Pedestrian andBicycle Data Collection

Pedestrian and BicycleData Collection

Krista Nordback, Ph.D., P.E.Oregon Transportation Research and Education Consortium (OTREC)

Portland State University

Overview• Introduction• Traffic Monitoring Programs• Counting Technologies• Questions• Counting Exercise

INTRODUCTION

Why measure walking & biking?

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

How many bike and walk?

• Surveys– National– Regional– Local

• Counts– Continuous– Short term

What good are counts?• Baseline use• Basis for forecasting• Validates modeling• Design• Signal timing• Safety analysis• Funding• Policies• Commercial value

TRAFFICMONITORINGPROGRAMS

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

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

Colorado’s Continuous Counters

• Add map of ATRs and short term counts

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

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

Can we apply thesemethods to biking and

walking?

Compute Annual Average Daily Bicyclists

(AADB)

AADT for bicyclists!

Factoring MethodAdapted from Traffic Monitoring Guide

AADB = Cknown* D * M

Cknown = known manual count for 24 hoursD = Daily FactorM = Monthly Factor

Monthly Factor

M = AADBMADB

whereMADB = Ave daily bike count in that month

July= 500

1,000= 0.5

July is 200% of AADB.

3 Steps to Estimate AADB

1. Collect continuous counts2. Compute factors3. Collect short term counts

• I know AADB at25 continuouscount stations.

Compute AADB

Compute AADB

• I know AADB at25 continuouscount stations.

• I conduct 3hour counts at100 morestations.

Signalized

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

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Short term Count Length (hours)

1 week

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.

MONTHLY

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

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

Bike/Ped and Motorist Factors

0%

50%

100%

150%

200%

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300%Ja

nuar

yFe

brua

ryM

arch

April

May

June July

Augu

stSe

ptem

ber

Oct

ober

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embe

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Perc

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fAAD

T

Group 1Group 2Group 3CDOT Group 3RecreationalMotorists

Daily Patterns for Bicycle Only

0%

50%

100%

150%

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250%

Perc

ento

fAAD

T

Daily Patterns for Bike/Ped

0%

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200%

250%

Perc

ento

fAAD

T

Bike/Ped and Motorists Factors

0%20%40%60%80%

100%120%140%160%

Perc

ento

fAAD

T Group 1Group 2Group 3CDOT Group 3RecreationalMotorists

HOURLY

Hourly Non commute Pattern

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011

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015

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: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

Hourly Commute Pattern

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0:00

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Aver

age

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lyVo

lum

e

JanFebMarAprMayJunJulAugSepOctNovDec

Source: Pam Johnson, PSU, from Hawthorne Bridge Data

COUNTING TECHNOLOGIES

Pedestrian Counts• Continuous: Hourly Counts 24/7

• Short term: One hour to one month

Infrared Manual

InfraredVideo Image Recognition

Radar

Pressure Sensor

Passive Infrared Counters

ActiveInfrared


Pressure Sensors

Jean Francois Rheault, Eco CounterTraffic Monitoring Guide. 2013, FHWA: Washington, DC.

Video Image Processing


Proxy Measures

• Pedestrian signal actuations• Crash data• Transit use

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

Traffic Monitoring Guide 2013 Update, Chapter 4.

Combined Bicycle and Pedestrian Continuous Counter

Inductive Loops

Inductive loop counters on paths

Inductive loop counters in bike lanes

Inductive loop counters on-street

Inductive loop counters in vehicle lane

Video Detection

Pneumatic Tube Counting

On Path

On Road

Questions?

MANUAL COUNTING

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

Screenline

A

B

National Bicycle and PedestrianDocumentation Project

http://bikepeddocumentation.org/downloads/

There’s an app for that!

Manualcounting onyour smartphone!

by Thomas Götschi

Turning Movement Counts

MotorVehicleCount

Example

Iowa State University http://www.ctre.iastate.edu/pubs/traffichandbook/3trafficcounts.pdf

National Bicycle and PedestrianDocumentation Project

http://bikepeddocumentation.org/downloads/

WashingtonState

PortlandVolunteer

CountForm

FIELD EXERCISE4th Ave. and Madison

We are here

The site is here

Data sheet for today

• Turning movement counts– Motor vehicles– Bicycles– Pedestrians

• Screenline– Motor vehicles– Bicycles– Pedestrians

Screenline

Go Forth!

What are counts not good for?

• Studying trip purpose• Demographics

Bike/Ped Factors

0%20%40%60%80%

100%120%140%160%

Perc

ento

fAAD

T Group 1

Group 2

Group 3

Bike/Ped and Motorists Factors

0%20%40%60%80%

100%120%140%160%

Perc

ento

fAAD

T Group 1Group 2Group 3CDOT Group 3RecreationalMotorists

Bike/Ped Factors

0%

50%

100%

150%

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nuar

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brua

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June July

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fAAD

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Group 1Group 2Group 3

Introduction

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

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

Pedestrian andBicycle Data Collection

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