Abstract
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 the Greater Orlando region, the current study estimates spatial panel models that accommodate for the impact of spatial and temporal observed and unobserved factors on transit ridership. Specifically, two spatial models, Spatial Error Model and Spatial Lag Model, 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, and sociodemographic and socioeconomic variables in the vicinity of the stop along with spatial and spatiotemporal 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.
Highlights
An important tool to evaluate the influence of these public transit investments on transit ridership is the application of statistical models
Model Specification and Overall Measures of Fit. e empirical analysis in our study is based on two different models, (1) Spatial Error Model (SEM) and (2) Spatial Lag Model (SAR), for boarding and alighting ridership. e loglinear independent models were estimated to serve as benchmark for advanced models
The loglikelihood at convergence, R-square value, the number of parameters estimated, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were calculated [28]. e AIC and BIC for a given empirical model are equal to AIC 2K − 2LL, (4)
Summary
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 the Greater Orlando region, the current study estimates spatial panel models that accommodate for the impact of spatial and temporal observed and unobserved factors on transit ridership. Two spatial models, Spatial Error Model and Spatial Lag Model, 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, and sociodemographic and socioeconomic variables in the vicinity of the stop along with spatial and spatiotemporal lagged variables. Ese models provide feedback to agencies on the benefits of public transit investments while providing lessons to improve the investment process. Drawing on stop level public transit boarding and alighting data for 6 four-month periods from May 2013 to April 2015, the current study estimates stop-level ridership models. In terms of exogenous factors, we consider stop-level attributes (such as headway), transportation infrastructure variables (such as secondary highway length including major and minor arterials and major collectors; railroad length; and local road length and sidewalk length), transit infrastructure variables (bus route length, presence of shelter and distance of bus stop from central business district (CBD)), land use and built environment attributes (land use mix, household density, and employment density) and demographic and socioeconomic variables in the vicinity of the bus stop (income, vehicle ownership, and age and gender distribution)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
