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

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?

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

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

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.

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)

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.

Performed a Kernel density

MONTGOMERY COUNTY RIDEON RIDERSHIP ANALYSIS Performed a Kernel density Localized Global

Performed a Kernel density

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.

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.

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?

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!