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Taking Pedestrian and Bicycle Counting Programs to the Next Level

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Title: Taking Pedestrian and Bicycle Counting Programs to the Next Level
Track: Connect
Format: 90 minute panel
Abstract: Panelists will provide practical guidance for pedestrian and bicycle counting programs based on findings from NCHRP Project 07-19, "Methods and Technologies for Collecting Pedestrian and Bicycle Volume Data."
Presenters:
Presenter: Robert Schneider University of Wisconsin-Milwaukee
Co-Presenter: RJ Eldridge Toole Design Group, LLC
Co-Presenter: Conor Semler Kittelson & Associates, Inc.

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Taking Pedestrian and Bicycle Counting Programs to the Next Level

  1. 1. MOVINGFORWARDTHINKING Taking Pedestrian and Bicycle Counting Programs to the Next Level Lessons from NCHRP 07-19 Robert Schneider, UW Milwaukee RJ Eldridge, Toole Design Group Conor Semler, Kittelson & Associates ProWalk/ProBike/ProPlace Pittsburgh, PA September 2014 Source: Kittelson & Associates, Inc.
  2. 2. Presentation Overview  Introduction  State of the Practice  Count Applications  Testing Approach and Findings  Guidebook Overview  Questions and Discussion 2 Source: Toole Design Group
  3. 3. NCHRP 7-19 Research Team  Kittelson & Associates, Inc. – Principal Investigator: Paul Ryus  University of Wisconsin-Milwaukee  UC Berkeley, SafeTREC  Toole Design Group  McGill University  Quality Counts, LLC 3
  4. 4. Project Purpose  Address lack of pedestrian and bicycle volume data  Assess data collection technologies and methods  Develop guidance for practitioners 4 Source: Bob Schneider, UW-Milwaukee
  5. 5. Related Work  National Bicycle and Pedestrian Documentation Project  FHWA Traffic Monitoring Guide (TMG) – 2013 edition includes chapter on non-motorized traffic – NCHRP research complements FHWA guide 5
  6. 6. State of the Practice  Early Findings  Multimodal Count Applications 6 Source: Bob Schneider, UW-Milwaukee
  7. 7. MOVINGFORWARDTHINKING NCHRP 7-19 Survey (N = 269) 10 20 36 3 11 69 2 35 39 20 0 10 20 30 40 50 60 70 80 Other University STATE DOT U.S. federal agency U.S. County U.S. city Transit agency Non-profit/advocacy MPO Consulting firm SURVEY RESPONSES
  8. 8. NCHRP 7-19 Survey Findings • Pedestrian and bicycle counts are becoming routine for cities, MPOs, and State DOTs. • Manual counts are the most prevalent data collection method • Most programs lack formal or dedicated funding source and rely heavily on volunteers • Organizations serving larger communities are more likely to conduct pedestrian and/or bicycle counts. • Some integration with motor vehicle count databases.
  9. 9. Current Methods of Bicycle Counting
  10. 10. NCHRP 7-19 Survey Findings  There is no standard approach for count programs  Practitioners are looking for more guidance – Choosing devices – Selecting locations – Count intervals and duration – Temporal/seasonal adjustments
  11. 11. Pedestrian & Bicycle Count Applications  Measuring facility usage  Evaluating before-and-after data  Monitoring travel patterns  Safety analysis  Project prioritization  Multimodal modeling 11
  12. 12. Measuring Facility Usage  Transportation system monitoring program  Typically requires collecting counts at set locations and regular intervals  Critical for tracking progress and measuring success 12 Change in Walking and Bicycling Activity at Washington State Count Sites, 2009–2012 Source: Washington State DOT (2012)
  13. 13. Evaluating Before-and-After Volumes  Measure volumes before and after facility is opened  Forecast usage of planned facilities 13 Before-and-After Bicycle Facility Usage – buffered bicycle lanes on Pennsylvania Ave Source: Kittelson & Associates, Portland State University, and Toole Design Group (2012)
  14. 14. Monitoring Travel Patterns  Developing extrapolation factors – Extrapolate short-duration counts over longer time periods – Control for effect of land use, weather, demographics, etc.  Evaluating user behavior patterns – Identify factors that influence walking/biking – Controllable (land use) and uncontrollable (weather) factors 14
  15. 15. Safety Analysis  Quantifying exposure – Variety of methods proposed to quantify exposure – One method compares pedestrian-vehicle collissions to average annual pedestrian volumes  Identifying before-and-after safety effects 15
  16. 16. 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 12th Street 5 Alameda County Pedestrian Crash Analysis
  17. 17. 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 12th Street 112,896 5,870,590 58,705,898 5 0.85 Alameda County Pedestrian Crash Analysis
  18. 18. 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 Broadway 12th Street 112,896 5,870,590 58,705,898 5 0.85 Alameda County Pedestrian Crash Analysis
  19. 19. Project Prioritization  Identify high-priority locations for improvements  Identify factors that influence walking/biking and prioritize accordingly  Measure improper user behaviors (i.e., wrong-way bike riding) to identify areas needing improvement 19
