HomeMy WebLinkAboutBack-Up Document FR/SR8/27/2015 New study confirms Miami traffic is reallyjammed up I Miami Herald
TraffiC AUGUST 25, 2015
-I-
New siudy confirms Miami traffic is really
jammed up
HIGHLIGHTS
Miami is among the urban areas with the worst traffic congestion in the nation
7-M
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8/27/2015
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ALFONSO CHARDY
New study confirms Miami traffic is reallyjammed up I Miami Herald
A report to be released Wednesday confirms one of the community's worst fears: Traffic is
getting worse, not just in South Florida, but across the country.
The 2015 Urban Mobility Scorecard from the Texas A&M Transportation Institute — the
national authority on transportation issues — ranks the Miami-Dade/Broward/Palm Beach
region in I 2th place of 15 urban areas with the worst traffic congestion in the nation. The
2012 report listed Miami in 1 1th place.
While the slight improvement may seem worthy of celebration, to the people who
assembled the report the shift is really just a reflection that traffic congestion in South
Florida's metropolitan areas remains about the same as it was in the 2011-2012 time
Overall, congestion is worse in all urban areas because traffic is increasing as the economy
rebounds.
"The recession tried to do something about the traffic," said David Schrank, research
scientist at Texas A&M Transportation Institute. "And it helped some urban areas for a
while, with a few less cars on the road. But the demand is back, jobs are coming back anif
the goods and services and the commuters are out there moving on the roads now." I
"Moving up or down 10 ranks might be worthy of investigation, but there's enough wobble in
these numbers that it could easily be up or down a couple of ranks without anybody really
I OR
Over the years, traffic congestion in the region has fluctuated. In 2000, the Miami/South
Florida region placed 12th and in 2008 it was 1 5th.
The periodic Urban Mobility report is considered the nation's most accurate measure of
traffic conditions in large metropolitan areas. Report authors tracked 101 urban areas, bull
generally showcase 15 because they are somewhat similar to each other in traffic
12MMMUM
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8/27/2015
New study confirms Miami traffic is really jammed up I Miami Herald
The urban areas included in the report with rankings worse than South Florida were — in
that order — Washington D.C., Los Angeles, San Francisco, New York, San Jose, Boston an*.
Seattle. Chicago and Houston were tied for eighth place. Riverside -San Bernardino ranked
1 oth place, Dallas -Fort Worth I I th and Miami tied for 12th place with several urban areas
In the report, an urban area includes suburbs or municipalities around the urban core of a
major city. For example, data for Miamouth Florida include congestion across Miami -
Dade, Broward and Palm Beach counties.
The 2015 report includes several measures by which to judge performance of the South
Florida transport system.
"I had Miami down as 50 hours of wasted time per commuter back in 2011, but I have y I
?t 52 hours wasted per commuter in 2014," said Schrank. "This is a little worse, but your
rank is basically the same. That shows to me that on most of those pretty large areas lik
Miami are getting worse sort of at the same rate or altogether." I
"This is the amount that a commuter would lose per year because they had to drive in
congestion," Schrank said.
Another measure of how congestion makes driving worse is the so-called freeway planning
time index. This refers to the time that a driver needs to get from one place to another to
111111 1111111111U•1101V1116M
"The higher that number, the less reliable your freeways are, meaning you get on them you
have no idea how long it's going to take you to get some place" Schrank said. "The lower
that number, the more reliable, the more like that same average trip you make all the time."
The number for Miami, noted Schrank, means commuters must leave early for
appointments because of congestion.
"If you had a 20 -minute trip you could make at night in 20 minutes, in order to make sure
you got there — maybe you're trying to go to the airport where you can't afford to be late —
you'd have to allow yourself between 50 and 60 minutes to make sure you got there."
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8/27/2015 New study confirms Miami traffic is reallyjammed up I Miami Herald
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Add a cornrTient...
New study confirms Miami traffic is reallyjammed up I Miami Herald
Sort by Oldest
Nevada Del Rio - Key Biscayne, Florida
How much did that study cost the tax payers, morning traffic on tv would have been free local
4w, ckeck the overtime tarpapers pay in your city , right before holidays, for the law,
Like - Reply - ,/ 4 - Aug 26, 2015 3:08am
InMichelle Rojas
I thought the same thing. Please say we did not pay for this study. LMAO!
Like - Reply - 18 lirs
Micco Mann - University of Miami
We needed a "study" to tell us something we already know? Well, at least now it's "official": traffic
sucks in Miami.
