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

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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  • 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. Overview • • • • Introduction Traffic Monitoring Programs Non-Motorized Count Programs Conclusions & Recommendations
  • 3. INTRODUCTION
  • 4. Why measure walking & biking?
  • 5. Why measure walking & biking?
  • 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. How many bike and walk? • Surveys – National – Regional – Local • Counts – Permanent – Short duration
  • 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. 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. 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. What? People actually bike here? Yes! 200 per day
  • 12. What? People actually walk here? Yes! 400 per day
  • 13. TRAFFIC MONITORING PROGRAMS
  • 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. Colorado’s Permanent Counters
  • 16. Annual Average Daily Traffic (AADT)
  • 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. AADT
  • 19. AADT
  • 20. AADT
  • 21. AADT
  • 22. Use AADT to Estimate VMT COLORADO HIGHWAYS Sum (AADT X Segment Length) over network to compute Vehicle Miles Traveled (VMT)
  • 23. Can we apply these methods to biking and walking?
  • 24. AADB: Annual Average Daily Bicyclists AADT for bicyclists!
  • 25. Traffic Monitoring Guide 2013: Chapter 4 for Nonmotorized Traffic
  • 26. NON-MOTORIZED COUNT PROGRAMS
  • 27. The TMG 2013 Approach
  • 28. The TMG 2013 Approach
  • 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. Passive Infrared Counters
  • 31. Inductive loop counters in bike lanes
  • 32. Combined Bicycle and Pedestrian Continuous Counter
  • 33. The TMG 2013 Approach
  • 34. Permanent Count Program
  • 35. Permanent Count Program
  • 36. Geographic/Climate Zones
  • 37. Urban vs. Rural
  • 38. 600 Continuous Count Stations Annual Average Daily Bicyclists (AADB) High Medium 200 Low Volume Categories 0 500 AADB 1,000
  • 39. Traffic Monitoring Guide 2013 Update, Chapter 4.
  • 40. Permanent Count Program
  • 41. Daily Patterns 180% 160% 140% % of AADB 120% 100% 80% 60% 40% 20% 0% Colorado Example (Bikes only)
  • 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. 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. Permanent Count Program
  • 45. 12 Possible groups 3 Daily Patterns Commute In Between Non-Commute
  • 46. 12 Possible groups 3 Daily Patterns 2 Weekly Patterns Commute Commute In Between Non-Commute Non-Commute
  • 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. 12 Possible groups 3 Daily Patterns 2 Weekly Patterns 2 Annual Patterns Commute Commute Commute In Between Non-Commute Non-Commute Non-Commute
  • 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. 12 Possible groups 3 Daily Patterns 2 Weekly Patterns 2 Annual Patterns Commute Commute Commute In Between Non-Commute Non-Commute Non-Commute
  • 51. Weekly Pattern Higher Weekends? No Location Yes Rural Mtn Trail? Mountain Non-commute Yes No Urban Plains Non-commute Commute
  • 52. Permanent Count Program
  • 53. Factoring Method Adapted from Traffic Monitoring Guide AADB = Cknown* D * M Cknown = 24-hour count D = Daily Factor M = Monthly Factor
  • 54. Factoring Method Adapted from Traffic Monitoring Guide AADB = Cknown* D * M Cknown = 24-hour count D = Daily Factor M = Monthly Factor
  • 55. Monthly Factor M= AADB MADB where MADB = Ave daily bike count in that month
  • 56. Monthly Factor June M= AADB MADB = 500 1,000 where MADB = Ave daily bike count in that month
  • 57. Monthly Factor June M= AADB MADB = 500 1,000 = 0.5 where MADB = Ave daily bike count in that month
  • 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. 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. Permanent Count Program
  • 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. Permanent Count Program
  • 63. The TMG 2013 Approach
  • 64. The TMG 2013 Approach
  • 65. The TMG 2013 Approach
  • 66. Short Duration Count Program
  • 67. Short Duration Count Program
  • 68. Turning Movement Counts
  • 69. Segment Count A B
  • 70. Short Duration Counters • Pedestrian Manual Infrared • Bicycle Manual Pneumatic Tube Counters
  • 71. Traffic Monitoring Guide 2013 Update, Chapter 4.
