How to Increase Rail Ridership in Maryland: Direct Ridership Models for Policy Guidance

Abstract

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 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 distance to 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.

Improved Huff Model for Estimating Urban Rail Transit Station Catchment Areas considering Station Choices

“…For example, 800 m has been broadly accepted as a reasonable walking distance to a rail transit station [14][15][16]. What is more, employment in the 800 m station area, service level, bus connectivity, station location in the Central Business District (CBD), distance to the nearest station, and terminal are all important factors affecting passenger flow [17]. However, some scholars considered that this distance varies spatially, with people living in suburbs likely to accept larger distances [18] and longer travel times [19] than people living in the CBD.…”

An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership

“…This paper summarizes the related studies on direct demand models for ridership estimation (see Figure A1 in A.1). As summarized in Figure A1, the most widely used method is Ordinary Least applied OLS regression to model transit ridership and its influencing factors [3,7,[10][11][12][13][14][15][16][17][18][19]. However, the limitation of OLS models is that they assume the transit ridership is affected by various factors but has nothing to do with the spatial location, without considering the spatial autocorrelation, in other words, OLS models estimate ridership from a global perspective believing that calculated coefficients do not have significant differences in space.…”

Geographically Modeling and Understanding Factors Influencing Transit Ridership: An Empirical Study of Shenzhen Metro

“…The main advantages of the direct demand model in ridership modeling are simple usage, easy interpretation, quick response, and low expenses. Ordinary least squares (OLS) multivariate regression is a commonly used direct demand model, and assumes that the parameters are stable [1,5,[7][8][9][10][11][12][13][14][15][16]. The advantages of spatial models are considered in direct demand models, and thus provide a better spatial interpretation by implementing geographically weighted regression (GWR), which can measure the nonstationarity and heterogeneity of spatial parameters.…”

“…A review of the literature related to transit ridership modeling[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][20][21][22][23][24][25][26][27][28][29]. Cont.…”