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Maryland Rail Time-Of-Day Direct Ridership Model Final Report

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Maryland Rail Time-Of-Day Direct Ridership Model Final Report The National Center for Smart Growth Research and Education University of Maryland, College Park in partnership with The Maryland Department of Transportation Time-of-Day Direct Ridership Model for Maryland Rail Transit Final Report July 21st, 2017 Prepared by: Chao Liu, Ph.D., Faculty Research Associate Hiroyuki Iseki, Ph.D., Research Faculty, Assistant Professor Sicheng Wang, Research Assistant The views expressed in this report are those of The National Center for Smart Growth Research and Education, and do not necessarily represent those of the University of Maryland or the Maryland Department of Transportation, or the State of Maryland Table of Contents Executive Summary ...................................................................................................................... 3 1. Introduction – Study Objective ........................................................................................... 1 2. Study Area, Data, and Data Sources ................................................................................... 2 2.1. Rail Stations in the Model Development ......................................................................................... 2 2.2. Data and Primary Data Sources ......................................................................................................... 4 Definition of Station Area .......................................................................................................................................................... 4 Rail Ridership by Station by Time of Day ........................................................................................................................... 4 Independent Variables ................................................................................................................................................................ 6 3. Regression Analysis .............................................................................................................. 8 4. Results and Discussion ........................................................................................................ 10 4.1. Descriptive Statistics ................................................................................................................................. 10 4.2. Spatial Distribution of Key Variables ............................................................................................ 13 4.3. Model Development Results ............................................................................................................. 18 AM Model ....................................................................................................................................................................................... 19 PM Model ........................................................................................................................................................................................ 22 Off-peak Model ............................................................................................................................................................................ 23 Limitations .................................................................................................................................................................................... 24 5. Concluding Remarks .......................................................................................................... 24 Potential Applications .............................................................................................................................................................. 26 Acknowledgements ..................................................................................................................... 27 References .................................................................................................................................... 27 Appendix A. Technical Note on Baltimore Light Rail Ridership Estimation ....................... 30 Appendix B. Zero Ridership Station after Adjustments ......................................................... 36 Appendix C. Additional Maps ................................................................................................... 38 Appendix D. Alternative Models ............................................................................................... 41 Appendix E. Land Use Mix Index and Accessibility Calculation ........................................... 41 i List of Tables Table 1 Rail Stations by System ..................................................................................................... 3 Table 2 Data and Data Sources for the DRM Development ........................................................... 4 Table 3 Multimodal Stations ........................................................................................................... 6 Table 4 Ridership by Time of Day and System ............................................................................ 11 Table 5 Number of Trains by Time of Day and System ............................................................... 11 Table 6 Number of Bus Lines by System ..................................................................................... 12 Table 7 Descriptive Statistics of Selected Independent Variables ................................................ 12 Table 8 Regression Model Results by Time of Day ..................................................................... 21 Table 9 MARC Stations with Zero Ridership by Time of Day .................................................... 36 Table 10 Variables Collected and Considered but Not Included in the Final Models ................. 37 List of Figures Figure 1 Study Area and Rail Systems ........................................................................................... 3 Figure 2 Ridership by Time of day: AM Peak .............................................................................. 13 Figure 3 Ridership by Time of day: PM Peak .............................................................................. 14 Figure 4 Number of Households ................................................................................................... 15 Figure 5 Number of Total Jobs ..................................................................................................... 16 Figure 6 Number of Trains: AM Peak .......................................................................................... 17 Figure 7 Number of Bus Lines...................................................................................................... 18 Figure 8 Flow chart for estimating Baltimore Light Rail ridership .............................................. 33 Figure 9 Ridership by Time of day: Off-peak .............................................................................. 38 Figure 10 Number of Trains: PM Peak ......................................................................................... 38 Figure 11 Number of Trains: Off-peak ......................................................................................... 39 Figure 12 Parking Capacity .......................................................................................................... 39 Figure 13 Transit Accessibility ..................................................................................................... 40 ii Executive Summary In cooperation with the Maryland Transit Administration (MTA), NCSG has developed a direct ridership model (DRM) for Maryland’s rail transit systems. This DRM is a regression-based model to estimate rail transit ridership at the station level by time of day (AM peak, PM peak, and off-peak periods) based on a set of selected location-specific factors related to transit services, land use and built environment, and demographics both at the place of residence and place of work within the walksheds of rail stations. The walksheds are defined by three walking distances: quarter-mile, half-mile, and one-mile from each station of four rail systems— Baltimore Light Rail, Baltimore Metro subway, MARC commuter rail service, and the Metrorail of WMATA (Washington Metropolitan Area Transit Authority). More specifically, walksheds are created using the most updated pedestrian network that can better represent characteristics of pedestrian-oriented walking areas. The model development has involved many analyses including pre-regression descriptive analysis, regression model development, and post-regression diagnostics. The results suggest that transit service related variables are the strongest predictors of ridership and the results are consistent across models of all time periods. Parking capacity has achieved higher coefficient in the AM model, suggesting that it might be the case that more riders used park-and-ride services in the AM periods. Feeder bus service is significant and positive in both PM and off-peak models. In the variables of land use and the built environment, employment and household are the two key predictors. As expected, the number of households is significant and positive in the AM model but is not significant in the PM and off-peak models. Employment is significant in both PM and off-peak models, but is not significant in AM model. Employment categorized as midday and weekend jobs is significant and positive in the off-peak model for non-MARC stations and the coefficient is even higher than the total employment numbers in the PM model. It is also interesting
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