Ting Ma

Retail Location and Transit: An Econometric Examination of Retail Location in Prince George’s and Montgomery County, Maryland

Authors: Ting Ma, Eli Knaap, and Gerrit-Jan Knaap
Report
Synopsis: Transit oriented development (TOD) is a widely accepted policy objective of many jurisdictions in the United States. There is both anecdotal and empirical evidence to suggest that the vitality of TODs and the transit boardings from any TOD depends significantly on the extent of retail development in the transit station area. We focus in this paper, on the determinants of retail location in two counties, MontgomeryCounty and Prince George’s County, Maryland, with a particular focus on the influence of proximity torail transit stations. We used data from two counties in the Washington DC suburbs to constructmeasures of transit and retail accessibility and constructed an econometric model to estimate the relationship between urban contextual factors and retail firm locations. The results from our analysis provide empirical support for the notion that retail firms are attracted to locations with high levels of transit accessibility. By extension, these findings suggest that investments in transit—particularly fixed rail transit—may be an effective method for stimulating retail development in metropolitan areas.

 

How to Increase Rail Ridership in Maryland? Direct Ridership Models (DRM) for Policy Guidance

Authors: Liu, Chao, Sevgi Erdogan, Ting Ma, and Frederick W. Ducca
Report
Synopsis: The state of Maryland aims to double its transit ridership by the end of 2020. The Maryland Statewide Transportation Model (MSTM) has been used to analyze different policy options at a system-wide level. Direct ridership models (DRM) estimate ridership as a function of station environment and transit service features rather than using mode‐choice results from large‐scale traditional models. They have been particularly favored for estimating the benefits of smart growth policies such as Transit Oriented Development (TOD) on transit ridership and can can be used as complementary to the traditional four-step models for analyzing smart growth scenarios at a local level and can provide valuable information that a system level analysis cannot provide. In this study, we developed DRMs of rail transit stations, namely light rail, commuter rail, Baltimore metro, and Washington D.C. metro for the state of Maryland. Data for 117 rail stations were gathered from a variety of sources and categorized by transit service characteristics, station built environment features and social-demographic variables. The results suggest that impacts of built environment show differences for light rail and commuter rail. For light rail stations, employment at half-mile buffer areas, service level, feeder bus connectivity, station location in the CBD, distance to the nearest station, and terminal stations are significant factors affecting ridership. For commuter rail stations only feeder bus connection is found to be significant. The policy implications of the results are discussed.