  20. 20. Multimodal Model Development  Multimodal travel demand modeling is an emerging field  Potential to estimate demand over a large area and forecast influence of infrastructure changes 20 Source: City of Berkeley, CA Pedestrian Master Plan
  21. 21. We need these data fields! Institutionalize Pedestrian & Bicycle Data  A multimodal transportation system requires collecting data for all modes of transportation  Establish baseline for pedestrian & bicycle safety, infrastructure, volumes, etc. X-Street Traffic Volume Mainline Traffic Volume X-Street Pedestrian Volume Mainline Pedestrian Volume
  22. 22. Challenges to Bicycle and Pedestrian Counting  Where to count?  When (how long/how frequently) to count?  What to count?  How to count?  Site/Mode challenges  Who should be counting? Source: Toole Design Group
  23. 23. Arlington County’s automated bicycle and pedestrian count program • First counter Custis Trail: Fall 2009 • Now 30 automatic counter stations • Automatic uploads to central server • Web based “dashboard” • Plans for real-time “totem” counter display Case Study: Arlington County, VA Statistics and graphics courtesy of David Patton Bicycle & Pedestrian Planner, Arlington County Division of Transportation
  24. 24. NCHRP 7-19 Research Approach  Focus on testing and evaluating commercially available automated technologies  Assess type of technology as opposed to a specific product  Cover a range of facility types, mix of traffic, and geographic locations  Evaluate accuracy through the use of manual count video data reduction 24
  25. 25. Types of Counts
  26. 26. Motor Vehicle Data Collection  Inductive Loops  Pneumatic Tubes  Manual Count Boards  Video cameras  ITS integration  In-vehicle sensors (toll tags) Photo Credit: Federal Highway Administration
  27. 27. Auto versus Multimodal Counts  Key differences: – Differences in demand variability – Ease of detection – Experience with counting technology 27 0 2,000 4,000 6,000 8,000 10,000 12,000 0 6 12 18 24 Hourly Volume (veh/h) Hour Starting Monday Tuesday Wednesday Thursday Friday Annual Weekday Average
  28. 28. Motor Vehicle Data Collection Constrained; somewhat predictable Source: Toole Design Group
  29. 29. Bicycle Data Collection Constrained environments easy to monitor Complex environments harder to define Detection Source: Toole Design Group
  30. 30. Pedestrian Data Collection Constrained environments easy to monitor Detection People tend to make their own path Source: Toole Design Group
  31. 31. Motor vehicle data collection • Widely collected • Easy to track vehicle movements • Predictable patterns and routes • Years of trend data to analyze Bicycle and pedestrian data collection • Sparsely collected • Difficult to track and tabulate movements • Unpredictable paths of travel • Weather and seasonal impacts • Lack of historical data Challenge: Site/Mode characteristics
  32. 32. Counting System  Sensor technology one piece of counting system 32
  33. 33. MOVINGFORWARDTHINKING NCHRP 07-19 Evaluation Automated devices that count all users (pedestrians and bicyclists)
  34. 34. Types of Counts
  35. 35. Passive Infrared (IR)  Detect pedestrians and cyclists by infrared radiation (heat) patterns them emit  Passive infrared sensor placed on one side of facility  Widely used and tested 35 Source: Toole Design Group
  36. 36. Active Infrared (IR)  Transmitter and receiver with IR beam  Counts caused by “breaking the beam” 36 Source: Steve Hankey, University of Minnesota
  37. 37. Radio Beam  Radio signal between transmitter and receiver  Detections occur when beam is broken  Not previously tested in literature  Some distinguish bikes from peds 37 Source: Karla Kingsley, Kittelson & Associates, Inc.
  38. 38. MOVINGFORWARDTHINKING NCHRP 07-19 Evaluation Automated devices that count bicyclists only
  39. 39. Pneumatic Tubes  Tube(s) stretched across roadway  When a bicycle rides over tube, pulse of air passes through tube to detector 39 Source: Karla Kingsley, Kittelson & Associates, Inc.
  40. 40. Inductive Loops  Generate a magnetic field that detect metal parts of bicycle passing over loop  In-pavement or temporary surface loops 40 Source: Toole Design Group
  41. 41. Piezoelectric Sensor  Emit an electric signal when physically deformed  Typically embedded in pavement across travel way 41 Source: Toole Design Group
  42. 42. MOVINGFORWARDTHINKING NCHRP 07-19 Evaluation Other counting methods
  43. 43. Combination  Use one technology to detect all others plus another technology to detect bicyclists only  Provide bicycle and pedestrian counts separately 43 Photo Sources: Bob Schneider, UW-Milwaukee
  44. 44. Manual Counts  Most common type of counting, to date  Capture many different locations  Record pedestrians & bicyclists separately  Can count road crossings  Capture user characteristics (gender, helmet use, behavior, etc.) Photo by Robert Schneider, UW-Milwaukee
  45. 45. Washington State DOT Pedestrian & Bicycle Counts Source: Cascade Bicycle Club. Washington State Bicycle and Pedestrian Documentation Project, Prepared for the Washington State Department of Transportation, February 2013.