Like - Reply - ea 3 - Aug 26, 2015 4:00ann
And our "elected officials" continue to approve projects that negatively impact the quality of life of
most lade county citizens. Traffic has only gotten worse with no major plans to improve the
infrastructure, Permits to build on any empty parcel has been the norm.
Like - Reply - V;, 5 1 - Aug 26, 2015 5:54arn
Mike Vidal - Chief Technology Officer (CTO) at Executive Learning Systems
It's called concurence, and it is in the books as oridinace, which our fearless leaders
conveniently ignore,
Like - Reply 1 Aug 26, 2015 7:06am
N a
The Only thing that would work is a, mass transportation of bus system running on a dedicated
lane in the Xpress Lane connected to a reliable city bus system on the ground. This can be done
cheaper than building more lanes but it will not be ready for a few more years, thanks to , poor
planning.
Like - Reply - u5 2 - Aug 26, 2015 7:1 0ann
Roll Back "foils
Adding more roads and widening existing highways is a very short term slolution. We are going to
have to bite the bullet and pony up for real mass transit solutions. RBT, Rapid Bus Transit is
viable alternative to Metrorail. I just hope MDX really makes this happen on their network.
Like - Reply - Aug 26, 2015 7:23am
http://www.miai-niherald.com/news/traffic/article32365728.html 5f7
8/27/2015 New study confirms Miami traffic is really jammed up I Miami Herald
Mitchell Garn - Miami, Florida
Untill or unless somebody does something about the lights, any talk of fixing our traffic prolems
are just talk.
What is so difficult about timing the traffic lights?
Like - Reply - d -LJ 2 - Aug 26, 20,15 7:38am
IsMicco Mann - University of Miami
The one at 5th and Alton should get top priority for longer east/west runs. But no, that
would be too difficult in this Bizarro World burg.
Like - Reply - Aug 26, 2015 8:14arn
f'1%'
, T
Mitchell Darn Miami, Florida
RUIP
Micco Mann No matter where you live in Dade county, you can point to one light that
regularly messes up the flow of traffic, but is never fixed.
Almost as if nobody is watching or even trying.
Like - Reply - g'L, I - Aug 26, 2015 9:4.6arn
Yamil Mi - Miami, Florida
everybody knew that. now what? I hope someone do something.
Like - Reply - Aug 26, 2015 8:34 am
What about does that drive to the front of all ready establish line to gain three or four vehicle
spots, not only messing the already established line, but also blocking a second lane of traffic.
Like - Reply - Aug 26, 2015 10:05arn
I feel a whole lot better having the obvious confirmed with a study. And I feel even better knowing
that I live in a pueblito where the primary news outlet considers this is front page worthy,
Like - Reply - 23 hrs
ll Facebook Connrnents PlUgin
Sponsored Content Hotl O.tv
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8127/2015
New study confirms Miami traffic is really jammed Lip I Miami Herald
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"lig
A
01,
ST 2015
exas,mfi&M
Transportation
Institute wm-,
�. 01&911
Tim Lomax
WO!
Texas A&M Transportation lnstitu�t
The Texas A&M University System
mobilitv.tamu.edu
•
Off.Mmam
DISCLAIMER
The contents of this report reflect the views of the authors, who are
responsible for the facts and the accuracy of the information presented
herein.
Shawn Turner, David Ellis and Phil Lasley—Concept and Methodology Development
Michelle Young, Lauren Simcic and Cheyenne McWilliams -Report Preparation
Lauren Geng and Jian Shen -GIS Assistance
Tobey Lindsey -Web Page Creation and Maintenance
Richard Cole, Bernie Eette, Michelle Hoelscher and Rick Davenport —Media Relations
John Henry—Cover Artwork
Dolores Nott and Clancy Pippin—Printing and Distribution
Rick Schuman and Myca Craven of INRIX—Technical Support and Media Relations
2O15Urban Mobility Scorecard .................... --- ... ................................... ............................... 1
Turning Congestion Data Into Insight ........... —............... .................................. ..................... 3
One Page ofCongestion Problems ............ ......... ..................................... ....... ....................... 5
More Detail About Congestion Problems .............................................. ................................... 8
The Trouble With Planning Your Trip ..... —...... —...................................... ........................... 1O
The Future ofCongestion ......................... ................................................. ............................. 11
Congestion Relief —AnOverview mfthe Strategies ... .... ................... ----......................... 2
Analysis Using the Best Congestion Data & Analysis Methodologies ......... —...... .................. 14
National Performance Measurement ..... ........... ................................. —............................... 15
ConcludingThoughto_.................. —........ .................... ......................... ............................ 7
References...................................... .............. —........... ......................... ......... .................... 3Q
2015 Urban Mobility Scorecard iii
The national congestion recession isover. Urban areas oƒmYsizes ore experiencing the challenges seen
in the early 2000s — population, jobs and therefore congestion are increasing. The L1Ieconomy has
regained nearly o8qfthe 9million jobs lost during the recession and the total congestion problem is
larger than the pre -recession levels. For the report and congestion data on your city, see:
The data from 1982 to 2014 (see Exhibit 1) show that, short ofmajor economic problems, congestion
will continue toincrease ifprojects, programs and policies are not expanded.
