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

  • Identifying Areas of High Bus Ridership in Montgomery County, Maryland Matt Yeh & Errol Dufour, Greenhorne & O’Mara, Inc.
  • 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
  • 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?
  • Hazardous Bus Stop
  • Hazardous Crossing Opportunity
  • Enhancements
    • Landing pad
    • Knee wall
    • Sidewalk
    • Curb Cuts
    • Crosswalks
    • Bus stop relocation
  • Before and After  ADA Access
  • Before and After  Drainage & Access Issues
  • Before and After  Ped Refuge Island
  •  
  • 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
  • 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.
  • 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)
  • 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.
  • MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS
    • Performed a Kernel density
    Localized Global
  • 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.
  • 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.
  • MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS Heavy concentration along State Route 97, 586 and U.S. Route 29
  • 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.
  • MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS Moran’s I / Measure of Spatial Autocorrelation
  • MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS Global Ordinary Kriging Localized Ordinary Kriging Ordinary Kriging
  • Ordinary Kriging
  • 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?
  • Questions ?
  • Thank You!