Hospital Trip Production and Attraction Modeling for Future Predictions


Journal of Urban Planning and Development, vol.147, no.4, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 147 Issue: 4
  • Publication Date: 2021
  • Doi Number: 10.1061/(asce)up.1943-5444.0000754
  • Journal Name: Journal of Urban Planning and Development
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, ICONDA Bibliographic, INSPEC, Metadex, Political Science Complete, Pollution Abstracts, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Trip production and attraction, Four-step model, Hospital trip, Outliers, Multicollinearity, REPRODUCTIVE HISTORY, RIDGE REGRESSION, ROBUST RIDGE, HEALTH-CARE, WORK TRIPS, UNMET NEED, GENERATION, MORTALITY, TRAVEL, POPULATION
  • Eskisehir Osmangazi University Affiliated: Yes


© 2021 American Society of Civil Engineers.The demand for healthcare facilities in the world is growing daily, and this growth in demand exceeds general population growth. This situation has made hospital trips a vital issue during urban transportation planning. In this context, the aim of this study is twofold: (1) to investigate the hospital trip behavior as home-based hospital trips within the scope of trip production and attraction (trip generation) models and (2) to evaluate the predictive power of some robust and regularization methods in trip generation modeling. Within this scope, approximately 49,000 valid Household Survey Data (HSD) from 2001 (base year) and 2015 (target year) were used to develop comprehensive trip generation models for the home-based hospital trips. Multiple linear regression (MLR), ridge regression (RR), Liu, least trimmed squares (LTS), least trimmed squares-ridge (LTS-R), and least trimmed square-Liu (LTS-L) methods were used for the analysis, and results were evaluated in terms of specialized mean squared error (MSE) and root mean square error (RMSE). As a result, LTS-R and LTS-L increased future predictive power by approximately 55% compared with OLS. This study fills an important gap in the literature in terms of not only the use of regularization and robust methods in trip demand modeling but also examining characteristics that affect hospital trip behaviors. In addition, the paper proposes a "Hospital Mobility Coefficient"projection that could be useful in demand estimation for future hospital trips.