• The problem isvery large. |n2O14,congestion caused urban Americans totravel anextra 6.9billion
hours and purchase anextra 11billion gallons offuel for acongestion cost nf$16Obillion. Trucks
account for $28 billion (17 percent) of that cost, much more than their 7 percent of traffic.
w From 2013 to 2014, 95 of America's 100 largest metro areas saw increased traffic congestion, from
ZOl2tm2013only 61cities experienced increases.
w In orderto reliably arrive on time for important freeway trips, travelers had to allow 48 minutes to
make a trip that takes 20 minutes in light traffic.
• Employment was up by more than 500,000 jobs from 2013 to 2014 (1); if transportation investment
continues tolag, congestion will get worse. Exhibit 2shows the historical national congestion trend.
• More detailed speed data on more roads and more hours of the day from INRIX (2) a leading private
sector provider of travel time information for travelers and shippers, have caused congestion
estimates in most urban areas to be higher than in previous Urban Mobility Scorecords.
The best mobility improvement programs involve a mix of strategies — adding capacity of all kinds,
operating the system to get the 'best bang for the buck,' travel and work schedule options and
encouraging homes and jobs 1obecloser. This involves everyone agencies, businesses, manufacturers,
commuters and travelers. Each region should use the combination ofstrategies that match its gods
and vision. The recovery from economic recession has proven that the problem will not solve itself.
Exhibit 1,Major Findings ofthe 2015Urban Mobility Scorecard (471U.S.Urban Areas)
(Note: See page 2for description ufchanges since the ZOI2report)
Measures of...
1982
2000
2010
2013
2014
... Individual Congestion
Yearly delay per auto commuter (hours)
18
37
40
42
42
Travel Time Index
1.09
1.19
1.20
1.21
1.22
Planning Time Index (Freeway only)
2.41
"Wasted" fuel per auto commuter (gallons)
4
15
15
19
19
Congestion cost per auto commuter (2014 $)
$400
$810
$930
$950
$960
... The Nation's Congestion Problem
Travel delay (billion hours)
1.8
5.2
6.4
6.8
6.9
"Wasted" fuel (billion gallons)
0.5
2.1
2.5
3.1
3.1
Truck congestion cost (billions of 2014 dollars)
$28
Congestion cost (billions of 2014 dollars)
$42
$114
$149
$156
$160
,eanvdelavpe,amucommmer—rhe extra time spent during the year traveling at congested speeds rather than free-flow
speeds by private vehicle drivers and passengers who typically travel in the peak periods.
Travel Time Index (Tn}—The ratio oftravel time inthe peak period totravel time at free-flow conditions. ATravel Time
Index of 1.30 indicates a 20 -minute free-flow trip takes 26 minutes in the peak period.
Planning Time Index (PTI) — The ratio of travel time on the worst day of the month to travel time in free-flow conditions.
Wasted fuel — Extra fuel consumed during congested travel.
Congestion cost — The yearly value of delay time and wasted fuel by all vehicles.
Truck congestion cost The yearly value ofoperating timeand wasted fuel for commercial trucks.
Notes:
See Exhibit 1for explanation ofmeasures.
