01660460

Component

The over-reliance on the automobile in the US over last few decades resulted in traffic congestion and crashes, air pollution associated environmental and health concerns, and dependence on foreign fuel. To counter the problem, policy makers and transportation professionals are promoting public transit and non-motorist travel modes through infrastructure investments. An important tool to evaluate the influence of these public transit investments on transit ridership is the application of statistical models. Drawing on stop level boarding and alighting data for 6 four-month periods for Greater Orlando region from May 2013 to April 2015, the current study estimates a spatial panel model that accommodates for impact of spatial and temporal observed and unobserved factors. Two spatial models: 1) Spatial Error Model (SEM) and 2) Spatial Lag Model (SAR) are estimated for boarding and alighting separately by employing several exogenous variables including stop level attributes, transportation and transit infrastructure variables, built environment and land use attributes, sociodemographic and socioeconomic variables in the vicinity of the stop and spatial and spatio-temporal lagged variables. The model estimation results are further augmented by a validation exercise. These models are expected to provide feedback to agencies on the benefits of public transit investments while also providing lessons to improve the investment process.

This paper was sponsored by TRB committee AP050 Standing Committee on Bus Transit Systems.

18-05724

English

9p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Bus transit; Mathematical models; Ridership; Transit riders

Spatiotemporal analysis

Orlando (Florida)

Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2018 Paper #18-05724

TRIS, TRB, ATRI

Jan 8 2018 11:28AM