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We are Traffic: Creating Robust Bicycle and Pedestrian Count Programs, Krista Nordback, Ph.D.

As agencies looking to improve bicycle and pedestrian infrastructure have learned, it doesn’t count if it’s not counted. Counting provides information on the level of intersections, paths and roadways—data already available for motor vehicles but lacking for non-motorized travelers. For the first time, Federal Highway Administration’s Traffic Monitoring Guide now includes a chapter detailing how to monitor bicycle and pedestrian traffic. The slides from this webinar explain how to create a robust bicycle and pedestrian count program based on the new guidance. Agencies that show clear evidence of use are more likely to receive funding for projects, so join us and learn how to improve your existing count program or create a new one.

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Slide share countingbikes&peds6

  1. 1. We are Traffic: Creating Robust Bicycle and Pedestrian Count Programs Krista Nordback, Ph.D., P.E. Oregon Transportation Research and Education Consortium (OTREC)
  2. 2. Overview • • • • Introduction Traffic Monitoring Programs Non-Motorized Count Programs Conclusions & Recommendations
  3. 3. INTRODUCTION
  4. 4. Why measure walking & biking?
  5. 5. Why measure walking & biking?
  6. 6. Why measure walking & biking? • • • • • • • Funding & policy decisions To show change over time Facility design Planning (short-term, long-term, regional…) Economic impact Public health Safety
  7. 7. How many bike and walk? • Surveys – National – Regional – Local • Counts – Permanent – Short duration
  8. 8. What good are counts? • Funding! • Facility Level – Change Over Time – Planning and Design – Safety Analysis • Validate Regional Models • Prioritize Projects • Bicycle Miles Traveled (BMT)
  9. 9. Signal Timing Vehicle Delay Kothuri, S. M., Reynolds, T., Monsere, C. M., & Koonce, P. (2013). Testing Strategies to Reduce Pedestrian Delay at Signalized Intersections. A Pilot Study in Portland, OR. Paper presented at the 92nd Annual Meeting of the Transportation Research Board, Washington, D.C.
  10. 10. Signal Timing Vehicle Delay Pedestrian Kothuri, S. M., Reynolds, T., Monsere, C. M., & Koonce, P. (2013). Testing Strategies to Reduce Pedestrian Delay at Signalized Intersections. A Pilot Study in Portland, OR. Paper presented at the 92nd Annual Meeting of the Transportation Research Board, Washington, D.C.
  11. 11. What? People actually bike here? Yes! 200 per day
  12. 12. What? People actually walk here? Yes! 400 per day
  13. 13. TRAFFIC MONITORING PROGRAMS
  14. 14. State Traffic Monitoring Permanent Counters Commonly inductive loops Metro Count Accessed 6/13/13 http://mtehelp.tech-metrocount.com/article.aspx?key=mc5805 Short Duration Counters Commonly pneumatic tubes
  15. 15. Colorado’s Permanent Counters
  16. 16. Annual Average Daily Traffic (AADT)
  17. 17. Colorado’s Short Duration Traffic Counts CDOT OTIS Accessed 6/18/13 http://dtdapps.coloradodot.info/Otis/HighwayData#/ui/0/1/criteria/~/184.667/210.864
  18. 18. AADT
  19. 19. AADT
  20. 20. AADT
  21. 21. AADT
  22. 22. Use AADT to Estimate VMT COLORADO HIGHWAYS Sum (AADT X Segment Length) over network to compute Vehicle Miles Traveled (VMT)
  23. 23. Can we apply these methods to biking and walking?
  24. 24. AADB: Annual Average Daily Bicyclists AADT for bicyclists!
