Transit Ridership Model Based on Geographically Weighted Regression

Abstract

This paper describes the development of a geographically weighted regression (GWR) model to explore the spatial variability in the strength of the relationship between public transit use for home-based work (HBW) trip purposes and an array of potential transit use predictors. The transit use predictors considered include demographics and socioeconomics, land use, transit supply and quality, and pedestrian environment. The best predictors identified through model estimation include two global variables (regional accessibility of employment and percentage of households with no car) and three local variables (employment density, average number of cars in households with children, and percentage of the population who are black). The models were estimated on the basis of the 2000 Census Transportation Planning Package data for Broward County, Florida. Model testing indicates the GWR model has improved accuracy in predicting transit use for HBW purposes over linear regression models. The GWR model also indicates that the effects of the independent variables on transit use vary across space. The research points to future research to explore different model structures within a geographic area.

Get full access to this article

View all access options for this article.

References

Kohn

H. M.

Factors Affecting Urban Transit Ridership. Proc., 35th Annual Conference of the Canadian Transportation Research Forum, Charlottetown, Prince Edward Island, Canada, 2000, pp. 271–285.

Chatterjee

Seaver

W. L.

Kiattikomol

and Wegmann

F. J.

. Models for Forecasting Urban Transit Ridership for Statewide Analysis. Presented at 81st Annual Meeting of the Transportation Research Board, Washington, D.C., 2002.

Yoh

A. C.

Haas

P. J.

and Taylor

B. D.

. Understanding Transit Ridership Growth: Case Studies of Successful Transit Systems in the 1990s. In Transportation Research Record: Journal of the Transportation Research Board, No. 1835, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 111–120.

Taylor

B. D.

Miller

Iseki

and Fink

. Analyzing the Determinants of Transit Ridership Using Two-Stage Least-Squares Regression on National Sample of Urbanized Areas. Presented at 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2004.

Parker

McKeever

Arrington

G. B.

and Smith-Heimer

. Statewide Transit-Oriented Development Study—Factors for Success in California. Final Report. Prepared for Business, Transportation and Housing Agency and California Department of Transportation, Sacramento, Calif., 2002.

Lawton

Metro 1994 Travel Behavior Survey, Portland Metro, Portland, Ore., 1997.

Parsons Brinckerhoff. TCRP Report 16: Transit and Urban Form, Vol. 1, Part 1. TRB, National Research Council, Washington, D.C., 1996.

Parsons Brinckerhoff. TCRP Report 16: Commuter and Light Rail Transit Corridors: The Land Use Connection, Vol. 1, Part 2. TRB, National Research Council, Washington, D.C., 1996.

Pushkarev

B. S.

and Zupan

J. M.

. Where Transit Works: Urban Densities for Public Transportation. In Urban Transportation: Perspectives and Prospects ( Levinson

H. S.

and Weant

R. A.

, eds.), Eno Foundation, Westport, Conn., 1982.

10.

Kikuchi

and Miljkovic

. Use of Fuzzy Inference for Modeling Prediction of Transit Ridership at Individual Stops. In Transportation Research Record: Journal of the Transportation Research Board, No. 1774, TRB, National Research Council, Washington, D.C., 2001, pp. 25–35.

11.

Enhancement and Refinement of the Integrated Transit and Demand and Supply Model, T-BEST Arc Version 1.1, Users Guide. Public Transit Office, Florida Department of Transportation, Tallahassee, 2004.

12.

Enhancement and Refinement of the Integrated Transit and Demand and Supply Model, T-BEST Arc Version 2.1, Users Guide. Public Transit Office, Florida Department of Transportation, Tallahassee, 2005.

13.

Fotheringham

A. S.

Brunsdon

and Charlton

. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, John Wiley & Sons Ltd., West Sussex, United Kingdom, 2002.

14.

Zhao

Chow

L.-F.

and Li

M.-T.

. A Transit Ridership Model Based on Geographically Weighted Regression and Service Quality Variables. Final Report. Prepared for the Florida Department of Transportation, Tallahassee, April 2005.