For more congestion information and for congestion information onyour city,
see Tables 1to4 and
2015 Urban Mobility Scorecard 2
Delay Per
Total Cost
Travel Time
Commuter
Total Delay
Fuel Wasted
(Billions of
Year
d
2014
1.22
42
6.9
3.1
$160
2013
1.21
42
68
3.1
$156
I012
1.21
41
GJ
3.0
$154
2011
1.21
41
6.6
2.5
$15I
2010
120
40
6.4
2.5
$149
2009
1.28
40
6.3
2.4
$147
2008
L21
42
66
2.4
$152
I007
1.21
42
6.6
2.8
$154
2006
1.21
42
6.4
2.8
$149
2005
1.21
41
6.3
2.7
$143
2004
1.21
41
6.1
2.6
$136
2003
1.20
40
5.9
2.4
$128
2002
1.20
39
5.6
2.3
$124
3001
1.19
38
5.3
2.2
$113
2000
1.19
37
5.2
2.1
$114
1999
1.18
36
4.9
IO
$106
1998
1.18
35
4.7
1.8
$101
1997
1.17
34
4.5
17
$97
1996
1.17
32
4.2
1.6
$93
1895
1.16
31
4.0
1.5
$87
1994
1.15
30
3.8
1.4
$82
1893
1.15
39
5.6
1.4
$77
1992
1.14
28
3.4
1.3
$73
1991
1.14
27
3.2
1.2
$69
1990
L13
26
IO
1.2
$65
1989
1.13
75
2.8
1.1
$62
1988
1.12
34
27
1.0
$58
1987
1.12
23
3.5
0.9
$56
1986
1.11
22
2.4
0.8
$52
1905
L11
21
23
07
$51
1984
1.10
ZO
2.1
0.6
$48
1983
1.10
19
2.8
0.5
$45
1983
1.09
18
1.8
0.5
$42
Notes:
See Exhibit 1for explanation ofmeasures.
For more congestion information and for congestion information onyour city,
see Tables 1to4 and
2015 Urban Mobility Scorecard 2
11111"IFiFill 11111111111
The 2015 Urban Mobility Scorecard is the 4 1h that TTI and INRIX (2) have prepared. The data behind the
2015 Urban Mobility Scorecard are hundreds of speed data points on almost every mile of major road in
urban America for almost every 15 --minute period of the average day of the week. For the congestion
analyst, this means 900 million speeds on 1.3 million miles of U.S. streets and highways — an awesome
amount of information. For the policy analyst and transportation planner, this means congestion
problems can be described in detail, and solutions can be targeted with much greater specificity and
accuracy.
Key aspects mfthe 2O25Urban Mobility Scorecordare summarized below.
• Congestion estimates are presented for each of the 471 U.S. urban areas. Improvements inthe
INRIX traffic speed data and the data provided by the states to the Federal Highway Administration
(3) means that for the first time the Urban Mobility Scorecord can provide an estimate of the
congestion effects on residents of every urban area. See Table 4for a few 2014 congestion
measures in each of the 370 urban areas that have not been intensively studied.
w Speeds collected by INRIX every 15 minutes from a variety of sources every day of the year on
almost every major road are used in the study. The data for all 96 15 -minute periods of the day
makes it possible to track congestion problems for the midday, overnight and weekend time
periods. For more information about |NR|X,goto
• This data improvement created significant difference in congestion estimates compared with past
Reports/Scorecards— more congestion overall, a higher percentage of congestion on streets and
different congestion estimates for many urban areas. As has been our practice, past measure values
were revised to provide our best estimate ofcongestion trends.
w More detail isprovided ontruck travel and congestion. Estimates oftruck volume during the day
were developed (in past reports, trucks were assumed to have the same patterns as cars travel).
This changed delay and fuel estimates in different ways for several cities.
m The measure of the variation in travel time from day-to-day now uses a more representative trip -
based process (4) rather than the old dataset that used individual road links. The Planning Time
Index (PTI) is based on the idea that travelers want to be on-time for an important trip 19 out of 20
times; so one would be late towork only one day per month (on-time for 19 out ofZOwork days
each month). For example, a PTI value of1.8Q indicates that atraveler should allow 36 minutes to
make onimportant trip that takes Z0minutes inlow traffic volumes. The new values are lower, and
closer to real-world experience.
w Many of the slow speeds that were formerly considered 'too slow to be a valid observation' are now
being retained in the |NR|Xdataset. Experience and increased travel speed sample sizes have
increased the confidence inthe data.
m Where speed estimates are required, the estimation process is benefitting from the increased
number ofspeeds inthe dataset The methodology isdescribed onthe mobility study website (5).
