You Are the Traffic Jam: Examination of Congestion Measures Robert L. Bertini Department of Civil &...

You Are the Traffic Jam: Examination of Congestion Measures

Robert L. BertiniDepartment of Civil & Environmental EngineeringNohad A. Toulan School of Urban Studies & PlanningPortland State UniversityTransportation Research Board Annual MeetingJanuary 24, 2006

You are the Traffic Jam 2

Objectives

“You’re not stuck in a traffic jam, you are the jam!” – German public transport campaign

How is traffic congestion in

metropolitan areas defined? How is congestion measured? How reliable and accurate are

such measures? How has congestion and its

perception been changing over

the past several decades?


Historical Framework

ANCIENT Rome

Julius Caesar: Regulations to limit carriage travel.



London

17th Century: regulations to control standing coaches.

1830’s: Monetary value for congestion.



New York

1867: William P. Eno’s first traffic jam on Broadway.

1910: Word “jam” first used to describe automotive congestion, Saturday Evening Post



EISENHOWER

Famous cross country journey—stuck in the mud

Interstate Highway program


Building Capacity: U.S. Highway Miles

a All public road and street mileage in the 50 states and the District of Columbia. For years prior to 1980, some miles of nonpublic roadways are included. No consistent data on private road mileage are available. Beginning in 1998, approximately 43,000 miles of Bureau of Land Management Roads are excluded.

3,500,000

3,550,000

3,600,000

3,650,000

3,700,000

3,750,000

3,800,000

3,850,000

3,900,000

3,950,000

4,000,000

1960 1965 1970 1975 1980 1985 1990 1995 2000 2001 2002 2003

Year

U.S

. H

igh

way

Sys

tem

Mil

es


Portland

1962: Harbor Drive

Source: Oregon Department of Transportation


Congestion

Impacts people and freight 2002 “wasted” $63.2 billion Affects travel decisions Background 1980-2000

More passenger car travel (VMT +44%) More vehicles (+39%) Not much more lane mileage (+2%) More population (+24%) Real GDP (+90%)

No absolute definition (relative) Measurement problems


Survey to Frame Issues

On-line survey of transportation professionals & academics.

480 responses.


Definition of Congestion

How Is Congestion Defined? (n=557)

Time18%

Speed28%

Vol19%

LOS15%

Cycle Failure16%

Other4%




Time18%

Speed28%

Vol19%

LOS15%

Cycle Failure16%

Other4%

Time Point

Volume Time Mean

Speed Spatial

Density Travel Time Space Mean

Speed




Time18%

Speed28%

Vol19%

LOS15%

Cycle Failure16%

Other4%

“Must be able to define it.”

“Anything below the posted speed limit.”

“Below a speed threshold.”

“A perception.” “I know it when I see it.” “Should be judged by

commonly accepted community standards.”


Measurement of Congestion

How Is Congestion Measured (n=682)

Travel Time14%

Speed13%

V/C14%

LOS20%

Delay29%

Queue Length4%

Density1%

Other5%


Measurement of Congestion

How Is Congestion Measured (n=682)

Travel Time14%

Speed13%

V/C14%

LOS20%

Delay29%

Queue Length4%

Density1%

Other5%

“It is never truly measured.”


Accuracy & Reliability of Measurements

How Accurate Are Congestion Measures? (n=525)

Accurate18%

Somewhat Accurate

33%Inaccurate

14%

Unknown6%

Subjective5%

Relative4%

Variable20%


Accuracy & Reliability of Measurements

How Accurate Are Congestion Measures? (n=525)

Accurate18%

Somewhat Accurate

33%Inaccurate

14%

Unknown6%

Subjective5%

Relative4%

Variable20%

“Reasonably accurate.” “Measure the wrong

things.” “Based on personal

experiences.” “A snapshot in time.”


Changes in Congestion

How Has Congestion Changed? (n=446)

Worse79%

Flat4%

Better3%

More Available Options

6%

Varies5%

Unknown3%


Changes in Congestion

How Has Congestion Changed? (n=446)

Worse79%

Flat4%

Better3%

More Available Options

6%

Varies5%

Unknown3%

“Western cities increasing.”

“Some rust belt cities decreasing.”

“Drivers conditioned to tolerate more.”

“Need to prepare for the world as it will be.”

“Focus on accessibility.” “Consider options.”


Literature Review

FHWA Level at which transportation system performance is no

longer acceptable due to traffic interference. May vary by facility type, location and/or time of day.

