We are Traffic: Creating Robust Bicycle and Pedestrian Count Programs

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

(OTREC)

Overview

• Introduction

• Traffic Monitoring Programs

• Non-Motorized Count Programs

• Conclusions & Recommendations

INTRODUCTION

Why measure walking & biking?

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

– Permanent

– Short duration

What good are counts?

• Funding!

• Facility Level– Change Over Time

– Planning and Design

– Safety Analysis

• Validate Regional Models

• Prioritize Projects

• Bicycle Miles Traveled (BMT)

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.

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.

Pedestrian

What?

People actually bike here?

Yes! 200 per day

What?People actually walk here?

Yes!

400 per day

TRAFFIC MONITORINGPROGRAMS

State Traffic Monitoring

Metro Count Accessed 6/13/13 http://mtehelp.tech-metrocount.com/article.aspx?key=mc5805

Commonly inductive loops

Permanent Counters

Short Duration Counters

Commonly pneumatic tubes

Colorado’s Permanent Counters

Annual Average Daily Traffic (AADT)

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

AADT

AADT

AADT

AADT

Use AADT to Estimate VMT

Sum (AADT X Segment Length) over network to compute Vehicle Miles Traveled (VMT)

COLORADO HIGHWAYS

Can we apply these methods to biking and

walking?

AADB: Annual Average Daily Bicyclists

AADT for bicyclists!

Traffic Monitoring Guide 2013:

Chapter 4 for Non-motorized Traffic

NON-MOTORIZED COUNT PROGRAMS

The TMG 2013 Approach

The TMG 2013 Approach

National Bicycle and Pedestrian Documentation Project

Manual Counts:

2 hours

5 to 7pm

Tues, Wed, or Thurs in

mid-September

http://bikepeddocumentation.org/

Passive Infrared Counters

Inductive loop counters in bike lanes

Combined Bicycle and Pedestrian Continuous Counter

The TMG 2013 Approach

Permanent Count

Program

Permanent Count

Program

Geographic/Climate Zones

Urban vs. Rural

Annual Average Daily Bicyclists (AADB)

Volume

Categories

0 500 1,000

AADB

Co

nti

nu

ou

s C

ou

nt

Stat

ion

s

Medium

High

600

200

Low

Traffic Monitoring Guide 2013 Update, Chapter 4.

Permanent Count

Program

Daily Patterns

0%

20%

40%

60%

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100%

120%

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160%

180%

% o

f A

AD

B

Colorado Example (Bikes only)

Hourly Commute Pattern

0%

5%

10%

15%

20%

25%1

2:0

0 A

M

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M

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PM

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City of Boulder Example (Bikes only)

Hourly Non-commute Pattern

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Feb

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Apr

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Jun

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Oct

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Source: Pam Johnson, PSU

Permanent Count

Program

12 Possible groups

Commute

Non-Commute

In Between

3 Daily Patterns

12 Possible groups

Commute

Non-Commute

In Between

3 Daily Patterns 2 Weekly Patterns

Commute

Non-Commute

12 Possible groups

Commute

Non-Commute

In Between

3 Daily Patterns 2 Weekly Patterns

Commute

Non-Commute

2 Annual Patterns

Commute

Non-Commute

12 Possible groups

Commute

Non-Commute

In Between

3 Daily Patterns 2 Weekly Patterns

Commute

Non-Commute

2 Annual Patterns

Commute

Non-Commute

12 Possible groups

Commute

Non-Commute

In Between

3 Daily Patterns 2 Weekly Patterns

Commute

Non-Commute

2 Annual Patterns

Commute

Non-Commute

12 Possible groups

Commute

Non-Commute

In Between

3 Daily Patterns 2 Weekly Patterns

Commute

Non-Commute

2 Annual Patterns

Commute

Non-Commute

Commute

Urban PlainsNon-commute

Mountain Non-commuteHigher

Week-ends?

Rural MtnTrail?

Weekly Pattern

Location

YesYes

NoNo

Permanent Count

Program

Factoring Method

Adapted from Traffic Monitoring Guide

AADB = Cknown* D * M

Cknown = 24-hour count

D = Daily Factor

M = Monthly Factor

Factoring Method

Adapted from Traffic Monitoring Guide

AADB = Cknown* D * M

Cknown = 24-hour count

D = Daily Factor

M = Monthly Factor

Monthly Factor

M = AADB

MADB

where

MADB = Ave daily bike count in that month

Monthly Factor

M = AADB

MADB

where

MADB = Ave daily bike count in that month

June

= 5001,000

Monthly Factor

M = AADB

MADB

where

MADB = Ave daily bike count in that month

June

= 5001,000

= 0.5

Monthly Factor

M = AADB

MADB

where

MADB = Ave daily bike count in that month

June

= 5001,000

= 0.5

Daily counts in June are twice AADB.