More information on the performance measures and data can be found at:
2015 Urban Mobility Scorecard 3
|nthe biggest regions and most congested corridors, traffic j a ms can occur at any hour, weekdays or
weekends. The problems that travelers and shippers face include extra travel time, extra cost from
wasted fuel and lost productivity and increasing unreliability where bad weather, roadwork, a
malfunctioning traffic signal, a local event or a small accident or stalled vehicle can result in major
delays. Some key measures are listed below. See data for your city at
htti3://mobilitv.tamu.edu/umsZcongestion data.
Congestion costs are increasing. The congestion "invoice"for the cost nfextra time and fuel inthe 471
U,S.urban areas was (all values inconstant 2O14doUao):
• |o2O14—$1GObillion
* |n2O0O—$114billion
m |n1982— $42 billion
Congestion wastes amassive amount of time, fuel and money. In 2014:
�
6.9 billion hours of extra time (more than the time it would take to drive to Pluto and back, if there
was road).
3.1 billion gallons of wasted fuel (more than 90 minutes worth of flow in the Missouri River).
... and if all that isn't bad enough, folks making important trips had to plan for nearly 2 Y2 times as
much travel time as in light traffic conditions in order to account for the effects of unexpected
crashes, bad weather, special events and other irregular congestion causes.
Congestion isalso atype of tax
m $160 billion of delay and fuel cost (the negative effect of uncertain or longer delivery times, missed
meetings, business relocations and other congestion -related effects are not included) (equivalent to
the lost productivity, clinic visit and medication costs for 53 million cases of poison ivy).
* 18 percent ($28 billion) of the delay cost was the effect of congestion on truck operations; this does
not include any value for the goods being transported inthe trucks.
0 The cost to the average auto commuter was $960 in 2014 compared to an inflation-adjusted $400 in
1982.
Congestion affects people who travel during the peak period. The average auto commuter:
m Spent an extra 42 hours traveling in I014 up from 18 hours in 1982.
w Wasted 19 gallons of fuel in 2014 — a week's worth of fuel for the average U.S. driver up from 4
gallons in19OZ
• |nareas with over one million persons, 2O14auto commuters experienced:
o anaverage ofG3hours ofextra travel time
o aroad network that was congested for 6hours ofthe average weekday
o had ocongestion tax of$1,440
Congestion isalso aproblem atother hours.
m Approximately 41 percent of total delay occurs in the midday and overnight (outside of the peak
hours) times ofday when travelers and shippers expect free-flow travel.
w Many manufacturing processes depend on a free-flow trip for efficient production and congested
networks interfere with those operations.
2015 Urban Mobility Scorecard 5
Congestion, by every measure, has increased substantially over the 33 years covered in this report. And
almost every area has "recovered" from the economic recession; almost all regions have worse
congestion than before the 2008 crash. Traffic problems as measured by per -commuter measures are
about the same as a decade ago, but because there are so many more commuters, and more congestion
during off-peak hours, total delay has increased by almost one billion hours. The total congestion cost
has also risen with more wasted hours, greater fuel consumption and more trucks stuck in stop -and -go
traffic.
Immediate solutions and long-term plans are needed to reduce undesirable congestion. The recession
reduced construction costs, or at least slowed their growth. Urban areas and states can still take
advantage of this situation — but each area must craft a set of programs, policies and projects that are
supported by their communities. This mix will be different in every city, but all of them can be informed
by data and trend information.
Congestion is worse in areas of every size — it is not just a big city problem. The growing delays also hit
residents of smaller cities (Exhibit 3). Big towns and small cities have congestion problems — every
economy is different and smaller regions often count on good mobility as a quality -of -life aspect that
allows them to compete with larger, more economically diverse regions. As the national economy
improves, it is important to develop the consensus on action steps -- major projects, programs and
funding efforts take 10 to 15 years to develop.
0
0
0
0
0
20
10
0
1982 _ 2000 2010 2014
Small Medium Large Very Large
Population Group
Small = less than 500,000 Large = 1 million to 3 million
Medium = 500,000 to 1 million Very Large = more than 3 million
2015 Urban Mobility Scorecard 6
Congestion Patterns
• Congestion builds through the week from Monday toFriday. The two weekend days have less
delay than any weekday (Exhibit 4).
w Congestion is worse in the evening, but it can be a problem during any daylight hour (Exhibit 5).
w Midday hours comprise asignificant share ofthe congestion problem.
Mon Tue Wed Thu Fri Sat Sun
GA Noon 6P Mid
Congestion on Freeways and Streets
= Streets have more delay than freeways, but there are also many more miles of streets (Exhibit 6).
w Approximately 4Opercent ofdelay occurs inoff-peak hours.
