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Comparison of pedestrian trip generation models

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Using Poisson regression and negative binomial regression, this paper presents an empirical comparison of four different regression models for the estimation of pedestrian demand at the regional level and finds the most appropriate model with reference to the National Household Travel Survey (NHTS) 2001 data for the Baltimore (USA) region. The results show that Poisson regression seems to be more appropriate for pedestrian trip generation modeling in terms of x2 ratio test, Pseudo R2, and Akaike’s information criterion (AIC). However, R2 based on deviance residuals and estimated log-likelihood value at convergence confirmed the empirical studies that negative binomial regression is more appropriate for the over-dispersed dependent variable than Poisson regression.

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  • Kim , Nam Seok & Susilo , Yusak O., 2013. "Comparison of pedestrian trip generation models," Working papers in Transport Economics 2013:22, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    • Handle: RePEc:hhs:ctswps:2013_022
    Note: Published as: Kim, N.S. and Susilo, Y.O. (2013) Comparison of pedestrian trip generation models. Journal of Advanced Transportation, Vol. 47 (4), pp. 399–412. DOI: 10.1002/atr.166
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    File URL: http://www.transportportal.se/swopec/CTS2013-22.pdf
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    1. Cameron, A Colin & Windmeijer, Frank A G, 1996. "R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 209-220, April.
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    Cited by:

    1. Sabouri, Sadegh & Tian, Guang & Ewing, Reid & Park, Keunhyun & Greene, William, 2021. "The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S," Journal of Transport Geography, Elsevier, vol. 93(C).
    2. Jina Mahmoudi & Lei Zhang, 2020. "Impact of the Built Environment Measured at Multiple Levels on Nonmotorized Travel Behavior: An Ecological Approach to a Florida Case Study," Sustainability, MDPI, vol. 12(21), pages 1-39, October.
    3. Seung-Nam Kim & Juwon Chung & Junseung Lee, 2022. "Exploring the Role of Transit Ridership as a Proxy for Regional Centrality in Moderating the Relationship between the 3Ds and Street-Level Pedestrian Volume: Evidence from Seoul, Korea," Land, MDPI, vol. 11(10), pages 1-22, October.
    4. Chengxi Liu & Yusak O. Susilo & Anders Karlström, 2016. "Measuring the impacts of weather variability on home-based trip chaining behaviour: a focus on spatial heterogeneity," Transportation, Springer, vol. 43(5), pages 843-867, September.

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    More about this item

    Keywords

    Pedestrian; Trip generation; Poisson; Negative binomial; Regression;
    All these keywords.

    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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