  25. 25. Traffic Monitoring Guide 2013: Chapter 4 for Nonmotorized Traffic
  26. 26. NON-MOTORIZED COUNT PROGRAMS
  27. 27. The TMG 2013 Approach
  28. 28. The TMG 2013 Approach
  29. 29. National Bicycle and Pedestrian Documentation Project Manual Counts: 2 hours 5 to 7pm Tues, Wed, or Thurs in mid-September http://bikepeddocumentation.org/
  30. 30. Passive Infrared Counters
  31. 31. Inductive loop counters in bike lanes
  32. 32. Combined Bicycle and Pedestrian Continuous Counter
  33. 33. The TMG 2013 Approach
  34. 34. Permanent Count Program
  35. 35. Permanent Count Program
  36. 36. Geographic/Climate Zones
  37. 37. Urban vs. Rural
  38. 38. 600 Continuous Count Stations Annual Average Daily Bicyclists (AADB) High Medium 200 Low Volume Categories 0 500 AADB 1,000
  39. 39. Traffic Monitoring Guide 2013 Update, Chapter 4.
  40. 40. Permanent Count Program
  41. 41. Daily Patterns 180% 160% 140% % of AADB 120% 100% 80% 60% 40% 20% 0% Colorado Example (Bikes only)
  42. 42. 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM % of AADB Hourly Commute Pattern 25% 20% 15% 10% 5% 0% City of Boulder Example (Bikes only)
  43. 43. 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Average Hourly Volume Hourly Non-commute Pattern 400 Jan 350 Feb Mar 300 Apr 250 May 200 Jun Jul 150 Aug 100 Sep 50 Oct Nov 0 Dec Source: Pam Johnson, PSU
  44. 44. Permanent Count Program
  45. 45. 12 Possible groups 3 Daily Patterns Commute In Between Non-Commute
  46. 46. 12 Possible groups 3 Daily Patterns 2 Weekly Patterns Commute Commute In Between Non-Commute Non-Commute
  47. 47. 12 Possible groups 3 Daily Patterns 2 Weekly Patterns 2 Annual Patterns Commute Commute Commute In Between Non-Commute Non-Commute Non-Commute
  48. 48. 12 Possible groups 3 Daily Patterns 2 Weekly Patterns 2 Annual Patterns Commute Commute Commute In Between Non-Commute Non-Commute Non-Commute
  49. 49. 12 Possible groups 3 Daily Patterns 2 Weekly Patterns 2 Annual Patterns Commute Commute Commute In Between Non-Commute Non-Commute Non-Commute
  50. 50. 12 Possible groups 3 Daily Patterns 2 Weekly Patterns 2 Annual Patterns Commute Commute Commute In Between Non-Commute Non-Commute Non-Commute
  51. 51. Weekly Pattern Higher Weekends? No Location Yes Rural Mtn Trail? Mountain Non-commute Yes No Urban Plains Non-commute Commute
  52. 52. Permanent Count Program
  53. 53. Factoring Method Adapted from Traffic Monitoring Guide AADB = Cknown* D * M Cknown = 24-hour count D = Daily Factor M = Monthly Factor
  54. 54. Factoring Method Adapted from Traffic Monitoring Guide AADB = Cknown* D * M Cknown = 24-hour count D = Daily Factor M = Monthly Factor
  55. 55. Monthly Factor M= AADB MADB where MADB = Ave daily bike count in that month
  56. 56. Monthly Factor June M= AADB MADB = 500 1,000 where MADB = Ave daily bike count in that month
  57. 57. Monthly Factor June M= AADB MADB = 500 1,000 = 0.5 where MADB = Ave daily bike count in that month
  58. 58. Monthly Factor June M= AADB MADB = 500 1,000 = 0.5 Daily counts in June are twice AADB. where MADB = Ave daily bike count in that month
  59. 59. Groups: Colorado Monthly Factors Mountain NonCommute Front Range NonCommute Commute January 3.9 1.5 February 3.2 2.0 March 1.3 1.2 April 2.2 1.1 1.1 May 1.0 0.8 0.9 June 0.5 0.8 0.7 July 0.4 0.8 0.8 August 0.5 0.7 0.7 September 0.7 0.8 0.8 October 1.7 1.0 1.0 November 1.5 1.4 December 2.5 2.3
  60. 60. Permanent Count Program
  61. 61. Precision of Monthly Factors How many counters/group? 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Non-Commute Factors Commute Counters Average 0 1 2 3 4 5 6 7 8 9 1011121314 Number of Counters
  62. 62. Permanent Count Program
  63. 63. The TMG 2013 Approach
  64. 64. The TMG 2013 Approach
  65. 65. The TMG 2013 Approach
  66. 66. Short Duration Count Program
  67. 67. Short Duration Count Program
  68. 68. Turning Movement Counts
  69. 69. Segment Count A B
  70. 70. Short Duration Counters • Pedestrian Manual Infrared • Bicycle Manual Pneumatic Tube Counters
  71. 71. Traffic Monitoring Guide 2013 Update, Chapter 4.
