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.

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

116. 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%

117. 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

118. Inductive loop counters on paths

119. Inductive Loops

120. Inductive loop counters on-street
Inductive loop counters in vehicle lane

121. Piezoelectric Bike Counters

122. Video Detection

123. Pneumatic Tube Counting
On Path
On Road

124. National Bicycle and Pedestrian
Documentation Project
http://bikepeddocumentation.org/downloads/

125. There’s an app for that!
Manual
counting on
your smart
phone!
by Thomas Götschi

126. National Bicycle and Pedestrian
Documentation Project
http://bikepeddocumentation.org/downloads/

127. Portland
Volunteer
Count
Form

128. Percent of AADT
Bike/Ped Daily Factors
160%
140%
120%
100%
80%
60%
40%
20%
0%
Group 1
Group 2
Group 3

129. 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

130. 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

131. Daily Patterns for Bike/Ped
Percent of AADT
250%
200%
150%
100%
50%
0%

132. 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

133. % 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

134. 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

135. 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

136. 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

137. 3 Steps to Estimate AADB
1. Collect continuous counts
2. Compute factors
3. Collect short duration counts

138. Compute AADB
• I know AADB at
25 continuous
count stations.

139. Motor
Vehicle
Count
Example
Iowa State University http://www.ctre.iastate.edu/pubs/traffichandbook/3trafficcounts.pdf

140. COUNTING TECHNOLOGIES

141. Permanent Counters
• Pedestrian
Infrared
Video Image Recognition
Radar
Pressure Sensor
• Bicycle
Video Image Recognition
Microwave
Magnetometers
Inductive Loop
Video Detection

142. Pedestrian Counts
• Permanent: Hourly Counts 24/7
Infrared
Video Image Recognition
Radar
Pressure Sensor
• Short Duration: One Hour to One Month
Manual
Infrared

143. 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

144. NCHRP 07-19: Testing accuracy of
existing bike/ped count technologies.
Traffic Monitoring Guide. 2013, FHWA: Washington, DC.

145. 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

146. Passive Infrared Counters
Traffic Monitoring Guide. 2013, FHWA: Washington, DC.

147. Active
Infrared
Traffic Monitoring Guide. 2013, FHWA: Washington, DC.

148. Pressure Sensors
Jean-Francois Rheault, Eco Counter
Traffic Monitoring Guide. 2013, FHWA: Washington, DC.

149. Video Image Processing
Traffic Monitoring Guide. 2013, FHWA: Washington, DC.

150. Source: Elizabeth Stolz, Sprinkle Consulting