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Spatial Analysis of Bus Boardings in Montgomery County, MD
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Spatial Analysis of Bus Boardings in Montgomery County, MD

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  • Transcript

    • 1. Identifying Areas of High Bus Ridership in Montgomery County, Maryland Matt Yeh & Errol Dufour, Greenhorne & O’Mara, Inc.
    • 2. Program Background
      • Americans With Disabilities Act (1990)
        • Title II:
          • Uniform Federal Accessibility Standards
          • ADA Standards for Accessible Design
      • Pedestrian Safety Initiative
        • Improve physical access
        • Promote public education
      • Bus Stop Improvement Program
    • 3. Prioritization Goals
      • Can passengers wait at the stop without being in danger?
      • Are stops reasonably close to a safe street crossing location?
      • Can/Should the street crossing location be improved?
      • Can passengers get to the stop along reasonably safe path?
    • 4. Hazardous Bus Stop
    • 5. Hazardous Crossing Opportunity
    • 6. Enhancements
      • Landing pad
      • Knee wall
      • Sidewalk
      • Curb Cuts
      • Crosswalks
      • Bus stop relocation
    • 7. Before and After  ADA Access
    • 8. Before and After  Drainage & Access Issues
    • 9. Before and After  Ped Refuge Island
    • 10.  
    • 11. Program Prioritization
      • data-driven methods to spatially target specific corridors for improvement (not merely anecdotal)
      • Sources
        • Bus stop inventory database
        • Safety ranking study
        • Census data
        • Trip generator/POI GIS data
        • Ridership values
    • 12. Ridership Analysis Goals
      • How can map users easily quantify the amount of riders at each stop?
      • Present a visual of ridership in order for policy makers to make decisions.
    • 13. Data limitations
      • Ridership values may not account for bus stops that have been added or removed
      • Ridership values are not the most current
      • Ridership values were manually assigned foreign keys based on bus stop GIS table
      • Bus stops are not widely dispersed (clustered along main routes)
    • 14. MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS
      • Performed a Kernel density
      Kernel density computes the density (value per area) of a particular feature in a proposed neighborhood around the features in study.
    • 15. MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS
      • Performed a Kernel density
      Localized Global
    • 16. MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS
      • Why perform a Kernel density?
      • SCOPE
      • Kernel density would provide an appropriate prediction of riders based on our concentration of .25 miles.
    • 17. MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS
      • Why perform a Kernel density?
      • APTNESS
      • A great visual showing the dispersion of riders around the bus stops. It highlights areas with high values of ridership.
    • 18. MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS Heavy concentration along State Route 97, 586 and U.S. Route 29
    • 19. MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS Ordinary Kriging - Ordinary Kriging assumes that the data contains a mean which is unknown but remains constant. - Weights for the values in the prediction process are based on the spatial correlation of the data. Correlation between points establishes the estimated value at an unsampled location.
    • 20. MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS Moran’s I / Measure of Spatial Autocorrelation
    • 21. MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS Global Ordinary Kriging Localized Ordinary Kriging Ordinary Kriging
    • 22. Ordinary Kriging
    • 23. Conclusions
      • Measure effectiveness of improvement program
      • Prioritize corridors for improvement by incorporating additional identified parameters
      • Identify separate set of parameters to find underperforming service areas
      • What “pull factors” do certain bus stops or trip generators have over others in influencing ridership?
    • 24. Questions ?
    • 25. Thank You!