Recurrent/Nonrecurrent Variability

Duration Extent Intensity Reliability

Speed Thresholds Minnesota: below 45 mph during peak periods California: below 35 mph for 15 minutes on weekdays Proposed California: below 60 mph Washington: 95th percentile travel time


Segment Level: Point Observer

Time

Dist

ance

FreeFlowSpeed

x

t = Measurement Interval

Speed

i

v -ve f = Delayve = Extrapolated Travel Time

vf = Free Flow Travel Time

Flow Capacity LOS Time Mean Speed Extrapolated Travel

Time Delay


Segment Level: Spatial Observation

Time

Dist

ance

i

d =

Segm

ent D

ista

nce

v-vi f = Delayvf = Free Flow Travel Time

vi = Actual Travel Time

j

Density Space Mean Speed Actual Travel Time Delay


Corridor Level

(a) Northbound I-5

7:06:00 AM

3:01:00 PM

7:13:00 PM

8:44:00 AM

0

20000

40000

60000

80000

100000

120000

140000

12:00:00 AM 3:00:00 AM 6:00:00 AM 9:00:00 AM 12:00:00 PM 3:00:00 PM 6:00:00 PM 9:00:00 PM 12:00:00 AM

Time

Cu

mu

lati

ve T

rave

l Tim

e (m

in)

0

20

40

60

80

100

120

140

Tra

vel T

ime

(min

)

Cumulative Travel TimeCumulative Free Flow Travel TimeTravel TimeFree Flow Travel Time

MeanS.D.Variance

28:5811.85140.35

Free Flow Travel Time 23:20 min23:30 min

28 min

50 min

24 min


Corridor Level

0

8

16

24

12:00:00 AM 3:00:00 AM 6:00:00 AM 9:00:00 AM 12:00:00 PM 3:00:00 PM 6:00:00 PM 9:00:00 PM 12:00:00 AM

Time

Act

ual

Tra

vel T

ime

- F

ree

Flo

w T

rave

l T

ime

(min

)

Delay = 8 min

8 min < Delay < 16 min

Delay > 16 min


Corridor Level: Data Fusion


Consideration of Total Trip

0

2

4

6

8

10

0 5 10 15 20 25 30 35Time (minutes)

2 %

1 4 %

5 7 %

1 9 %

5 %2 %

0 %

1 %

1 7 %5 %4 %1 % 1 7 % 4 0 % 1 2 % 5 %

25 Freeway

20

20 Arteria l

16

18

12

3

60

ArterialParking Walking

Collector

Local

Walk

Dist

ance

(mile

s)

Speed

2 7 .7 m in3 6 .1 m in

10

.6 m

ile

s

Segment Time Distance(min) (mi)

Walk 0.2 0.01Local 1.5 0.26Collector 3.4 0.76Arterial 9.4 2.76Freeway 23.8 8.76Arterial 28.3 10.26Park 30.1 10.51Walk 36.1 10.61

1.02.4

Segment speedTravel time index

2 2 %7 %5 %1 % 2 2 % 2 2 % 1 6 % 6 %


Journey to Work Data

+14%

+18%

+18%


Metropolitan Level Mobility Measures

Portland Area Trends 1982-2002

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

1982 1987 1992 1997 2002

Year

Pro

po

rtio

n to

198

2 V

alu

e

VMT

Population

Size

Size/Population

Travel Time

Minneapolis Area Trends 1982-2002

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

1982 1987 1992 1997 2002

Year

Pro

po

rtio

n to

198

2 V

alu

e

VMT

Population

Size

Size/Population

Travel Time



Portland Freeway and PAS VMT and Lane Miles, 1982-2002

100%

120%

140%

160%

180%

200%

220%

240%

1982 1987 1992 1997 2002Year

Per

cent

of 1

982

Val

ue

Freeway DVMT (1000)

Arterial DVMT (1000)

Freeway Lane Miles

Arterial Lane Miles

Minneapolis Freeway and PAS VMT and Lane Miles, 1982-2002

1.00

1.40

1.80

2.20

2.60

1982 1987 1992 1997 2002

Year

Pe

rce

nt

of

19

82

Va

lue

Freeway DVMT (1000)

Arterial DVMT (1000)

