The Art and Science of Pedestrian and Bicycle Data Collection

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The Art and Science ofPedestrian and Bicycle
Data Collection

• Robert J. Schneider
• Robert S. Patten
• Jennifer L. Toole
• Craig Raborn

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Presentation Overview

• Purpose of case study analysis
• Background and methodology
• How information was gathered
• Characteristics of case study communities
• Findings
• Data collection categories
• General findings
• Future research

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Why do communities collect pedestrian and bicycle
data?

• Benchmarking progress strategically on
  non-motorized projects and programs
• Be able to use data in reports, plans, and public
  presentations
• Justify spending on non-motorized facilities
• Satisfy an advocacy group
• Just to have some non-motorized data
• We found that agencies are asking the following
  types of questions

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Where is pedestrian and bicycle activity taking
place?
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When do people walk and bicycle?
6
What types of people are walking and bicycling?
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Where are pedestrian and bicycle facilities
located (or missing)?
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What is the quality of non-motorized facilities?
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How many people use non-motorized facilities
after they are constructed?
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Where do pedestrians and bicycle crashes occur?
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Why gather case studies on non-motorized data
collection?

• Limitations to national data
• Isolated community efforts ped/bike data is new

Census 2000
2002 Nat. Survey of Ped/Bike Attitudes Behaviors
2001 National Household Transportation Survey
(NHTS)
Pedestrian Trips by Purpose
2000-2003 Omnibus Household Surveys
Source Clifton and Krizek (2004)
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Project Background

• Broad request for information
• 29 case study communities
• 13 local, 7 regional, 9 state
• From 20 states and D.C.
• 6,000 to 8,000,000 residents
• Sample does not represent all data collection
  efforts

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3 General Categories of Non-Motorized Data
Collection

• Quantifying use
• Manual counts
• Automated counts
• Surveying users
• Targeting non-motorized users
• Sampling a general population
• Documenting facility extent
• Inventories
• Spatial analyses

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Manual Counts
Example Communities Albuquerque, NM Baltimore,
MD New York Region (NYMTC) Washington, DC
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Manual Counts

• Pros
• Observations can include other behaviors
• Integrating with motor vehicle counts can reduce
  counts
• Can be collected in bad weather

• Cons
• Require training
• Labor-intensive
• Can only collect data during certain time periods

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Automated Counts
Pneumatic tubesExample Community North Carolina
Piezo FilmExample Community Iowa
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Automated Counts
Passive Infrared SensorsExample Communities
Licking County, OH Cheyenne, WY
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Automated Counts
In-Pavement Loop DetectorsExample Communities
Boulder, CO Madison, WI
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Automated Counts
Active Infrared SensorsExample Community
Massachusetts
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Automated Counts
Time-Lapse VideoExample Community Davis, CA
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Automated Counts

• Pros
• Initial cost, long-term cost-savings
• Continuous data collection, 24-hours per day

• Cons
• Ability to collect a variety of data depends on
  the technology used
• Must be located appropriately and adjusted to
  proper settings
• Some accuracy is often lost (leaves, animals,
  weather)

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Surveys Targeting Non-Motorized Users
Example Communities Rhode Island Pinellas
County, FL
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Surveys Targeting Non-Motorized Users

• Pros
• Obtain detailed characteristics about
  non-motorized users
• Meet community members face-to-face

• Cons
• High labor costs
• Survey design and distribution is critical
• Differences between survey participants and
  overall population

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Surveys Sampling a General Population
Example Communities California Boulder, CO
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Surveys Sampling a General Population

• Pros
• Well-executed surveys represent entire community
• Can gather a large amount of data without field
  data collectors

• Cons
• Survey design and distribution is critical
• Randomly-selected participants
• Response rate

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Inventories
Example Communities Washington State Florida
Maryland Loudoun County, VA St. Petersburg,
FL Columbia, MO New York, NY
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Inventories