  • 72. Short Duration Count Program
  • 73. Potential Selection Criteria • Variety of facility types Path On-street
  • 74. Potential Selection Criteria • Variety of land uses – Central business district – Residential – School/University • Technology related criteria
  • 75. Short Duration Count Program
  • 76. % Error of AADB Estimates Count Duration 70% 60% 50% 40% 30% 20% 10% 0% 0 200 400 Count Duration (hours) 600
  • 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. Short Duration Count Program
  • 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. 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. The TMG 2013 Approach
  • 82. Factoring Method Adapted from Traffic Monitoring Guide AADB = Cknown* D * M Cknown = 24-hour count D = Daily Factor M = Monthly Factor
  • 83. AADB
  • 84. VMT for bicycles
  • 85. CONCLUSIONS & RECOMMENDATIONS
  • 86. Summary • Traffic Monitoring Guide Approach: – Permanent Count Program – Short Duration Count Program – Compute AADT for Bikes and Pedestrians
  • 87. On-line Guide www.pdx.edu/ibpi/count
  • 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. Balance Permanent and Short Duration Programs SHORT PERMANENT COUNT PROGRAM DURATION COUNT PROGRAM
  • 90. Iterative Process
  • 91. Iterative Process
  • 92. Example
  • 93. 1st Year SHORT PERMANENT COUNT PROGRAM 1 Permanent Counter DURATION COUNT PROGRAM 20 Manual Counts
  • 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. 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. 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. 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. 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. TRB Bike/Ped Data Subcommittee
  • 100. Questions? Krista Nordback Nordback@pdx.edu 503-725-2897 Guide to Bicycle & Pedestrian Count Programs http://www.pdx.edu/ibpi/count
  • 101. EXTRA SLIDES
  • 102. Why daily counts? Average Hourly Count 70 60 50 40 30 20 10 0
  • 103. Why daily counts? Average Hourly Count 70 60 50 40 30 20 10 0
  • 104. Why daily counts? Average Hourly Count 70 60 50 40 30 20 10 0
  • 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. 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. 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. 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. The Problem TMAS Bicycle counts live here and die here. Some bicycle No bicycle counts live here. counts live here.
  • 110. The Solution TMAS bike counts bike counts
  • 111. CDOT Continuous Counters
  • 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. 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. 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. 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. Inductive loop counters on paths
  • 117. Inductive Loops
  • 118. Inductive loop counters on-street Inductive loop counters in vehicle lane
  • 119. Piezoelectric Bike Counters
  • 120. Video Detection
  • 121. Pneumatic Tube Counting On Path On Road
  • 122. National Bicycle and Pedestrian Documentation Project http://bikepeddocumentation.org/downloads/
  • 123. There’s an app for that! Manual counting on your smart phone! by Thomas Götschi
  • 124. National Bicycle and Pedestrian Documentation Project http://bikepeddocumentation.org/downloads/
  • 125. Portland Volunteer Count Form
  • 126. Percent of AADT Bike/Ped Daily Factors 160% 140% 120% 100% 80% 60% 40% 20% 0% Group 1 Group 2 Group 3
  • 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. 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. Daily Patterns for Bike/Ped Percent of AADT 250% 200% 150% 100% 50% 0%
  • 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. % 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. 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. 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. 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. 3 Steps to Estimate AADB 1. Collect continuous counts 2. Compute factors 3. Collect short duration counts
  • 136. Compute AADB • I know AADB at 25 continuous count stations.
  • 137. Motor Vehicle Count Example Iowa State University http://www.ctre.iastate.edu/pubs/traffichandbook/3trafficcounts.pdf
  • 138. COUNTING TECHNOLOGIES
  • 139. Permanent Counters • Pedestrian Infrared Video Image Recognition Radar Pressure Sensor • Bicycle Video Image Recognition Microwave Magnetometers Inductive Loop Video Detection
  • 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. 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. NCHRP 07-19: Testing accuracy of existing bike/ped count technologies. Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  • 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. Passive Infrared Counters Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  • 145. Active Infrared Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  • 146. Pressure Sensors Jean-Francois Rheault, Eco Counter Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  • 147. Video Image Processing Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
  • 148. Source: Elizabeth Stolz, Sprinkle Consulting