Groups:

MountainNon-

Commute

Front RangeNon-

Commute 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

Colorado Monthly Factors

Permanent Count

Program

How many counters/group?

0%

10%

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100%

0 1 2 3 4 5 6 7 8 9 1011121314

Pre

cisi

on

of

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nth

ly F

acto

rs

Number of Counters

Non-Commute Factors

Commute Counters

Average

Permanent Count

Program

The TMG 2013 Approach

The TMG 2013 Approach

The TMG 2013 Approach

Short Duration

Count Program

Short Duration

Count Program

Turning Movement Counts

Segment Count

A

B

Short Duration Counters• Pedestrian

• BicycleInfraredManual

Manual Pneumatic Tube Counters

Traffic Monitoring Guide 2013 Update, Chapter 4.

Short Duration

Count Program

Potential Selection Criteria

• Variety of facility types

Path On-street

Potential Selection Criteria

• Variety of land uses– Central business district

– Residential

– School/University

• Technology related criteria

Short Duration

Count Program

Count Duration

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% E

rro

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AD

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Count Duration (hours)

Count Duration

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Count Duration (hours)

1 week

Short Duration

Count Program

Schedule Counts

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100%

1 2 3 4 5 6 7 8 9 10 11 12

Ab

solu

te %

Err

or

in A

AD

B

Esti

mat

es

Month

Schedule Counts

0%

20%

40%

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100%

1 2 3 4 5 6 7 8 9 10 11 12

Ab

solu

te %

Err

or

in A

AD

T Es

tim

ate

Month

May to October bestfor Midwestern Climate

The TMG 2013 Approach

Factoring Method

Adapted from Traffic Monitoring Guide

AADB = Cknown* D * M

Cknown = 24-hour count

D = Daily Factor

M = Monthly Factor

AADB

VMT for

bicycles

CONCLUSIONS & RECOMMENDATIONS

Summary

• Traffic Monitoring Guide Approach:

– Permanent Count Program

– Short Duration Count Program

– Compute AADT for Bikes and Pedestrians

On-line Guide

www.pdx.edu/ibpi/count

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

Balance Permanent and Short Duration Programs

PERMANENT

COUNT

PROGRAM

SHORT

DURATION

COUNT

PROGRAM

Iterative Process

Iterative Process

Example

1st Year

PERMANENT

COUNT

PROGRAM

SHORT

DURATION

COUNT

PROGRAM

1 Permanent Counter 20 Manual Counts

2nd Year

PERMANENT

COUNT

PROGRAM

SHORT

DURATION

COUNT

PROGRAM

1 Permanent Counter 24 Automated Short Duration Sites(one week per site)

Rotate 1 counter all summer

3rd Year

PERMANENT

COUNT

PROGRAM

SHORT DURATION

COUNT PROGRAM

5 Permanent Counters 48 Automated Short Duration Sites(one week per site)

Rotate 2 counters all summer

4th Year

PERMANENT

COUNT

PROGRAM

SHORT DURATION COUNT

PROGRAM

6 Permanent Counters 120 Automated Short Duration Sites(one week per site)

Rotate 5 counters all summer

10th Year

PERMANENT COUNT

PROGRAM

SHORT DURATION COUNT

PROGRAM

12 Permanent Counters 720 Automated Short Duration Sites(one week per site) on 3 year rotation

Rotate 10 counters all summer on 3 year rotation

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

TRB Bike/Ped Data Subcommittee

Questions?

Krista Nordback

Nordback@pdx.edu

503-725-2897

Guide to Bicycle & Pedestrian Count Programshttp://www.pdx.edu/ibpi/count

EXTRA SLIDES

Why daily counts?

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Why daily counts?

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Why daily counts?

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Why annual average?

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1200

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Why annual average?

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1200

1 2 3 4 5 6 7 8 9 10 11 12

Ave

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635

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.

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.

The Problem

Bicycle counts live here Some bicycle

counts live here.and die here.

TMAS

No bicycle counts live here.

