Pedestrian andBicycle Data Collection
Transcript of Pedestrian andBicycle Data Collection
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Pedestrian and BicycleData Collection
Krista Nordback, Ph.D., P.E.Oregon Transportation Research and Education Consortium (OTREC)
Portland State University
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Overview• Introduction• Traffic Monitoring Programs• Counting Technologies• Questions• Counting Exercise
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INTRODUCTION
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Why measure walking & biking?
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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
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How many bike and walk?
• Surveys– National– Regional– Local
• Counts– Continuous– Short term
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What good are counts?• Baseline use• Basis for forecasting• Validates modeling• Design• Signal timing• Safety analysis• Funding• Policies• Commercial value
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TRAFFICMONITORINGPROGRAMS
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Traffic Monitoring• Required by FHWA (MAP21):
– all urban and rural principal arterial roadways– all intermodal connector roadways– the strategic defense highway network
• Historically used to allocate federal funds tostate DOTs.
• Municipalities– Planning– Signal timing
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State Traffic Monitoring
Metro Count Accessed 6/13/13 http://mtehelp.tech metrocount.com/article.aspx?key=mc5805
Commonly inductive loops
Continuous Counters
Short term CountersCommonly pneumatic tubes
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Colorado’s Continuous Counters
• Add map of ATRs and short term counts
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Colorado’s Short Term Traffic Counter
CDOT OTIS Accessed 6/18/13 http://dtdapps.coloradodot.info/Otis/HighwayData#/ui/0/1/criteria/~/184.667/210.864
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Compute AADT
AADT =Short term count
x D x M
Continuous Counts
Short-term Counts
Method to CalculateAnnual Average Daily Traffic (AADT)
EstimatedAADT
Compute Factors
D= Daily FactorM= Monthly Factor
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Can we apply thesemethods to biking and
walking?
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Compute Annual Average Daily Bicyclists
(AADB)
AADT for bicyclists!
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Factoring MethodAdapted from Traffic Monitoring Guide
AADB = Cknown* D * M
Cknown = known manual count for 24 hoursD = Daily FactorM = Monthly Factor
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Monthly Factor
M = AADBMADB
whereMADB = Ave daily bike count in that month
July= 500
1,000= 0.5
July is 200% of AADB.
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3 Steps to Estimate AADB
1. Collect continuous counts2. Compute factors3. Collect short term counts
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• I know AADB at25 continuouscount stations.
Compute AADB
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Compute AADB
• I know AADB at25 continuouscount stations.
• I conduct 3hour counts at100 morestations.
Signalized
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AADB Error withLength of Short term Counts
0%
10%
20%
30%
40%
50%
60%
70%
0 200 400 600 800
Aver
age
Abso
lute
%Di
ffere
nce
Short term Count Length (hours)
1 week
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Traffic Monitoring Guide 2013Chapter 4: Traffic Monitoring for Non motorized Traffic
1. Review the existing continuous count program2. Develop an inventory3. Determine the traffic patterns4. Establish factor groups5. Determine the appropriate number of
continuous monitoring locations6. Select specific count locations7. Compute factors for annualizing short duration
counts
Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
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MONTHLY
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Monthly Patterns for Bike Only
0%50%
100%150%200%250%300%350%400%450%500%
0 2 4 6 8 10 12
Perc
ento
fAAD
T
Month
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Monthly Pattern for Bike/Ped
0%50%
100%150%200%250%300%350%400%450%500%
0 2 4 6 8 10 12
Perc
ento
fAAD
T
Month
With Outliers removedDillon Dam PathFour MileOfficers GulchSwan MtArbaney KittleEmmaRGTEofAspenHunterCrkWoodyCrkDawson ButteGlendaleGreenlandHidden MesaSpruce MeadowsSpruce MtRock CreekCCHolly 2011KC470Broomfield Combo
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Bike/Ped and Motorist Factors
0%
50%
100%
150%
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250%
300%Ja
nuar
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brua
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fAAD
T
Group 1Group 2Group 3CDOT Group 3RecreationalMotorists
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DAILY
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Daily Patterns for Bicycle Only
0%
50%
100%
150%
200%
250%
Perc
ento
fAAD
T
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Daily Patterns for Bike/Ped
0%
50%
100%
150%
200%
250%
Perc
ento
fAAD
T
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Bike/Ped and Motorists Factors
0%20%40%60%80%
100%120%140%160%
Perc
ento
fAAD
T Group 1Group 2Group 3CDOT Group 3RecreationalMotorists
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HOURLY
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Hourly Non commute Pattern
0
50
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150
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0:00
1:00
2:00
3:00
4:00
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6:00
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8:00
9:00
10:0
011
:00
12:0
013
:00
14:0
015
:00
16:0
017
:00
18:0
019
:00
20:0
021
:00
22:0
023
:00
Aver
age
Hour
lyVo
lum