  46. 46. Existing Market Technology • Passive infrared • Active infrared • Radio beam • Pneumatic tubes • Inductive loops • Piezoelectric sensors • Combination devices • Manual Counts • Automated video • Emerging technologies Understanding Count Technologies Classify Bicycle Only Everything Sources: Toole Design Group
  47. 47. Untested/Emerging technologies  Thermal  Radar  Laser scanners  Pressure and acoustic sensors  Automated video 47
  48. 48. Related Work  Topics related to quantifying pedestrian & bicycle activity, but not covered: – Bluetooth and WiFi detection – GPS data collection – Cell/smartphone data collection – Radio frequency ID (RFID) tags – Bike sharing data – Pedestrian signal actuation buttons – Surveys – Presence detection – Trip generation 48
  49. 49. Quick Break for Questions 49 Detection
  50. 50. Study Technologies and Site Locations  Technologies – Passive infrared – Active infrared – Pneumatic tubes – Inductive loops – Piezoelectric – Radio beam – Combination of technologies 50  Site Locations – Portland, OR – San Francisco, CA – Davis, CA – Berkeley, CA – Minneapolis, MN – Washington, D.C. – Arlington, VA – Montreal, Canada
  51. 51. Washington, D.C. and Arlington, VA  Key Bridge separated path – Passive Infrared – Pneumatic Tubes – Passive Infrared with inductive loops 51 Source: Toole Design Group
  52. 52. Minneapolis, MN  Midtown Greenway multiuse path – Active Infrared – Passive Infrared – Radio Beam – Inductive Loops – Pneumatic Tubes 52 Source: Toole Design Group
  53. 53.  Eastbank Esplanade multiuse path – Passive Infrared – Pneumatic Tubes – Radio Beam Portland, OR 53 Source: Kittelson & Associates, Inc.
  54. 54. San Francisco, CA  Fell Street Bicycle Lane – Passive Infrared – Pneumatic Tubes – Inductive Loops 54 Source: Frank Proulx, UC Berkeley SafeTREC
  55. 55. Evaluation Method  Video-based manual counts  Interrater reliability tested 55 Source: Frank Proulx, UC Berkeley SafeTREC Source: Toole Design Group
  56. 56. Measures to Evaluate the Quality of Count Data from Technologies  Accuracy: The magnitude of the difference between the count produced by the technology, and actual (“ground-truth”) count. (Typically depends on user volumes, movement patterns, traffic mix, and environmental characteristics)  Consistency (Precision): The remaining variability in the count data after being corrected for expected under- or over-counting, given specific conditions. (Typically depends on the counting technology itself, how a specific vendor uses the technology in a particular product, and quality of installation) 56
  57. 57. Graphical Analysis 57 Undercounting Overcounting Source: Frank Proulx, UC Berkeley SafeTREC
  58. 58. Active Infrared Counter Validation Data (Somewhat accurate, very precise)
  59. 59. (Somewhat accurate, but not precise) Passive Infrared Counter Validation Data
  60. 60. Evaluation Criteria  Accuracy  Precision  Ease of installation, maintenance requirements, cost, flexibility of data, etc. 60
  61. 61. Summary of Data Collected Condition Passive Infrared Active Infrared Pneumatic Tubes Inductive Loops Inductive Loops (Facility) Piezo-electric Radio Beam Total hours of data 298 30 160 108 165 58 95 Temperature (°F) (mean/SD) 70 / 15 64 / 26 71 / 9 73 / 12 71 / 17 72 / 10 74 / 10 Hourly user volume (mean/SD) 240 / 190 328 / 249 218 / 203 128 / 88 200 / 176 128 / 52 129 / 130 Nighttime hours 30 3 10 13 19 15.75 3.5 Rain hours 17 0 4 7 7 0 6 Cold hours (<30 °F) 12 5 0 0 7 0 0 Hot hours (>90 °F) 11 0 0 5 5 3 4 Thunder hours 8 0 0 2 2 0 0 61
  62. 62. Passive Infrared  Easy installation  Mounts to existing pole/surface or in purpose-built pole  Potential false detections from background  Possible undercounting due to occlusion 62
  63. 63. Passive Infrared  Average undercount rate 8.75%  Differences between products tested  Correction function could account for facility width  Accuracy not affected by high temperatures 63
  64. 64. Active Infrared  Moderately easy installation – requires aligning transmitter and receiver  Single device tested – accurate and highly precise 64
  65. 65. Pneumatic Tubes  Tested BSCs – bicycle specific counters  Primarily tested tubes on multi-use paths and bicycle lanes  Issues with site on 15th Avenue in Minneapolis – Tube nails came out of ground – Severe undercounting and overcounting – Removed sensors from data set 65
  66. 66. Pneumatic Tubes  Fairly high accuracy at very high volumes  Accuracy rates not observed to decline with aging tubes  Future research in mixed traffic settings 66
  67. 67. Radio Beam  Required mounting devices 10’ apart  Tested two products: one that distinguished bicyclists and pedestrians (product A), one that did not (product B) 67