Freeway Lane Miles

Arterial Lane Miles



Major Road Congestion Delay, 1982-2002

0

20

40

60

80

100

1982 1987 1992 1997 2002Year

Phoenix

Portland

Sacramento

San Diego

San Jose

Seattle

Other

Average

An

nu

al H

ou

rs o

f D

ela

y P

er

Pe

ak

Pe

rio

d T

rav

ele

r




0

20

40

60

80

100

1982 1987 1992 1997 2002Year

Phoenix

Portland

Sacramento

San Diego

San Jose

Seattle

Other

Average

An

nu

al H

ou

rs o

f D

ela

y P

er

Pe

ak

Pe

rio

d T

rav

ele

r


0

20

40

60

80

100

1982 1987 1992 1997 2002

Year

An

nu

al H

ou

rs o

f D

ela

y P

er

Pe

ak

Pe

rio

d T

rav

ele

r

Atlanta BaltimoreDenver MinneapolisPhoenix San DiegoSeattle St. LouisTampa OtherLUA Average



Trends in Travel Time, 1982-2002

50

100

150

200

250

300

350

1982 1987 1992 1997 2002Year

PhoenixPortlandSacramentoSan Diego

San JoseSeattleOtherAverage

An

nu

al H

ou

rs o

f T

rav

el T

ime

Pe

r P

ea

k P

eri

od

Tra

ve

ler




50

100

150

200

250

300

350

1982 1987 1992 1997 2002Year

PhoenixPortlandSacramentoSan Diego

San JoseSeattleOtherAverage

An

nu

al H

ou

rs o

f Tra

vel T

ime

Per

Pea

k P

erio

d T

rave

ler


50

100

150

200

250

300

350

1982 1987 1992 1997 2002Year

An

nu

al H

ou

rs o

f T

rave

l Tim

e P

er P

eak

Per

iod

T

rave

ler




Travel Time Index, 1982-2002

1

1.1

1.2

1.3

1.4

1.5

1982 1987 1992 1997 2002Year

Tra

ve

l T

ime

In

de

x

Phoenix

Portland

Sacramento

San Diego

San Jose

Seattle

Other

Average

Travel Time Index, 1982-2002

1.0

1.1

1.2

1.3

1.4

1.5

1982 1987 1992 1997 2002Year

Tra

vel T

ime

Ind

ex


TTI=Actual/Free Flow



Travel Time and Population 2002

Las Vegas

Milw aukee

New Orleans

Buffalo Cleveland

PittsburghOklahoma City

Kansas City

Sacramento

Virginia Beach

PORTLAND

Denver

Columbus

Orlando

RiversideAtlanta

San JoseCincinnati

San Antonio Indianapolis

St. Louis

Phoenix

Minneapolis

San DiegoSeattle

TampaBaltimore

100

120

140

160

180

200

220

240

0 500 1000 1500 2000 2500 3000 3500

Population

Ann

ual H

ours

of T

rave

l Per

Pea

k P

erio

d Tr

avel

er

Travel Time and Travel Time Index 2002

BaltimoreTampaSeattle

San Diego

Minneapolis

PhoenixSt. Louis

IndianapolisSan Antonio

CincinnatiSan Jose

AtlantaRiverside

Orlando

Columbus

Denver

PORTLAND

Virginia Beach

Sacramento

Kansas City

Oklahoma CityPittsburgh

ClevelandBuffalo

New Orleans

Milw aukee

Las Vegas100

120

140

160

180

200

220

240

1 1.1 1.2 1.3 1.4 1.5

Travel Time Index

Annu

al H

ours

of T

rave

l Per

Pea

k Pe

riod

Tr

avel

er


Beyond Congestion Measures

Travel Time Budget

Source: Ausubel, Marchette and Meyer


Travel time and reliability


Other Viewpoints

Congestion occurs where people pursue economic and social interactions.

Sign of healthy economy.

Link measures to alternative mode availability.

Impact of non-work trips in peak.

Define the problem.


Conclusions

Reality and perception. Can no longer build our

way out. Need new methods for

system performance measurement.

Consider impacts on individual users and on individual trips (passenger and freight).

Travel time and reliability.


Acknowledgements

Sonoko Endo, Portland State University Brian Gregor, Oregon Department of

Transportation Tim Lomax, Texas Transportation Institute Oregon Department of Transportation Portland State University Center for Transportation

Studies Prof. Joe Sussman and Jane Holtz Kay Prof. David Levinson and Prof. Kevin Krizek,

University of MinnesotaAccess to Destinations


Questions?

You Are the Traffic Jam: Examination of Congestion Measures Robert L. Bertini Department of Civil &...

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