  72. 72. Short Duration Count Program
  73. 73. Potential Selection Criteria • Variety of facility types Path On-street
  74. 74. Potential Selection Criteria • Variety of land uses – Central business district – Residential – School/University • Technology related criteria
  75. 75. Short Duration Count Program
  76. 76. % Error of AADB Estimates Count Duration 70% 60% 50% 40% 30% 20% 10% 0% 0 200 400 Count Duration (hours) 600
  77. 77. % Error of AADB Estimates Count Duration 70% 60% 50% 1 week 40% 30% 20% 10% 0% 0 200 400 Count Duration (hours) 600
  78. 78. Short Duration Count Program
  79. 79. Absolute % Error in AADB Estimates Schedule Counts 100% 80% 60% 40% 20% 0% 1 2 3 4 5 6 7 Month 8 9 10 11 12
  80. 80. Absolute % Error in AADT Estimate Schedule Counts 100% 80% 60% 40% 20% 0% 1 2 3 4 5 6 7 8 9 10 11 12 Month May to October best for Midwestern Climate
  81. 81. The TMG 2013 Approach
  82. 82. Factoring Method Adapted from Traffic Monitoring Guide AADB = Cknown* D * M Cknown = 24-hour count D = Daily Factor M = Monthly Factor
  83. 83. AADB
  84. 84. VMT for bicycles
  85. 85. CONCLUSIONS & RECOMMENDATIONS
  86. 86. Summary • Traffic Monitoring Guide Approach: – Permanent Count Program – Short Duration Count Program – Compute AADT for Bikes and Pedestrians
  87. 87. On-line Guide www.pdx.edu/ibpi/count
  88. 88. Recommendations • Both permanent and short duration count programs are needed. • Continuous counters are needed! • Prefer 1 week short count • Short duration counts in high volume months – May to October (Midwestern climates) • Integrate bike/ped counts into traffic data for preservation and access
  89. 89. Balance Permanent and Short Duration Programs SHORT PERMANENT COUNT PROGRAM DURATION COUNT PROGRAM
  90. 90. Iterative Process
  91. 91. Iterative Process
  92. 92. Example
  93. 93. 1st Year SHORT PERMANENT COUNT PROGRAM 1 Permanent Counter DURATION COUNT PROGRAM 20 Manual Counts
  94. 94. 2nd Year SHORT PERMANENT COUNT PROGRAM 1 Permanent Counter Rotate 1 counter all summer DURATION COUNT PROGRAM 24 Automated Short Duration Sites (one week per site)
  95. 95. 3rd Year PERMANENT COUNT PROGRAM 5 Permanent Counters SHORT DURATION COUNT PROGRAM Rotate 2 counters all summer 48 Automated Short Duration Sites (one week per site)
  96. 96. 4th Year SHORT DURATION COUNT PROGRAM PERMANENT COUNT PROGRAM 6 Permanent Counters Rotate 5 counters all summer 120 Automated Short Duration Sites (one week per site)
  97. 97. 10th Year SHORT DURATION COUNT PROGRAM PERMANENT COUNT PROGRAM 12 Permanent Counters Rotate 10 counters all summer on 3 year rotation 720 Automated Short Duration Sites (one week per site) on 3 year rotation
  98. 98. On-going Work • Colorado, Vermont, Minnesota, Oregon, North Carolina, Washington State DOT’s are developing programs. • TRB Bike/Ped Data Subcommittee https://sites.google.com/site/bikepeddata/home • FHWA to include bike/ped counts in Travel Monitoring Analysis System (TMAS) • NCHRP 07-19: Bike/Ped Data Methods & Technologies • Google Group for future discussion! • OTREC’s Bike/Ped Data Archive
  99. 99. TRB Bike/Ped Data Subcommittee