  68. 68. Radio Beam  Product B higher accuracy  Product A – low precision and lower accuracy  Occlusion errors  Temperature, lighting, rain issues 68
  69. 69. Inductive Loops  Permanent (in ground) or temporary (on surface)  Bypass errors – Cyclists passing outside bike lane – Loops leaving gaps in detection zone 69
  70. 70. Inductive Loops  Accurate and precise  Errors with age of loops not detected  Higher volumes slightly affect accuracy  No substantial difference between permanent and temporary loops 70
  71. 71. Inductive Loops  Need to mitigate bypass errors 71
  72. 72. Piezoelectric Strips  Tested one existing device, due to difficulties procuring equipment  Data suggests device is not functioning very precisely or accurately  Caution – data from single device not installed by research team 72
  73. 73. Combination Counter  Passive infrared + inductive loops  Each device also assessed separately  Inferred pedestrian volumes (Total – bikes)  Occlusion errors and undercounting  High rate of precision 73
  74. 74. Accuracy Calculations 74  Average percentage deviation (APD): overall divergence from perfect accuracy (undercounting rate)  Average of the absolute percentage deviation (AAPD): accounts for undercount/overcount cancelation (total deviation)
  75. 75. Research Conclusions Device Undercounting Rate Total Deviation Passive Infrared (2 products) 8.75% 20.11% Active Infrared 9.11% 11.61% Pneumatic Tubes 17.89% 18.50% Radio Beam 18.18% 48.15% Inductive Loops 0.55% 8.87% Piezoelectric Strips 11.36% 26.60% 75  Automated counter accuracy:
  76. 76. Research Conclusions  Factors influencing accuracy – Proper calibration and installation – Occlusion – Vendor differences  Factors not found to influence accuracy – Age of inductive loops or pneumatic tubes – Temperature – Snow/rain (limited data) 76
  77. 77. Guidebook Organization Quick Start Guide 1. Introduction 2. Non-Motorized Count Data Applications 3. Data Collection Planning and Implementation 4. Adjusting Count Data 5. Sensor Technology Toolbox Case Studies Manual Pedestrian and Bicyclist Counts: Example Data Collector Instructions Count Protocol Used for NCHRP Project 07-19 Appendix D. Day-of-Year Factoring Approach 77 Appendices
  78. 78. 2. Non-Motorized Count Applications  Measuring facility usage  Evaluating before-and-after data  Monitoring travel patterns  Safety analysis  Project prioritization  Multimodal modeling Source: Kittelson & Associates, Portland State University, and Toole Design Group (2012) Before-and-After Bicycle Facility Usage – buffered bicycle lanes on Pennsylvania Avenue For each application: Details Case Studies 78
  79. 79. 3. Data Collection Planning & Implementation  Covers: 1. Planning the count program 2. Implementing the count program  Provides examples, detailed guidance, checklists Source: Toole Design Group. 79
  80. 80. Common Strategy: Manual + Automated Geographic Coverage A Few Locations Many Locations Count Duration Continuous Automated Short-Term Manual
  81. 81. Comparison of Counting Technologies Characteristic Passive Infrared Active Infrared Pneumatic Tubes Inductive Loops Piezoelectric Sensor Passive IR + Inductive Loops Radio Beam (One Frequency) Radio Beam (High/Low Frequency) Automated Video1 Manual Counts2 Type of users counted All facility users Yes Yes Yes Yes Yes Yes Yes Pedestrians only Yes Yes Yes Yes Bicycles only Yes Yes Yes Yes Yes Yes Yes Pedestrians vs. bicycles Yes Yes Yes Yes Bicycles vs. automobiles Yes Yes Yes Yes Characteristics collected Different user types Yes Yes Yes Yes Direction of travel3 Yes Yes Yes Yes Yes Yes Yes Yes Yes User characteristics4 Yes Yes Types of sites counted Multiple-use trail segments Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sidewalk segments Yes Yes Yes Yes Yes Yes Yes Bicycle lane segments Yes Yes Yes Yes Yes Cycle track segments Yes Yes Yes Yes Yes Yes Shared roadway segments Yes Yes Yes Yes Roadway crossings (detect from median)5 Yes Yes Yes Yes Yes Yes Yes Yes Roadway crossings (detect from end of crosswalk) Yes Yes Intersections (identify turning movements) Yes Notes: (1) Existing “automated video” systems may not use a completely automated counting process; they may also incorporate manual data checks of automated video processing. (2) Includes manual counts from video images. (3) Technologies noted as “Yes” have at least one vendor that uses the technology to capture directionality. (4) User characteristics include estimated age, gender, helmet use, use of wheelchair or other assistive device, pedestrian and bicyclist behaviors, and other characteristics. (5) Roadway crossings at medians potentially have issues with overcounting due to people waiting in the median. Median locations were not tested during this project.