e
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Source: Pam Johnson, PSU – Recreational Pattern
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Hourly Commute Pattern
0
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200
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500
600
700
800
0:00
1:00
2:00
3:00
4:00
5:00
6:00
7:00
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9:00
10:0
011
:00
12:0
013
:00
14:0
015
:00
16:0
017
:00
18:0
019
:00
20:0
021
:00
22:0
023
:00
Aver
age
Hour
lyVo
lum
e
JanFebMarAprMayJunJulAugSepOctNovDec
Source: Pam Johnson, PSU, from Hawthorne Bridge Data
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COUNTING TECHNOLOGIES
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Pedestrian Counts• Continuous: Hourly Counts 24/7
• Short term: One hour to one month
Infrared Manual
InfraredVideo Image Recognition
Radar
Pressure Sensor
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Passive Infrared Counters
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Passive Infrared Counters
Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
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ActiveInfrared
Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
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Pressure Sensors
Jean Francois Rheault, Eco CounterTraffic Monitoring Guide. 2013, FHWA: Washington, DC.
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Video Image Processing
Traffic Monitoring Guide. 2013, FHWA: Washington, DC.
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Proxy Measures
• Pedestrian signal actuations• Crash data• Transit use
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Bicycle Counts• Continuous: Hourly Counts 24/7
• Short-term: One hour to one monthInductive Loop
Manual
Video Detection
Pneumatic Tube Counters
Video Image Recognition
Microwave
Magnetometers
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Traffic Monitoring Guide 2013 Update, Chapter 4.
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Combined Bicycle and Pedestrian Continuous Counter
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Inductive Loops
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Inductive loop counters on paths
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Inductive loop counters in bike lanes
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Inductive loop counters on-street
Inductive loop counters in vehicle lane
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Video Detection
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Pneumatic Tube Counting
On Path
On Road
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Questions?
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MANUAL COUNTING
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Manual Counts• Volunteer vs. Paid Staff
• Paper vs. Electronic count board
• Screenline vs. Intersection TurningMovement Count
• On site vs. Video watching in office
http://www.ctre.iastate.edu/pubs/traffichandbook/3trafficcounts.pdf
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Screenline
A
B
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National Bicycle and PedestrianDocumentation Project
http://bikepeddocumentation.org/downloads/
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There’s an app for that!
Manualcounting onyour smartphone!
by Thomas Götschi
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Turning Movement Counts
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MotorVehicleCount
Example
Iowa State University http://www.ctre.iastate.edu/pubs/traffichandbook/3trafficcounts.pdf
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National Bicycle and PedestrianDocumentation Project
http://bikepeddocumentation.org/downloads/
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WashingtonState
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PortlandVolunteer
CountForm
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FIELD EXERCISE4th Ave. and Madison
We are here
The site is here
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Data sheet for today
• Turning movement counts– Motor vehicles– Bicycles– Pedestrians
• Screenline– Motor vehicles– Bicycles– Pedestrians
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Screenline
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Go Forth!
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What are counts not good for?
• Studying trip purpose• Demographics
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Bike/Ped Factors
0%20%40%60%80%
100%120%140%160%
Perc
ento
fAAD
T Group 1
Group 2
Group 3
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Bike/Ped and Motorists Factors
0%20%40%60%80%
100%120%140%160%
Perc
ento
fAAD
T Group 1Group 2Group 3CDOT Group 3RecreationalMotorists
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Bike/Ped Factors
0%
50%
100%
150%
200%
250%
300%Ja
nuar
yFe
brua
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May
June July
Augu
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Oct
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Nov
embe
rDe
cem
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Perc
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fAAD
T
Group 1Group 2Group 3
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Introduction
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Factor Method• Adapted from Traffic Monitoring Guide
AADB = Cknown* H * D * M
Cknown = known manual count for one hourH = Hourly FactorD = Daily FactorM = Monthly Factor
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Factor Method• Adapted from Traffic Monitoring Guide
AADB = Cknown* H * D * MCknown = known manual count for one hourH = Ave daily count on that day of the week in that month
Ave count for that hour for that day in that month
D = Ave daily count for that monthAve daily count on that day of the week in thatmonth
M = Ave daily count for that yearAve daily count in that month