  100. 100. Questions? Krista Nordback Nordback@pdx.edu 503-725-2897 Guide to Bicycle & Pedestrian Count Programs http://www.pdx.edu/ibpi/count
  101. 101. EXTRA SLIDES
  102. 102. Why daily counts? Average Hourly Count 70 60 50 40 30 20 10 0
  103. 103. Why daily counts? Average Hourly Count 70 60 50 40 30 20 10 0
  104. 104. Why daily counts? Average Hourly Count 70 60 50 40 30 20 10 0
  105. 105. Why annual average? Average Daily Count 1200 1000 800 600 400 200 0 1 2 3 4 5 6 7 Month 8 9 10 11 12
  106. 106. Why annual average? Average Daily Count 1200 1000 800 635 600 400 200 0 1 2 3 4 5 6 7 Month 8 9 10 11 12
  107. 107. Nosal, T., L. Miranda-Moreno, et al. (2014). Incorporating weather: a comparative analysis of Average Annual Daily Bicyclist estimation methods. 93rd Annual Meeting of the Transportation Research Board. Washington, D.C., National Academies.
  108. 108. Hankey, S., G. Lindsey, et al. (2014). Day-of-Year Scaling Factors and Design Considerations for Non-motorized Traffic Monitoring Programs. 93rd Annual Meeting of the Transportation Research Board. Washington, D.C., National Academies.
  109. 109. The Problem TMAS Bicycle counts live here and die here. Some bicycle No bicycle counts live here. counts live here.
  110. 110. The Solution TMAS bike counts bike counts
  111. 111. CDOT Continuous Counters
  112. 112. All Colorado Continuous Counters • 45 stations with enough data to study (2010-2012) – 21 bicyclist only count stations – 24 bicyclists and pedestrians combined stations Denver Metro Area
  113. 113. Short-term Counters About 6 portable infrared counters: • Rotated around the state – By request – About 30 sites • Each site over 1 week, usually at least one month
  114. 114. Colorado Count Stations Bicycle Only Bicycle & Pedestrian All Number of Stations 21 24 45 Average AADT 401 182 284 Rural 10% 88% 51% Mountains 10% 50% 31% On Paths 67% 100% 84%
  115. 115. Other Suggested Groupings • Turner, TTI: 3 factor groups – Commute – In between – Non-Commute – • Miranda-Moreno: 4 factor groups – Commute – 2 groups in between – Non-Commute
  116. 116. Inductive loop counters on paths
  117. 117. Inductive Loops
  118. 118. Inductive loop counters on-street Inductive loop counters in vehicle lane
  119. 119. Piezoelectric Bike Counters
  120. 120. Video Detection
  121. 121. Pneumatic Tube Counting On Path On Road
  122. 122. National Bicycle and Pedestrian Documentation Project http://bikepeddocumentation.org/downloads/
  123. 123. There’s an app for that! Manual counting on your smart phone! by Thomas Götschi
  124. 124. National Bicycle and Pedestrian Documentation Project http://bikepeddocumentation.org/downloads/
  125. 125. Portland Volunteer Count Form
  126. 126. Percent of AADT Bike/Ped Daily Factors 160% 140% 120% 100% 80% 60% 40% 20% 0% Group 1 Group 2 Group 3
  127. 127. Percent of AADT Bike/Ped and Motorists Factors 160% 140% 120% 100% 80% 60% 40% 20% 0% Group 1 Group 2 Group 3 CDOT Group 3 Recreational Motorists
  128. 128. Bike/Ped and Motorist Factors 300% 200% 150% Group 1 100% Group 2 Decemb… Novemb… October August July June May April March February 0% Septem… 50% January Percent of AADT 250% Group 3 Recreational CDOT Group 3 Motorists