  82. 82. Characteristic Passive Infrared Active Infrared Pneumatic Tubes Inductive Loops Piezoelectric Sensor Passive IR + Inductive Loops Radio Beam (One Frequency) Radio Beam (High/Low Frequency) Automated Video1 Manual Counts2 Type of users counted All facility users Yes Yes Yes Yes Yes Yes Yes Pedestrians only Yes Yes Yes Yes Bicycles only Yes Yes Yes Yes Yes Yes Yes Pedestrians vs. bicycles Yes Yes Yes Yes Bicycles vs. automobiles Yes Yes Yes Yes Characteristics collected Different user types Yes Yes Yes Yes Direction of travel3 Yes Yes Yes Yes Yes Yes Yes Yes Yes User characteristics4 Yes Yes Types of sites counted Multiple-use trail segments Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sidewalk segments Yes Yes Yes Yes Yes Yes Yes Bicycle lane segments Yes Yes Yes Yes Yes Cycle track segments Yes Yes Yes Yes Yes Yes Shared roadway segments Yes Yes Yes Yes Roadway crossings (detect from median)5 Yes Yes Yes Yes Yes Yes Yes Yes Roadway crossings (detect from end of crosswalk) Yes Yes Intersections (identify turning movements) Yes Notes: (1) Existing “automated video” systems may not use a completely automated counting process; they may also incorporate manual data checks of automated video processing. (2) Includes manual counts from video images. (3) Technologies noted as “Yes” have at least one vendor that uses the technology to capture directionality. (4) User characteristics include estimated age, gender, helmet use, use of wheelchair or other assistive device, pedestrian and bicyclist behaviors, and other characteristics. (5) Roadway crossings at medians potentially have issues with overcounting due to people waiting in the median. Median locations were not tested during this project. Comparison of Counting Technologies
  83. 83. Characteristic Passive Infrared Active Infrared Pneumatic Tubes Inductive Loops Piezoelectric Sensor Passive IR + Inductive Loops Radio Beam (One Frequency) Radio Beam (High/Low Frequency) Automated Video1 Manual Counts2 Type of users counted All facility users Yes Yes Yes Yes Yes Yes Yes Pedestrians only Yes Yes Yes Yes Bicycles only Yes Yes Yes Yes Yes Yes Yes Pedestrians vs. bicycles Yes Yes Yes Yes Bicycles vs. automobiles Yes Yes Yes Yes Characteristics collected Different user types Yes Yes Yes Yes Direction of travel3 Yes Yes Yes Yes Yes Yes Yes Yes Yes User characteristics4 Yes Yes Types of sites counted Multiple-use trail segments Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sidewalk segments Yes Yes Yes Yes Yes Yes Yes Bicycle lane segments Yes Yes Yes Yes Yes Cycle track segments Yes Yes Yes Yes Yes Yes Shared roadway segments Yes Yes Yes Yes Roadway crossings (detect from median)5 Yes Yes Yes Yes Yes Yes Yes Yes Roadway crossings (detect from end of crosswalk) Yes Yes Intersections (identify turning movements) Yes Notes: (1) Existing “automated video” systems may not use a completely automated counting process; they may also incorporate manual data checks of automated video processing. (2) Includes manual counts from video images. (3) Technologies noted as “Yes” have at least one vendor that uses the technology to capture directionality. (4) User characteristics include estimated age, gender, helmet use, use of wheelchair or other assistive device, pedestrian and bicyclist behaviors, and other characteristics. (5) Roadway crossings at medians potentially have issues with overcounting due to people waiting in the median. Median locations were not tested during this project. Comparison of Counting Technologies
  84. 84. Characteristic Passive Infrared Active Infrared Pneumatic Tubes Inductive Loops Piezoelectric Sensor Passive IR + Inductive Loops Radio Beam (One Frequency) Radio Beam (High/Low Frequency) Automated Video1 Manual Counts2 Type of users counted All facility users Yes Yes Yes Yes Yes Yes Yes Pedestrians only Yes Yes Yes Yes Bicycles only Yes Yes Yes Yes Yes Yes Yes Pedestrians vs. bicycles Yes Yes Yes Yes Bicycles vs. automobiles Yes Yes Yes Yes Characteristics collected Different user types Yes Yes Yes Yes Direction of travel3 Yes Yes Yes Yes Yes Yes Yes Yes Yes User characteristics4 Yes Yes Types of sites counted Multiple-use trail segments Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sidewalk segments Yes Yes Yes Yes Yes Yes Yes Bicycle lane segments Yes Yes Yes Yes Yes Cycle track segments Yes Yes Yes Yes Yes Yes Shared roadway segments Yes Yes Yes Yes Roadway crossings (detect from median)5 Yes Yes Yes Yes Yes Yes Yes Yes Roadway crossings (detect from end of crosswalk) Yes Yes Intersections (identify turning movements) Yes Notes: (1) Existing “automated video” systems may not use a completely automated counting process; they may also incorporate manual data checks of automated video processing. (2) Includes manual counts from video images. (3) Technologies noted as “Yes” have at least one vendor that uses the technology to capture directionality. (4) User characteristics include estimated age, gender, helmet use, use of wheelchair or other assistive device, pedestrian and bicyclist behaviors, and other characteristics. (5) Roadway crossings at medians potentially have issues with overcounting due to people waiting in the median. Median locations were not tested during this project. Comparison of Counting Technologies