* Freeway delay is much less of the problem in areas under 1 million population.
Exhibit 6. percent ofDe|aV-Road Type and Time of Day
Peak
Freeways
1M Population
0
2015 Urban Mobility Scorecard 7
Rush Hour Congestion
• Severe and extreme congestion levels affected only 1in9trips in1982,but 1in4trips in2Q14.
* The most congested sections of road account for 8Q%of peak period delays, but only have 26% of
the travel (Exhibit 7).
severe conRestion.....
... but those worst trips
travel time.
Truck Congestion
* Trucks account for lApercent ofthe urban "congestion invoice"althoughtheyonk/nepresent7
percent ofurban travel (Exhibit 8).
• The costs in Exhibit do not include the extra costs borne by private companies who build
additional distribution centers, buy more trucks and build more satellite office centers to allow them
to overcome the problems caused by a congested and inefficient transportation network.
Exhibit 8. 2014 Congestion Cost for Urban Passenger and Freight Vehicles
Travel by Vehicle Type Congestion Cost by Vehicle Type
2015 Urban Mobility Scorecard 8
m American motorists are enduring about 5percent more delay than the pre -recession peakin2OO7.
(Exhibit 2)
w While this is associated with a "good thing" -- economic and population growth in our major metro
areas — it is also clear this growth is outpacing the investment in infrastructure and programs to
address the increased demand mnthe network.
* Cities with employment and population growth faster than the national averages also experienced
some ofthe biggest increases intraffic congestion.
0 Cities that showed little to no change in traffic congestion were also those where employment and
population growth was slower than the national average
m 53 of the 101. urban areas saw the total urban area delay exceed the pre -recession levels within 3
years; an immediate 'snapback' was seen in more than one-quarter of the studied regions.
0 2Jareas still have lower total annual delay than in2OO7/0.(Exhibit 9)
0 In contrast to total delay, average auto commuter delay is still less than pre -recession levels in 60
areas
w Commuters in16areas saw the 'rapid snapback' hours per commuter exceeding the ZOO7/8values
in 3 or fewer years. (Exhibit 8)
Total Urban Area Delay
2015 Urban Mobility Scorecard 9
The Trouble With Planning Your Trip
We've all made urgent trips—catching an airplane, getting to a medical appointment, or picking up a
child atdaycare ontime. We know we need to leave a little early to make sure we are not late for these
important trips, and we understand that these trips will take longer during the "rush hour." The need to
add extra time isn't just o"rush hour" consideration. Trips during the off-peak can also take longer than
expected. If we have to catch an airplane at 1 p.m., we might still be inclined to add a little extra time,
and the data indicate that our intuition is correct.
Exhibit 1Oillustrates this problem. Say your typical trip takes 2Ominutes when there are few other cars
onthe road. That is represented by the green bar across the morning, midday, and evening. Your trip
usually takes longer, on average, whether that trip is in the morning, midday, or evening. This "average
trip time" is shown in the solid yellow bar in Exhibit 10 — in 2014 the average big city auto commute was
25minutes \nthe morning and 27minutes inthe evening peak.
Now, if you have to make a very important trip during any of these time periods there is additional
"planning time" you must allow toreliably arrive on-time. And, asshown inExhibit 1Q(red bar),bisn't
just a "rush hour" problem — it can happen any time of the day and amounts to an extra 29 minutes in
the morning, 35 minutes in the evening and even 14 minutes for your 20 -minute trip in the midday. The
news isn't much better for those planning trips in areas with fewer than 1 million people — 14 and 18
minutes longer in the morning and evening peaks. Data for individual urban areas is presented in Table 3
(in the back of the report).
70
60
50
40
30
20
10
0
Exhibit 10. How Much Extra Time Should You Allow to Be'On-Time'?
11 Plamm|ngTime
Areas with More Than Average Time Areas with Less Than
1Million Population 1Million Population
ra Low -Volume Time
Morning Midday Evening
Moming Midday Evening
2015 Urban Mobility Scorecard 10
Before the economic recession, congestion was increasing at between 2 and 4 percent every year —
which meant that extra travel time for the average commuter increased slightly less than I hour every
year. The economic recession set back that trend a few years, but the trend in the last few years
indicates congestion isrising again. Congestion isthe result ufanimbalance between travel demand
and the supply of transportation capacity — whether that is freeway lanes, bus seats or rail cars. As the
number of residents or jobs goes up in an improving economy, or the miles or trips that those people
make increases, the road and transit systems a|soneedto,insomecombination,eitherexpandnr
operate more efficiently. As the rising congestion levels in this report demonstrate, however, this is an
infrequent occurrence. Travelers are not only paying the price for this inadequate response, but traffic
congestion can also become a drain on further economic growth.
