01763579

Component

https://annualmeeting.myt...nlineProgram/Details/15468

Direct demand transit modeling is challenging due to complex demographic and geographic phenomena. Many direct demand models rely on overly general demographic characteristics such as population density as predictors. Additionally, they often ignore the inherently spatial nature of ridership by using non-spatial methods or by aggregating bus stops counterintuitively into Census geographies. These practices fail to appropriately model the spatial structure of transit data and limit the ability of the model to describe ridership in terms of rider demographics. In this paper, the authors implement a spatial Bayesian model, the BYM2, to model bus ridership at Metro Transit (Minneapolis-Saint Paul, MN). The authors incorporate more descriptive demographic predictors to understand characteristics of transit riders in the region. The model conducts geographic smoothing which improves model fit and more accurately describes spatial bus ridership data. The authors identify demographic predictors which are rarely used in the transit modeling literature. Finally, the authors recommend spatial modeling techniques as a partial solution to known bounding issues for bus stops aggregated to Census geographies.

This paper was sponsored by TRB committee AP090 Standing Committee on Transit Data.

TRBAM-21-04281

English

14p

2021

Transportation Research Board 100th Annual Meeting

Web

Figures; References; Tables

Bus transit; Census; Demographics; Public transit; Ridership; Spatial analysis; Travel demand

Minneapolis (Minnesota); St. Paul (Minnesota)

Passenger Transportation; Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-04281

TRIS, TRB, ATRI

Dec 23 2020 11:06AM