  85. 85. Characteristic Passive Infrared Active Infrared Pneumatic Tubes Inductive Loops Piezoelectric Sensor Passive IR + Inductive Loops Radio Beam (One Frequency) Radio Beam (High/Low Frequency) Automated Video1 Manual Counts2 Type of users counted All facility users Yes Yes Yes Yes Yes Yes Yes Pedestrians only Yes Yes Yes Yes Bicycles only Yes Yes Yes Yes Yes Yes Yes Pedestrians vs. bicycles Yes Yes Yes Yes Bicycles vs. automobiles Yes Yes Yes Yes Characteristics collected Different user types Yes Yes Yes Yes Direction of travel3 Yes Yes Yes Yes Yes Yes Yes Yes Yes User characteristics4 Yes Yes Types of sites counted Multiple-use trail segments Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sidewalk segments Yes Yes Yes Yes Yes Yes Yes Bicycle lane segments Yes Yes Yes Yes Yes Cycle track segments Yes Yes Yes Yes Yes Yes Shared roadway segments Yes Yes Yes Yes Roadway crossings (detect from median)5 Yes Yes Yes Yes Yes Yes Yes Yes Roadway crossings (detect from end of crosswalk) Yes Yes Intersections (identify turning movements) Yes Notes: (1) Existing “automated video” systems may not use a completely automated counting process; they may also incorporate manual data checks of automated video processing. (2) Includes manual counts from video images. (3) Technologies noted as “Yes” have at least one vendor that uses the technology to capture directionality. (4) User characteristics include estimated age, gender, helmet use, use of wheelchair or other assistive device, pedestrian and bicyclist behaviors, and other characteristics. (5) Roadway crossings at medians potentially have issues with overcounting due to people waiting in the median. Median locations were not tested during this project. Comparison of Counting Technologies
  86. 86. Characteristic Passive Infrared Active Infrared Pneumatic Tubes Inductive Loops Piezoelectric Sensor Passive IR + Inductive Loops Radio Beam (One Frequency) Radio Beam (High/Low Frequency) Automated Video1 Manual Counts2 Type of users counted All facility users Yes Yes Yes Yes Yes Yes Yes Pedestrians only Yes Yes Yes Yes Bicycles only Yes Yes Yes Yes Yes Yes Yes Pedestrians vs. bicycles Yes Yes Yes Yes Bicycles vs. automobiles Yes Yes Yes Yes Characteristics collected Different user types Yes Yes Yes Yes Direction of travel3 Yes Yes Yes Yes Yes Yes Yes Yes Yes User characteristics4 Yes Yes Types of sites counted Multiple-use trail segments Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sidewalk segments Yes Yes Yes Yes Yes Yes Yes Bicycle lane segments Yes Yes Yes Yes Yes Cycle track segments Yes Yes Yes Yes Yes Yes Shared roadway segments Yes Yes Yes Yes Roadway crossings (detect from median)5 Yes Yes Yes Yes Yes Yes Yes Yes Roadway crossings (detect from end of crosswalk) Yes Yes Intersections (identify turning movements) Yes Notes: (1) Existing “automated video” systems may not use a completely automated counting process; they may also incorporate manual data checks of automated video processing. (2) Includes manual counts from video images. (3) Technologies noted as “Yes” have at least one vendor that uses the technology to capture directionality. (4) User characteristics include estimated age, gender, helmet use, use of wheelchair or other assistive device, pedestrian and bicyclist behaviors, and other characteristics. (5) Roadway crossings at medians potentially have issues with overcounting due to people waiting in the median. Median locations were not tested during this project. Comparison of Counting Technologies
  87. 87. Characteristic Passive Infrared Active Infrared Pneumatic Tubes Inductive Loops Piezoelectric Sensor Passive IR + Inductive Loops Radio Beam (One Frequency) Radio Beam (High/Low Frequency) Automated Video1 Manual Counts2 Type of users counted All facility users Yes Yes Yes Yes Yes Yes Yes Pedestrians only Yes Yes Yes Yes Bicycles only Yes Yes Yes Yes Yes Yes Yes Pedestrians vs. bicycles Yes Yes Yes Yes Bicycles vs. automobiles Yes Yes Yes Yes Characteristics collected Different user types Yes Yes Yes Yes Direction of travel3 Yes Yes Yes Yes Yes Yes Yes Yes Yes User characteristics4 Yes Yes Types of sites counted Multiple-use trail segments Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sidewalk segments Yes Yes Yes Yes Yes Yes Yes Bicycle lane segments Yes Yes Yes Yes Yes Cycle track segments Yes Yes Yes Yes Yes Yes Shared roadway segments Yes Yes Yes Yes Roadway crossings (detect from median)5 Yes Yes Yes Yes Yes Yes Yes Yes Roadway crossings (detect from end of crosswalk) Yes Yes Intersections (identify turning movements) Yes Notes: (1) Existing “automated video” systems may not use a completely automated counting process; they may also incorporate manual data checks of automated video processing. (2) Includes manual counts from video images. (3) Technologies noted as “Yes” have at least one vendor that uses the technology to capture directionality. (4) User characteristics include estimated age, gender, helmet use, use of wheelchair or other assistive device, pedestrian and bicyclist behaviors, and other characteristics. (5) Roadway crossings at medians potentially have issues with overcounting due to people waiting in the median. Median locations were not tested during this project. Comparison of Counting Technologies
