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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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MOVING THINKINGFORWARD
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.
Presentation Overview
Introduction
State of the Practice
Count Applications
Testing Approach and Findings
Guidebook Overview
Questions and Discussion
2
Source: Toole Design Group
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
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
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
State of the Practice
Early Findings
Multimodal Count Applications
6
Source: Bob Schneider, UW-Milwaukee
MOVING THINKINGFORWARD
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
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.
Current Methods of Bicycle Counting
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
Pedestrian & Bicycle Count Applications
Measuring facility usage
Evaluating before-and-after data
Monitoring travel patterns
Safety analysis
Project prioritization
Multimodal modeling
11
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)
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)
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
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
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
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
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
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
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
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
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?
ACH SLIDE ADDITION
Source: Toole Design Group
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
ACH SLIDE ADDITION
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
Types of Counts
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
ACH SLIDE ADDITION
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
Ho
url
y V
olu
me
(ve
h/h
)
Hour Starting
Monday Tuesday Wednesday Thursday Friday Annual Weekday Average
Motor Vehicle Data Collection Constrained; somewhat predictable
ACH SLIDE ADDITION
Source: Toole Design Group
Bicycle Data Collection Constrained environments easy to monitor
Complex environments harder to define
Detection
ACH SLIDE ADDITION
Source: Toole Design Group
Pedestrian Data Collection Constrained environments easy to monitor
Detection
People tend to make their own path
ACH SLIDE ADDITION
Source: Toole Design Group
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
Counting System
Sensor technology one piece of counting system
32
MOVING THINKINGFORWARD
NCHRP 07-19 Evaluation Automated devices that count all users
(pedestrians and bicyclists)
Types of Counts
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
Active Infrared (IR)
Transmitter and receiver with IR beam
Counts caused by “breaking the beam”
36
Source: Steve Hankey, University of Minnesota
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.
MOVING THINKINGFORWARD
NCHRP 07-19 Evaluation Automated devices that count
bicyclists only
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.
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
Piezoelectric Sensor
Emit an electric signal when physically deformed
Typically embedded in pavement across travel way
41
Source: Toole Design Group
MOVING THINKINGFORWARD
NCHRP 07-19 Evaluation Other counting methods
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
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
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.
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
Eve
ryth
ing
Bic
ycle
On
ly
Cla
ssif
y
Sources: Toole Design Group
Untested/Emerging technologies
Thermal
Radar
Laser scanners
Pressure and acoustic sensors
Automated video
47
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
Quick Break for Questions
49
Detection
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
Washington, D.C. and Arlington, VA
Key Bridge separated path – Passive Infrared
– Pneumatic Tubes
– Passive Infrared with inductive loops
51
Source: Toole Design Group
Minneapolis, MN
Midtown Greenway multiuse path
– Active Infrared
– Passive Infrared
– Radio Beam
– Inductive Loops
– Pneumatic Tubes
52
Source: Toole Design Group
Eastbank Esplanade multiuse path – Passive Infrared
– Pneumatic Tubes
– Radio Beam
Portland, OR
53
Source: Kittelson & Associates, Inc.
San Francisco, CA
Fell Street Bicycle Lane
– Passive Infrared
– Pneumatic Tubes
– Inductive Loops
54
Source: Frank Proulx, UC Berkeley SafeTREC
Evaluation Method
Video-based manual counts
Interrater reliability tested
55
Source: Frank Proulx, UC Berkeley SafeTREC
Source: Toole Design Group
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
Graphical Analysis
57
Undercounting
Overcounting
Source: Frank Proulx, UC Berkeley SafeTREC
Active Infrared Counter Validation Data
(Somewhat accurate, very precise)
(Somewhat accurate, but not precise)
Passive Infrared Counter Validation Data
Evaluation Criteria
Accuracy
Precision
Ease of installation, maintenance requirements, cost, flexibility of data, etc.
60
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
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
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
Active Infrared
Moderately easy installation – requires aligning transmitter and receiver
Single device tested – accurate and highly precise
64
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
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
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
Radio Beam
Product B higher accuracy
Product A – low precision and lower accuracy
Occlusion errors
Temperature, lighting, rain issues
68
Inductive Loops
Permanent (in ground) or temporary (on surface)
Bypass errors
– Cyclists passing
outside bike lane
– Loops leaving gaps
in detection zone
69
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
Inductive Loops
Need to mitigate bypass errors
71
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
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
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)
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:
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
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
Ap
pen
dic
es
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
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
Common Strategy: Manual + Automated
Geographic Coverage
A Few Locations
Many Locations
Co
un
t D
ura
tio
n
Continuous Automated
Short-Term Manual
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.
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
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
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
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
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
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
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.
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.
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.
Downloading data from an automated counter in the field.
Source: Lindsay Arnold, UC Berkeley Safe Transportation Research & Education Center
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.
Downloading data from an automated counter in the field.
Source: Lindsay Arnold, UC Berkeley Safe Transportation Research & Education Center
4. Adjusting Count Data
Sources of counter inaccuracy
Measured counter accuracy
Counter correction factors
Expansion factors
Example applications
Occlusion error
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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.
Raw Data
Corrected Data
5. Treatment Toolbox
Description
Typical application
Level of effort
Strengths
Limitations
Accuracy
Usage
Sidebar with quick facts
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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?
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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
Questions and Discussion
Contact Information
– Conor Semler, Kittelson & Associates, Boston, MA [email protected], 857.265.2153
– Robert Schneider, UW-Milwaukee, Milwaukee, WI [email protected], 414.229.3849
– RJ Eldridge, Toole Design Group, Washington, DC [email protected], 301.927.1900 x107
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(Extra Slides)
100
Plan the Count Program
Specify the data collection purpose
Identify data collection resources
Select count locations & determine timeframe
Consider available counting methods
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
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.
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
Example: 24-hours on one weekday
Example: 24-hours on one weekday
Adjustment 1: Clean Data
Adjustment 1: Clean Data
Adjustment 2: Correct Data
+X% to correct for undercounting
Adjustment 3: Expand Data
Adjustment 3: Expand Data
Raw Count Data with Missing Data Imputed