The Solution

bike counts

TMAS

bike counts

CDOT Continuous Counters

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

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

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%

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

Inductive loop counters on paths

Inductive Loops

Inductive loop counters on-street

Inductive loop counters in vehicle lane

Piezoelectric Bike Counters

Video Detection

Pneumatic Tube Counting

On Path

On Road

National Bicycle and Pedestrian Documentation Project

http://bikepeddocumentation.org/downloads/

There’s an app for that!

Manual counting on your smart phone!

by Thomas Götschi

National Bicycle and Pedestrian Documentation Project

http://bikepeddocumentation.org/downloads/

Portland Volunteer

Count Form

Bike/Ped Daily Factors

0%

20%

40%

60%

80%

100%

120%

140%

160%

Pe

rce

nt

of

AA

DT Group 1

Group 2

Group 3

Bike/Ped and Motorists Factors

0%

20%

40%

60%

80%

100%

120%

140%

160%

Pe

rce

nt

of

AA

DT Group 1

Group 2

Group 3

CDOT Group 3Recreational Motorists

Bike/Ped and Motorist Factors

0%

50%

100%

150%

200%

250%

300%Ja

nu

ary

Feb

ruar

y

Mar

ch

Ap

ril

May

Jun

e

July

Au

gust

Sep

tem

…

Oct

ob

er

No

vem

b…

Dec

emb

…

Pe

rce

nt

of

AA

DT

Group 1

Group 2

Group 3

CDOT Group 3Recreational Motorists

Daily Patterns for Bike/Ped

0%

50%

100%

150%

200%

250%

Pe

rce

nt

of

AA

DT

Monthly Patterns for Bike Only

0%50%

100%150%200%250%300%350%400%450%500%

0 2 4 6 8 10 12

Pe

rce

nt

of

AA

DT

Month

Monthly Pattern

0%

50%

100%

150%

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400%

450%

500%

1 2 3 4 5 6 7 8 9 10 11 12

% o

f A

AD

BP

Month

With Outliers removedDillon 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

Colorado Example (Bikes and Peds combined)

Hourly Pattern

0%

5%

10%

15%

20%

25%

% o

f A

AD

B

Arap38thArapahoe2BdwyNsideBdwySsideBldrCrkEsideBldrCrkEside2BldrCrkWsideBldrCrkWside2BrdwyBwyTmesaCentennialFoothillsFoothills2FthlsNECorFthlsSECorPrl55thNPrl55thSPrlPkwySECorPrlPkwySWCorSkunk

City of Boulder Example (Bikes only)

Bike/Ped Factors

0%

50%

100%

150%

200%

250%

300%Ja

nu

ary

Feb

ruar

y

Mar

ch

Ap

ril

May

Jun

e

July

Au

gust

Sep

tem

…

Oct

ob

er

No

vem

b…

Dec

emb

…

Pe

rce

nt

of

AA

DT

Group 1

Group 2

Group 3

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

3 Steps to Estimate AADB

1. Collect continuous counts

2. Compute factors

3. Collect short duration counts

• I know AADB at 25 continuous count stations.

Compute AADB

Motor Vehicle Count

Example

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

COUNTING TECHNOLOGIES

Permanent Counters• Pedestrian

• Bicycle

InfraredVideo Image Recognition

Radar

Pressure Sensor

Inductive Loop Video Detection

Video Image Recognition

Microwave

Magnetometers

Pedestrian Counts• Permanent: Hourly Counts 24/7

• Short Duration: One Hour to One Month

InfraredManual

InfraredVideo Image Recognition

Radar

Pressure Sensor

Bicycle Counts• Permanent: Hourly Counts 24/7

• Short Duration: One Hour to One MonthInductive Loop

Manual

Video Detection

Pneumatic Tube Counters

Video Image Recognition

Microwave

Magnetometers

NCHRP 07-19: Testing accuracy of existing bike/ped count technologies.

Traffic Monitoring Guide. 2013, FHWA: Washington, DC.

Manual Counts• Volunteer vs. Paid Staff

• Paper vs. Electronic iPhone App

• Screenline vs. Intersection Turning Movement Count

• On-site vs. Video watching in office

by Thomas Götschi

Passive Infrared Counters

Traffic Monitoring Guide. 2013, FHWA: Washington, DC.

Active Infrared

Traffic Monitoring Guide. 2013, FHWA: Washington, DC.

Pressure Sensors

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

Video Image Processing

Traffic Monitoring Guide. 2013, FHWA: Washington, DC.

Source: Elizabeth Stolz, Sprinkle Consulting