  88. 88. INSTALLATION CHECKLIST: ADVANCE PREPARATION Site visit to identify the specific installation location. Specifically, note poles that will be used, where pavement will be cut, or where utility boxes will be installed to house electronics. Verify that no potential obstructions (e.g., vegetation) or sources of interference (e.g., doorway, bus stop, bicycle rack) are present. Obtain and document necessary permissions. Permits or permissions may include right-of- way encroachment permits, pavement cutting permits or bonds, landscaping permits, or interagency agreements. Obtaining these permissions may take up to several months, particularly if other agencies are involved. Create a site plan. Develop a detailed diagram of the planned installation on an aerial photo or ground-level image documenting the intended equipment installation locations and anticipated detection zone (after installation this will be useful for validating equipment either visually or with video monitoring). This diagram may be useful for obtaining installation permissions and working with contractors. Figure 3-6 provides an example site plan. Hire a contractor if necessary (or schedule appropriate resources from within the organization). Arrange an on-site coordination meeting involving all necessary parties (e.g., staff representing the organization installing the counter, permitting staff, contractors). If possible, a vendor representative should be on hand or available by phone. It may take several weeks to find a suitable time when everyone is available. Check for potential problems. Problems with the site may include interference from utility wires, upcoming constructions projects, hills, sharp curves, nearby illicit activity, and nearby insect and animal activity. Some of these conditions can be identified from imagery, but they should also be evaluated in the field.
  89. 89. INSTALLATION CHECKLIST: ADVANCE PREPARATION Site visit to identify the specific installation location. Specifically, note poles that will be used, where pavement will be cut, or where utility boxes will be installed to house electronics. Verify that no potential obstructions (e.g., vegetation) or sources of interference (e.g., doorway, bus stop, bicycle rack) are present. Obtain and document necessary permissions. Permits or permissions may include right-of- way encroachment permits, pavement cutting permits or bonds, landscaping permits, or interagency agreements. Obtaining these permissions may take up to several months, particularly if other agencies are involved. Create a site plan. Develop a detailed diagram of the planned installation on an aerial photo or ground-level image documenting the intended equipment installation locations and anticipated detection zone (after installation this will be useful for validating equipment either visually or with video monitoring). This diagram may be useful for obtaining installation permissions and working with contractors. Figure 3-6 provides an example site plan. Hire a contractor if necessary (or schedule appropriate resources from within the organization). Arrange an on-site coordination meeting involving all necessary parties (e.g., staff representing the organization installing the counter, permitting staff, contractors). If possible, a vendor representative should be on hand or available by phone. It may take several weeks to find a suitable time when everyone is available. Check for potential problems. Problems with the site may include interference from utility wires, upcoming constructions projects, hills, sharp curves, nearby illicit activity, and nearby insect and animal activity. Some of these conditions can be identified from imagery, but they should also be evaluated in the field.
  90. 90. EQUIPMENT MONITORING CHECKLIST Sensor height. Is the sensor still mounted at the correct height? Sensor direction. Is the sensor still pointed in the correct direction? Clock. Is the device’s clock set correctly? Cleaning. Remove dirt, mud, water, or other material that may affect the sensor or other vital components. Battery. Is the remaining battery life sufficient to last until the next scheduled visit? Obstructions. For example, is vegetation growing too close to the device? Unanticipated site problems. For example, is the pole being used for bicycle parking, or are people congregating in the area (as opposed to walking past the counter)? Pedestrian or bicycle detection. Are pedestrians or bicyclists passing through the counter’s detection zone being counted? If not, can the counter’s sensitivity be adjusted in the field, or does it need to be removed for repairs? Download data. Use the same export option consistently to ease the data management burden back in the office. Securement. Are the installation elements and locking devices still secure and durable? Poorly secured or loose fitted devices are more vulnerable to theft and vandalism.
  91. 91. EQUIPMENT MONITORING CHECKLIST Sensor height. Is the sensor still mounted at the correct height? Sensor direction. Is the sensor still pointed in the correct direction? Clock. Is the device’s clock set correctly? Cleaning. Remove dirt, mud, water, or other material that may affect the sensor or other vital components. Battery. Is the remaining battery life sufficient to last until the next scheduled visit? Obstructions. For example, is vegetation growing too close to the device? Unanticipated site problems. For example, is the pole being used for bicycle parking, or are people congregating in the area (as opposed to walking past the counter)? Pedestrian or bicycle detection. Are pedestrians or bicyclists passing through the counter’s detection zone being counted? If not, can the counter’s sensitivity be adjusted in the field, or does it need to be removed for repairs? Download data. Use the same export option consistently to ease the data management burden back in the office. Securement. Are the installation elements and locking devices still secure and durable? Poorly secured or loose fitted devices are more vulnerable to theft and vandalism.
