This document provides tips for manually and automatically counting pedestrians and bicyclists. It discusses choosing count locations and forms, training data collectors, prioritizing data collection, reviewing automated count data, and using count data to analyze safety risks and develop models. The tips are intended to help obtain accurate and consistent pedestrian and bicycle counts.

1. Sweat the Details! Tips for Pedestrian and Bicycle Counting Bob Schneider, UC Berkeley Safe Transportation Research & Education Center ProWalk/ProBike Conference 2010

4. Manual Counts

5. Tip 1: Train the Data Collectors

6. “ Why do we need training? ...It’s just counting people walking and bicycling!”

9. Example Google Earth—Tele Atlas 2008

10. Example Google Earth—Tele Atlas 2008 Pedestrian Midblock Crossing Counts

11. Example Google Earth—Tele Atlas 2008 Pedestrian Segment/Screenline Counts

12. Example Google Earth—Tele Atlas 2008 Pedestrian Intersection Crossing Counts

13. Example Google Earth—Tele Atlas 2008 Pedestrian Intersection Crossing Counts

14. Example Google Earth—Tele Atlas 2008 Right Straight Left Bicyclist Intersection Turning Counts

15. Example Google Earth—Tele Atlas 2008 Right Straight Left Bicyclist Intersection Turning Counts

17. Tip 2: Choose a Good Count Form (or recording device)

18. National Documentation Project Screenline Count Form

19. Pedestrian Intersection Count Form

20. Pedestrian Intersection Count Form (“Maddox”)

21. Informal Experiment Group Using Original Group Using “Maddox”

22. Bicycle Intersection Count Form

23. Bicycle Turning Counts (Complex) Google Earth—Tele Atlas 2008 Right Straight Left Bicyclist Intersection Turning Counts

24. Bicycle Approach Counts (Simple) Google Earth—Tele Atlas 2008 Approaching from Leg C Bicyclist Intersection Counts

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. Tip 3: Identify locations that need more than one data collector in advance

28. Tip 4: Prioritize data items so that most important information is collected Essential Important Optional

30. Automated Counts

31. Tip 1: Understand the type of data that the automated counter will provide

32. “ Typical” Alameda County Pedestrian Activity Pattern (13 sites)

33. In-Pavement Loop Detectors

34. UC-Berkeley Summer Break Rain Rain Bike To Work Day Bicycle Lane Volume Pattern (Alameda County Site)

35. Tip 2: Review raw data and correct anomalies

37. Tip 3: Understand and correct for undercounting

38. Validation counts taken in Alameda County and San Francisco, CA. Included locations with different sidewalk widths, temperature, precipitation.

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.

41. “ Typical” Alameda County Pedestrian Activity Pattern (13 sites)

42. “ Typical” Alameda County Pedestrian Activity Pattern (13 sites) 2-hour count period

43. “ Typical” Pedestrian Activity Pattern vs. Employment Centers

44. “ Typical” Pedestrian Activity Pattern vs. Employment Centers

45. “ Typical” Pedestrian Activity Pattern vs. Employment Centers

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)

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)

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)

49. Seasonal Adjustment Factors Each month has a different proportion of the total annual pedestrian or bicycle volume (Example: National Documentation Project)

51. Analyze Crash Risk

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

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

54. Alameda County Pedestrian Volume Model

55. Pilot Pedestrian Volume Model Application

56. New York City Bicycle Counts (1980-2009) Source: New York City DOT, 2010

57. Seattle Bicycle Counts Source: City of Seattle

58. Community with Pedestrian Counts? Source: ?

59. Questions & Answers