Forecasting ridership impacts of transit oriented development at MARTA rail stations

Abstract

The Metropolitan Atlanta Rapid Transit Authority (MARTA) Transit Oriented Development (TOD) program has been expanding the number of stations being considered for development of surface parking lots and into the air rights over certain rail stations. As of 2015, MARTA has six rail stations in various stages of TOD development, which will increase multi-modal options for metro Atlanta residents. The overarching goal of TOD development is to increase transit ridership and reduce auto-dependency; hence quantifying the potential benefits of TOD development in terms of ridership is paramount. Despite several drawbacks, travel demand models have historically been utilized to forecast ridership for land use changes and transit improvements. Direct ridership models (DRMs) are transit demand forecasting methods that can be applied to land development in cases where traditional travel demand models (TDMs) are not well suited. DRMs leverage geographic tools commonly used by planners to take advantage of small scale pedestrian environment factors immediately surrounding transit stations. Although DRM data and methods can achieve greater precision in predicting local walk-access transit trips, the lack of regional and large-scale datasets reduces the ability to model ridership generated from riders outside the immediate vicinity of the rail stations. Stations that have high multi-modal access trips, particularly via personal vehicle and connecting buses, are not typically accounted for by DRMs. Hence, this study focuses on pedestrian-based rail boardings only, a metric that also allows the use of a large scale onboard survey distributed by the Atlanta Regional Commission (ARC) in late 2009 and early 2010 in Atlanta, Georgia. Analysis of the large scale on-board ridership survey also reveals variables that may be useful in forecasting ridership at the station level when coupled with available census data. Comparison of variables such as income, age, gender, ethnicity, and race from census data with the large scale survey guided the selection of candidate variables to be included in a DRM for MARTA rail stations. Results from the comparison showed that using census data in DRMs does not always accurately reflect the ridership demographics. Notable differences in pedestrian-based ridership and transit catchments appear to occur in populations making less than $40,000, African American populations, and the young and elderly populations. Large differences in the survey and census data reported around the stations raise questions about the usability of census data in predicting ridership at rail stations. Despite the shortcomings of using census data to directly predict walk access transit ridership, an ordinary least squared (OLS) regression model predicts a high proportion of variance of pedestrian-based ridership in Atlanta, Georgia. A small number of variables were incorporated into a DRM to show the strong relationship of employment density with pedestrian based ridership. The number of low income residents was also influential in increasing ridership via walk access.