  129. 129. Daily Patterns for Bike/Ped Percent of AADT 250% 200% 150% 100% 50% 0%
  130. 130. Percent of AADT Monthly Patterns for Bike Only 500% 450% 400% 350% 300% 250% 200% 150% 100% 50% 0% 0 2 4 6 Month 8 10 12
  131. 131. % of AADBP Monthly Pattern 500% 450% 400% 350% 300% 250% 200% 150% 100% 50% 0% With Outliers removed 1 2 3 4 5 6 7 Month Colorado Example (Bikes and Peds combined) 8 9 10 11 12 Dillon Dam Path Four Mile Officers Gulch Swan Mt Arbaney Kittle EmmaRGT EofAspen HunterCrk WoodyCrk Dawson Butte Glendale Greenland Hidden Mesa Spruce Meadows Spruce Mt Rock Creek CCHolly-2011 KC470 Broomfield Combo
  132. 132. Hourly Pattern 25% % of AADB 20% 15% 10% 5% 0% City of Boulder Example (Bikes only) Arap38th Arapahoe2 BdwyNside BdwySside BldrCrkEside BldrCrkEside2 BldrCrkWside BldrCrkWside2 Brdwy BwyTmesa Centennial Foothills Foothills2 FthlsNECor FthlsSECor Prl55thN Prl55thS PrlPkwySECor PrlPkwySWCor Skunk
  133. 133. 0% Decemb… Novemb… October Septem… August July June May April March February January Percent of AADT Bike/Ped Factors 300% 250% 200% 150% Group 1 100% Group 2 50% Group 3
  134. 134. Factor Method • Adapted from Traffic Monitoring Guide AADB = Cknown* H * D * M Cknown = known manual count for one hour H = Hourly Factor D = Daily Factor M = Monthly Factor
  135. 135. 3 Steps to Estimate AADB 1. Collect continuous counts 2. Compute factors 3. Collect short duration counts
  136. 136. Compute AADB • I know AADB at 25 continuous count stations.
  137. 137. Motor Vehicle Count Example Iowa State University http://www.ctre.iastate.edu/pubs/traffichandbook/3trafficcounts.pdf
  138. 138. COUNTING TECHNOLOGIES
  139. 139. Permanent Counters • Pedestrian Infrared Video Image Recognition Radar Pressure Sensor • Bicycle Video Image Recognition Microwave Magnetometers Inductive Loop Video Detection
  140. 140. Pedestrian Counts • Permanent: Hourly Counts 24/7 Infrared Video Image Recognition Radar Pressure Sensor • Short Duration: One Hour to One Month Manual Infrared
  141. 141. Bicycle Counts • Permanent: Hourly Counts 24/7 Video Image Recognition Microwave Magnetometers Inductive Loop Video Detection • Short Duration: One Hour to One Month Manual Pneumatic Tube Counters
  142. 142. NCHRP 07-19: Testing accuracy of existing bike/ped count technologies. Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  143. 143. Manual Counts • Volunteer vs. Paid Staff • Paper vs. Electronic iPhone App by Thomas Götschi • Screenline vs. • On-site vs. Intersection Turning Movement Count Video watching in office
  144. 144. Passive Infrared Counters Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  145. 145. Active Infrared Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  146. 146. Pressure Sensors Jean-Francois Rheault, Eco Counter Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  147. 147. Video Image Processing Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  148. 148. Source: Elizabeth Stolz, Sprinkle Consulting

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