehensive evaluation of home-based hospital trip production models for the Eskişehir City. In this context, the five characteristics affecting hospital trips were used as independent variables. Approximately 20000 valid household survey data (HSD) for 2001 (Training data) and 29000 valid HSD for 2015 (Testing data) were used and results were evaluated in terms of Mean Squared Error (MSE). Results: As a result of the analysis, the MSE values of LTS-Ridge, LTS, RR, and OLS models are 127484, 169060, 274211, 434164, respectively. The most consistent and successful results were obtained from LTS-Ridge according to MSE and direction of the coefficients. Hospital demand coefficient proposed in this study increased the success of future estimations. Conclusion: When data have multicollinearity or contain outliers, LTS-Ridge makes more successful predictions than OLS. This study fills a large gap in the literature by examining the home-based hospital trips in terms of socio-economic and demographic characteristics from a transportation planning perspective.