Session #7 - Pedestrian & Bicycle Counting Tips - Schneider

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Session #7 - Pedestrian & Bicycle Counting Tips - Schneider

  1. 1. Sweat the Details! Tips for Pedestrian and Bicycle Counting Bob Schneider, UC Berkeley Safe Transportation Research & Education Center ProWalk/ProBike Conference 2010
  2. 2. Pedestrian and Bicycle Counting Tips <ul><li>Manual counts </li></ul><ul><li>Automated counts </li></ul><ul><li>Pedestrian and bicycle count applications </li></ul>
  3. 3. Overall advice: Count with a purpose <ul><li>Identify possible uses of count data before starting </li></ul><ul><li>Possible purposes: </li></ul><ul><ul><li>Track trends in walking & bicycling over time </li></ul></ul><ul><ul><li>Evaluate crash risk at specific locations </li></ul></ul><ul><ul><li>Show the effect of specific projects/programs on use or safety (before and after studies) </li></ul></ul><ul><ul><li>Demonstrate that there are many people walking and bicycling </li></ul></ul><ul><ul><li>Develop pedestrian or bicycle volume models </li></ul></ul>
  4. 4. Manual Counts
  5. 5. Tip 1: Train the Data Collectors
  6. 6. “ Why do we need training? ...It’s just counting people walking and bicycling!”
  7. 7. We Need Consistent, Reliable Counts <ul><li>Accuracy is most important </li></ul><ul><li>Counts will be used by transportation & planning agencies, advocates, researchers </li></ul><ul><li>Counting is easy. </li></ul><ul><li>Counting accurately & consistently is the challenge. </li></ul><ul><li>Data collectors get better with experience. </li></ul>
  8. 8. Several Different Ways to Count <ul><li>“ Intersection” </li></ul><ul><ul><li>Where two roadways cross </li></ul></ul><ul><li>“ Screenline” or “Segment” </li></ul><ul><ul><li>Along sidewalk/roadway segment </li></ul></ul><ul><ul><li>National Documentation Project </li></ul></ul><ul><li>“ Mid-block” </li></ul><ul><ul><li>Crossing in the middle of the block, away from the intersection </li></ul></ul>
  9. 9. Example Google Earth—Tele Atlas 2008
  10. 10. Example Google Earth—Tele Atlas 2008 Pedestrian Midblock Crossing Counts
  11. 11. Example Google Earth—Tele Atlas 2008 Pedestrian Segment/Screenline Counts
  12. 12. Example Google Earth—Tele Atlas 2008 Pedestrian Intersection Crossing Counts
  13. 13. Example Google Earth—Tele Atlas 2008 Pedestrian Intersection Crossing Counts
  14. 14. Example Google Earth—Tele Atlas 2008 Right Straight Left Bicyclist Intersection Turning Counts
  15. 15. Example Google Earth—Tele Atlas 2008 Right Straight Left Bicyclist Intersection Turning Counts
  16. 16. Questions in Data Collectors’ Minds Eliminate them. <ul><li>Who is a pedestrian? </li></ul><ul><ul><li>Baby in Dad’s arms? Skateboarder? Person walking a bike? </li></ul></ul><ul><li>Who is a bicyclist? </li></ul><ul><ul><li>Moped rider? Person walking a bike? </li></ul></ul><ul><li>When does a pedestrian get counted? </li></ul><ul><ul><ul><li>Jaywalking? Turning right around the corner? </li></ul></ul></ul><ul><li>When does a bicyclist get counted? </li></ul><ul><ul><ul><ul><li>Riding on sidewalk? Turning right around the corner? </li></ul></ul></ul></ul>
  17. 17. Tip 2: Choose a Good Count Form (or recording device)
  18. 18. National Documentation Project Screenline Count Form
  19. 19. Pedestrian Intersection Count Form
  20. 20. Pedestrian Intersection Count Form (“Maddox”)
  21. 21. Informal Experiment Group Using Original Group Using “Maddox”
  22. 22. Bicycle Intersection Count Form
  23. 23. Bicycle Turning Counts (Complex) Google Earth—Tele Atlas 2008 Right Straight Left Bicyclist Intersection Turning Counts
  24. 24. Bicycle Approach Counts (Simple) Google Earth—Tele Atlas 2008 Approaching from Leg C Bicyclist Intersection Counts
  25. 25. Bicycle Approach Counts (Simple) Google Earth—Tele Atlas 2008 Approaching from Leg C Bicyclist Intersection Counts Approaching from Leg A Approaching from Leg D Approaching from Leg B
  26. 26. Tip 3: Identify locations that need more than one data collector in advance
  27. 27. When do you need more than one data collector? <ul><li>Rule of thumb: 400-500 pedestrians per hour is upper limit of single data collector for intersections </li></ul><ul><li>Greater mix of pedestrians & bicyclists requires more attention/more data collectors </li></ul>
  28. 28. Tip 4: Prioritize data items so that most important information is collected Essential Important Optional
