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Paratransit demand of disabled people

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  • Bearse, Peter
  • Gurmu, Shiferaw
  • Rapaport, Carol
  • Stern, Steven

Abstract

This paper estimates the demand for transportation systems that are used primarily by disabled individuals. These systems are known as paratransit systems and have experienced large increases in number and average size over the past 15 years. We first use a national database and standard time series techniques to model aggregate demand. We then use a unique data set of administrative records from a paratransit system in central Virginia to estimate standard and nonstandard count models of individual demand. We conclude that most of the demand growth is from new passengers, but that predicting the growth of new passengers is very difficult. Our results also highlight the importance of incorporating autocorrelation and possible sample attrition into standard count models.

Suggested Citation

  • Bearse, Peter & Gurmu, Shiferaw & Rapaport, Carol & Stern, Steven, 2004. "Paratransit demand of disabled people," Transportation Research Part B: Methodological, Elsevier, vol. 38(9), pages 809-831, November.
    • Handle: RePEc:eee:transb:v:38:y:2004:i:9:p:809-831
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    1. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    2. Phillips, Peter C B & Ploberger, Werner, 1996. "An Asymptotic Theory of Bayesian Inference for Time Series," Econometrica, Econometric Society, vol. 64(2), pages 381-412, March.
    3. Fitzgerald, James & Shaunesey, Donna & Stern, Steven, 2000. "The effect of education programs on paratransit demand of people with disabilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(4), pages 261-285, May.
    4. Geweke, John, 1988. "Antithetic acceleration of Monte Carlo integration in Bayesian inference," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 73-89.
    5. Steven Stern, 1997. "Simulation-Based Estimation," Journal of Economic Literature, American Economic Association, vol. 35(4), pages 2006-2039, December.
    6. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    7. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    8. Stern, Steven, 1993. "A disaggregate discrete choice model of transportation demand by elderly and disabled people in rural Virginia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(4), pages 315-327, July.
    9. Phillips, Peter C B, 1996. "Econometric Model Determination," Econometrica, Econometric Society, vol. 64(4), pages 763-812, July.
    10. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
    11. Vassilis A. Hajivassiliou & Daniel McFadden & Paul A. Ruud, 1994. "Simulation of Multivariate Normal Rectangle Probabilities: Theoretical and Computational Results," Cowles Foundation Discussion Papers 1021R, Cowles Foundation for Research in Economics, Yale University.
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    Cited by:

    1. Kwon, Kihyun & Akar, Gulsah, 2022. "People with disabilities and use of public transit: The role of neighborhood walkability," Journal of Transport Geography, Elsevier, vol. 100(C).
    2. Nguyen-Hoang, Phuong & Yeung, Ryan, 2010. "What is paratransit worth?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 841-853, December.
    3. Konstantinos Zografos & Konstantinos Androutsopoulos & Teemu Sihvola, 2008. "A methodological approach for developing and assessing business models for flexible transport systems," Transportation, Springer, vol. 35(6), pages 777-795, November.
    4. Deka, Devajyoti & Gonzales, Eric J., 2014. "The generators of paratransit trips by persons with disabilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 181-193.
    5. Mercado, Ruben & Páez, Antonio & Newbold, K. Bruce, 2010. "Transport policy and the provision of mobility options in an aging society: a case study of Ontario, Canada," Journal of Transport Geography, Elsevier, vol. 18(5), pages 649-661.
    6. Chao Wang & Mohammed Quddus & Marcus Enoch & Tim Ryley & Lisa Davison, 2014. "Multilevel modelling of Demand Responsive Transport (DRT) trips in Greater Manchester based on area-wide socio-economic data," Transportation, Springer, vol. 41(3), pages 589-610, May.
    7. Alexandra Q. Pan & Susan Shaheen, 2025. "What Makes the Route More Traveled? Optimizing U.S. Suburban Microtransit for Sustainable Mobility," Sustainability, MDPI, vol. 17(3), pages 1-19, January.
    8. Fitzgerald, James & Shaunesey, Donna & Stern, Steven, 2000. "The effect of education programs on paratransit demand of people with disabilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(4), pages 261-285, May.

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