• Pros
• Can evaluate large portions of the community in
  systematic way
• Identify gaps in existing facilities
• Technology may save labor costs

• Cons
• Pre-planning is critical for efficiency
• Train data collectors immediately prior to data
  collection do data checks
• Space for comments

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Spatial Analyses
Computer-Aided Design (CAD)

• Pros
• Detailed features and accurate measurements
• ADA and streetscape inventories

• Cons
• Specialized training to operate software

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Example Community New York City, NY
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Sidewalk Condition BlueGood RedFair PinkPoor Gr
eenMissing
Example Community Sandpoint, ID
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Spatial Analyses
Geographic Information Systems (GIS)

• Pros
• Sophisticated spatial analysis capabilities
• Integrates inventory databases with maps

• Cons
• Specialized training to operate software
• Data entry can be labor-intensive

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Example Communities St. Petersburg, FL
Lexington-Fayette, KY
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Crosswalk Compliance OrangePossibly-Compliant Pin
kNon-Compliant
Example Community Seattle, WA
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Example Community Miami-Dade County, FL
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Bicycle Commute Mode Split 1990
Portland, Oregon
Example Community Portland, OR
With 1990 bikeway network...
and 1990 mode splits (by census tract)
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Bicycle Commute Mode Split 2000
Portland, Oregon
Example Community Portland, OR
With 2000 bikeway network and 2000 mode splits
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General Findings

• Benefits of ped/bike data collection
• Reasons agencies do not collect data
• Data collection process
• Techniques to increase the efficiency of data
  collection
• Institutionalization

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General Findings

• Benefits of ped/bike data collection
• Objective evidence of facility use
• Documenting changes over time (BA)
• Understanding travel patterns
• Setting facility quality standards
• Exposure data for crash analyses
• Using data in plans and other documents

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General Findings

• Reasons agencies do not collect data
• Limited funding and staff time
• Concerned that data may show too few pedestrians
  and bicyclists using facilities
• Departments in charge of data collection do not
  see pedestrians and bicycles as a part of the
  transportation mix

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General Findings

• Data collection process
• Tailored to local community
• Identify need plan collect data store data
  analyze data create plans and reports implement
  new projects
• Varying levels of success with data dissemination

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General Findings

• Increasing efficiency of data collection
• Piggybacking ped/bike observations onto existing
  data collection programs
• Volunteer and student labor
• Automated counting technologies
• Using technology for data analysis
• Usually improves over time

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General Findings

• Institutionalization
• Consistent methods repeated over time
• Benchmark progress
• Produce data at regular intervals so that data
  are available to staff
• Dont need to re-invent data collection methods
  becomes more efficient

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Looking for good examples?

• Coordinated, wide-reaching counting effort
• New York Metropolitan Transportation Commission
• Use of bicycle data in a broader study
• North Carolina DOT
• Documenting use and opinions to build political
  support
• Pinellas County, FL
• Cost-effective facility inventory and community
  involvement
• City of Sandpoint, ID
• Comprehensive data collection program
• City of Boulder, CO

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Future Research

• Additional case studies
• Documenting facility extent bike parking
  pedestrian signals lighting traffic calming
• Technologies for data collection GPS, PDAs
• Investigate potential for uniform national data
  formats

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Thank You

• Local and State Agency Representatives
• Study Sponsors and Contributors
• John Fegan (Federal Highway Administration)
• Charlie Zegeer (Pedestrian and Bicycle
  Information Center)
• Craig Raborn (Pedestrian and Bicycle Information
  Center)
• Steve Wernick (Pedestrian and Bicycle Information
  Center)
• Lynn McCallum (Toole Design Group)
• Please look for the case studies and full report
  to be posted online at
• the Pedestrian and Bicycle Information Center
• www.pedbikeinfo.org/pdf/casestudies/
• Contact information
• Charle Zegeer
• Pedestrian and Bicycle Information Center