  29. 29. Possible Data Priority Ranking <ul><li>Count of pedestrians </li></ul><ul><li>Count of bicyclists </li></ul><ul><li>Gender </li></ul><ul><li>Helmet Use </li></ul><ul><li>Pedestrian Crossing Direction </li></ul><ul><li>Bicyclist Turning Movement </li></ul>
  30. 30. Automated Counts
  31. 31. Tip 1: Understand the type of data that the automated counter will provide
  32. 32. “ Typical” Alameda County Pedestrian Activity Pattern (13 sites)
  33. 33. In-Pavement Loop Detectors
  34. 34. UC-Berkeley Summer Break Rain Rain Bike To Work Day Bicycle Lane Volume Pattern (Alameda County Site)
  35. 35. Tip 2: Review raw data and correct anomalies
  36. 37. Tip 3: Understand and correct for undercounting
  37. 38. Validation counts taken in Alameda County and San Francisco, CA. Included locations with different sidewalk widths, temperature, precipitation.
  38. 39. Undercounting is likely to depend on the width and design of the sidewalk in addition to the volume of pedestrians. However, this is an early attempt to develop a general conversion function.
  39. 40. Tip 4: Use data to develop adjustment (extrapolation) factors <ul><li>Time of day, day of week, season of year </li></ul><ul><li>Land use </li></ul><ul><li>Weather </li></ul>
  40. 41. “ Typical” Alameda County Pedestrian Activity Pattern (13 sites)
  41. 42. “ Typical” Alameda County Pedestrian Activity Pattern (13 sites) 2-hour count period
  42. 43. “ Typical” Pedestrian Activity Pattern vs. Employment Centers
  43. 44. “ Typical” Pedestrian Activity Pattern vs. Employment Centers
  44. 45. “ Typical” Pedestrian Activity Pattern vs. Employment Centers
  45. 46. Land Use Adjustment Factors Counts taken at locations with specific types of land uses were multiplied by these factors to match counts taken at “typical” Alameda County Locations (Example: Alameda County, CA)
  46. 47. Weather Adjustment Factors Counts taken under certain weather conditions were multiplied by these factors to match counts taken during “typical” Alameda County weather conditions (Example: Alameda County, CA)
  47. 48. Seasonal Adjustment Factors Counts taken during the spring were multiplied by these factors to match counts taken in Alameda County during a typical time of the year (Example: Alameda County, CA)
  48. 49. Seasonal Adjustment Factors Each month has a different proportion of the total annual pedestrian or bicycle volume (Example: National Documentation Project)
  49. 50. Applications of Count Data <ul><li>Analyze crash risk </li></ul><ul><ul><li>Prioritize locations for safety treatments </li></ul></ul><ul><ul><li>Improve roadway designs & CRFs </li></ul></ul><ul><li>Develop predictive volume models </li></ul><ul><li>Track progress over time </li></ul>
  50. 51. Analyze Crash Risk
  51. 52. Alameda County Pedestrian Crash Analysis Mainline Roadway Intersecting Roadway Reported Pedestrian Crashes (1996-2005) Mission Boulevard Torrano Avenue 5 Davis Street Pierce Avenue 4 Foothill Boulevard D Street 1 Mission Boulevard Jefferson Street 5 University Avenue Bonar Street 7 International Boulevard 107th Avenue 2 San Pablo Avenue Harrison Street 2 East 14th Street Hasperian Boulevard 1 International Boulevard 46th Avenue 3 Solano Avenue Masonic Avenue 2 Broadway 12 th Street 5
  52. 53. Alameda County Pedestrian Risk Analysis Mainline Roadway Intersecting Roadway Estimated Total Weekly Pedestrian Crossings Annual Pedestrian Volume Estimate Ten-Year Pedestrian Volume Estimate Reported Pedestrian Crashes (1996-2005) Pedestrian Risk (Crashes per 10,000,000 crossings) Mission Boulevard Torrano Avenue 1,169 60,796 607,964 5 82.24 Davis Street Pierce Avenue 1,570 81,619 816,187 4 49.01 Foothill Boulevard D Street 632 32,862 328,624 1 30.43 Mission Boulevard Jefferson Street 5,236 272,246 2,722,464 5 18.37 University Avenue Bonar Street 11,175 581,113 5,811,127 7 12.05 International Boulevard 107th Avenue 3,985 207,243 2,072,429 2 9.65 San Pablo Avenue Harrison Street 4,930 256,357 2,563,572 2 7.80 East 14th Street Hasperian Boulevard 3,777 196,410 1,964,102 1 5.09 International Boulevard 46th Avenue 12,303 639,752 6,397,522 3 4.69 Solano Avenue Masonic Avenue 22,203 1,154,559 11,545,589 2 1.73 Broadway 12 th Street 112,896 5,870,590 58,705,898 5 0.85
  53. 54. Alameda County Pedestrian Volume Model
  54. 55. Pilot Pedestrian Volume Model Application
  55. 56. New York City Bicycle Counts (1980-2009) Source: New York City DOT, 2010
  56. 57. Seattle Bicycle Counts Source: City of Seattle
  57. 58. Community with Pedestrian Counts? Source: ?
  58. 59. Questions & Answers

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