Transit Ridership Modeling at the Bus Stop Level: Comparison of Approaches Focusing on Count and Spatially Dependent Data

Abstract

Boarding and alighting modeling at the bus stop level is an important tool for operational planning of public transport systems, in addition to contributing to transit-oriented development. The interest variables, in this case, present two particularities that strongly influence the performance of proposed estimates: they demonstrate spatial dependence and are count data. Moreover, in most cases, these data are not easy to collect. Thus, the present study proposes a comparison of approaches for transit ridership modeling at the bus stop level, applying linear, Poisson, Geographically Weighted and Geographically Weighted Poisson (GWPR) regressions, as well as Universal Kriging (UK), to the boarding and alighting data along a bus line in the city of São Paulo, Brazil. The results from goodness-of-fit measures confirmed the assumption that adding asymmetry and spatial autocorrelation, isolated and together, to the transportation demand modeling, contributes to a gradual improvement in the estimates, highlighting the GWPR and UK spatial estimation techniques. Moreover, the spatially varying relationships between the variables of interest (boardings and alightings) and their predictors (land use and transport system features around the bus stops), shown in the present study, may support land use policies toward transit-oriented development. In addition, by using an approach with little information, the good results achieved proved that satisfactory boarding and alighting modeling can be done in regions where there is a lack of travel demand data, as in the case of emerging countries.

Spatio-Temporal Investigation of Public Transport Demand Using Smart Card Data

“…In recent years, GWR has been extensively applied within the field of station-revel ridership modeling, as each station can be viewed as a geographic unit that can be covered by a local model. These local models estimate the number of passengers boarding at each station as a function of the station characteristics and the surrounding area that they serve (Cardozo et al, 2012) or the built environment (Gao et al, 2022), but also as a function of the land use near the stations and the characteristics of the transit system surrounding the stations (Marques & Pitombo, 2022). These station-level models have been applied both on a national scale (Blainey, 2010) and on an urban scale (Cardozo et al, 2012;Blainey & Mulley, 2013;He et al, 2019;Marques & Pitombo, 2022).…”

“…These local models estimate the number of passengers boarding at each station as a function of the station characteristics and the surrounding area that they serve (Cardozo et al, 2012) or the built environment (Gao et al, 2022), but also as a function of the land use near the stations and the characteristics of the transit system surrounding the stations (Marques & Pitombo, 2022). These station-level models have been applied both on a national scale (Blainey, 2010) and on an urban scale (Cardozo et al, 2012;Blainey & Mulley, 2013;He et al, 2019;Marques & Pitombo, 2022). In addition to further developments in the fields of application, the methodology itself has also evolved.…”