  92. 92. 4. Adjusting Count Data  Sources of counter inaccuracy  Measured counter accuracy  Counter correction factors  Expansion factors  Example applications Occlusion error 92
  93. 93. Linear Correction Factors Table 4-2. Simple Counter Correction Factors Developed by NCHRP Project 07-19 Sensor Technology Adjustment Factor Hours of Data Passive infrared 1.137 298 Product A 1.037 176 Product B 1.412 122 Active infrared† 1.139 30 Radio beam 1.130 95 Product A (bicycles) 1.470 28 Product A (pedestrians) 1.323 27 Product B 1.123 39 Pneumatic tubes* 1.135 160 Product A 1.127 132 Product B 1.520 28 Surface inductive loops* 1.041 29 Embedded inductive loops* 1.054 79 Piezoelectric strips† 1.059 58 Notes: *Bicycle-specific products. †Factor is based on a single sensor at one site; use caution when applying.
  94. 94. Raw Data
  95. 95. Corrected Data
  96. 96. 5. Treatment Toolbox  Description  Typical application  Level of effort  Strengths  Limitations  Accuracy  Usage Sidebar with quick facts 96
  97. 97. Summary: Key Issues for Practice  Consider automated + manual counts  Determine whether to use permanent or mobile counters (or both)  Accuracy of devices & service vary by vendor  Results are useful for general corrections, but best practice is to conduct local ground-truth counts & make specific corrections  Consider approvals and site characteristics when selecting a count site  How critical is accuracy? 97
  98. 98. Suggested Research  Additional testing of automated technologies – Technologies not tested or underrepresented – Additional sites and conditions  Extrapolating short-duration counts to longer-duration counts  Adjustment factors for environmental factors  Uniform approach(es) for FHWA TMG? 98
  99. 99. Questions and Discussion  Contact Information – Conor Semler, Kittelson & Associates, Boston, MA csemler@kittelson.com, 857.265.2153 – Robert Schneider, UW-Milwaukee, Milwaukee, WI rjschnei@uwm.edu, 414.229.3849 – RJ Eldridge, Toole Design Group, Washington, DC reldridge@tooledesign.com, 301.927.1900 x107 99
  100. 100. (Extra Slides) 100
  101. 101. Plan the Count Program  Specify the data collection purpose  Identify data collection resources  Select count locations & determine timeframe  Consider available counting methods
  102. 102. Implement the Count Program  Obtain necessary permissions  Procure counting devices  Inventory and prepare devices  Train staff  Install and validate devices  Calibrate devices  Maintain devices  Manage count data  Clean and correct count data
  103. 103. Three Types of Data Adjustments  Cleaning erroneous data involves identifying when a count is likely to represent a time period when an automated counter was not observing the intended pedestrian or bicycle movements. Data from these time periods are discarded or replaced by better estimates.  Correcting for systematic errors involves applying a correction function that removes the expected amount of under- or overcounting for a particular counting technology (often due to occlusion).  Expanding short counts to longer time periods involves applying an expansion factor to a count collected for a short time period. The expansion factor is based on the type of activity pattern assumed to exist at the site, and it can account for the effects of weather on pedestrian and bicycle volumes.
  104. 104. Weekday Hour Raw Count Cleaned Count Corrected Count Extrapolated Count 0:00 10 10 11.2 11.2 1:00 5 5 5.6 5.6 2:00 3 3 3.36 3.36 3:00 2 2 2.24 2.24 4:00 3 3 3.36 3.36 5:00 15 15 16.8 16.8 6:00 65 65 72.8 72.8 7:00 125 125 140 140 8:00 150 150 168 168 9:00 140 140 156.8 156.8 10:00 95 95 106.4 106.4 11:00 105 105 117.6 117.6 12:00 130 130 145.6 145.6 13:00 0 115 128.8 128.8 14:00 100 100 112 112 15:00 125 125 140 140 16:00 140 140 156.8 156.8 17:00 160 160 179.2 179.2 18:00 155 155 173.6 173.6 19:00 145 145 162.4 162.4 20:00 120 120 134.4 134.4 21:00 85 85 95.2 95.2 22:00 190 45 50.4 50.4 23:00 20 20 22.4 22.4 0:00 11.2 1:00 5.6 2:00 3.36 3:00 2.24 4:00 3.36 Three Types of Data Adjustments 1 2 3
  105. 105. Example: 24-hours on one weekday
  106. 106. Example: 24-hours on one weekday
  107. 107. Adjustment 1: Clean Data
  108. 108. Adjustment 1: Clean Data
  109. 109. Adjustment 2: Correct Data +X% to correct for undercounting
  110. 110. Adjustment 3: Expand Data
  111. 111. Adjustment 3: Expand Data
  112. 112. Raw Count Data with Missing Data Imputed

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