Identifying Areas of High Bus Ridership in Montgomery County, Maryland   Matt Yeh & Errol Dufour,  Greenhorne & O’Mara, Inc.
Program Background <ul><li>Americans With Disabilities Act (1990) </li></ul><ul><ul><li>Title II:  </li></ul></ul><ul><ul>...
Prioritization Goals <ul><li>Can passengers wait at the stop without being in danger? </li></ul><ul><li>Are stops reasonab...
Hazardous Bus Stop
Hazardous Crossing Opportunity
Enhancements <ul><li>Landing pad </li></ul><ul><li>Knee wall </li></ul><ul><li>Sidewalk </li></ul><ul><li>Curb Cuts </li><...
Before and After    ADA Access
Before and After   Drainage & Access Issues
Before and After   Ped Refuge Island
 
Program Prioritization <ul><li>data-driven methods to spatially target specific corridors for improvement (not merely anec...
Ridership Analysis Goals <ul><li>How can map users easily quantify the amount of riders at each stop? </li></ul><ul><li>Pr...
Data limitations <ul><li>Ridership values may not account for bus stops that have been added or removed </li></ul><ul><li>...
MONTGOMERY COUNTY RIDEON   RIDERSHIP ANALYSIS <ul><li>Performed a Kernel density </li></ul>Kernel density computes the den...
MONTGOMERY COUNTY RIDEON   RIDERSHIP ANALYSIS <ul><li>Performed a Kernel density </li></ul>Localized Global
MONTGOMERY COUNTY RIDEON   RIDERSHIP ANALYSIS <ul><li>Why perform a Kernel density? </li></ul><ul><li>SCOPE </li></ul><ul>...
MONTGOMERY COUNTY RIDEON   RIDERSHIP ANALYSIS <ul><li>Why perform a Kernel density? </li></ul><ul><li>APTNESS </li></ul><u...
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 wh...
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 <ul><li>Measure effectiveness of improvement program </li></ul><ul><li>Prioritize corridors for improvement by...
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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

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

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