Asone estimate ofcongestion inthenearfutune,thisreportusestheexpectedpopu|ationgrowthand
congestion trends from the period of sustained economic growth between 2000 and 2005 to get an idea
of what the next five years might hold, The basic input and analysis features:
The combined role of the government and private sector will yield approximately the same rate of
transportation system expansion (both roadway and public transportation). The analysis assumes
that policies and funding levels will remain about the same.
The growth in usage of any of the alternatives (biking, walking, work or shop at home) will continue
atthe same rate.
The period before the economic recession (from 2000 to 2005) was used as the indicator of the
effect of growth. These years had generally steady economic growth in most U,S. urban regions;
these years are assumed to be the best indicator of the future level of investment in solutions and
the resulting increase in congestion for each urban area.
The congestion estimateforanyoinO|eregionvviUbeaffec1edbythefunding,pnojec1se|ecbonsand
operational strategies; the simplified estimation procedure used in this report did not capture these
variations. Using this simplified approach the following offers an idea of the national congestion
problem in ZOZO.
* The national congestion cos will grow from $16Obillion to$192billion inZO2O(in 2O14doUao).
• Delay will grow to 8.3 billion hours in 2020.
m Wasted fuel will increase to 3.8 billion gallons in 2020i
• The average commuter's congestion cost will grow to$1,1OOin7O2O(in ZO14doUars).
• The average commuter will waste 47hours and 21gallons in2O2O.
2015 Urban Mobility SCoRecB/I/ 11
We recommend a balanced and diversified approach to reduce congestion — one that focuses on more
of everything; more policies, programs, projects, flexibility, options and understanding. |tisclear that
our current investment levels have not kept pace with the problems. Most urban regions have big
problems now — more congestion, poorer pavement and bridge conditions and less public
transportation service than they would like.
There will be a different Mix Of solutions in metro regions, cities, neighborhoods, job centers and
shopping areas. Some areas might be more amenable to construction solutions, other areas might use
more technology to promote and facilitate travel options, operational improvements, or land use
redevelopment. In all cases, the solutions need to work together to provide an interconnected network
ofsmart transportation services aswell asimprove the qua|iiy+of-|ife.
There will also bearange mfcongestion targets. Many large urban areas, for example, use atarget
speed of 35 mph or 45 mph for their freeways; if speeds are above that level, there is not a 'congestion
problem.' Smaller metro areas, however, typically decide that good mobility is one part of their quality -
of -life goals, and have higher speed expectations. Even within ametro region, the congestion target will
typically be different between downtown and the remote suburbs, different for freeways and streets,
and different for rush hours than midday travel.
The level ofcongestion deemed unacceptable is alocal decision. The Urban Mobility Scorecard uses
one consistent, easily understood comparison level. But that level isnot 'the goa|,'itisonly an
expression nfthe problem, The Scorecard isonly one ofmany pieces ofinformation that should be
considered when determining how much ofthe problem tosolve.
Better data can play a valuable role in all of the analyses. Advancements in volume collection, travel
speed data and origin to destination travel paths for people and freight allow transportation agencies at
all government levels and the private sector to better identify existing chokepoints, possible alternatives
and growth patterns. The solution begins with better understanding ofthe challenges, problems,
possibilities and opportunities — where, when, how and how often mobility problems occur — and moves
into similar questions about Solutions — where, when, how can mobility be improved. These data will
allow travelers to capitalize on new transportation services, identify novel programs, have better travel
time reliability and improve their access to information.
More information on the possible solutions, places they have been implemented and the effects
estimated inthis report can befound onthe website None ofthese
ideas are the whole mobility solution, but they can all play arole.
Get as much service as possible from what we have — Many low-cost improvements have broad
public support and can berapidly deployed. These operations programs require innovation, new
monitoring technologies and staffing plans, constant attention and adjustment, but they pay
dividends in faster, safer and more reliable travel. Rapidly removing crashed vehicles, timing the
traffic signals so that more vehicles see green lights, and improving road and intersection designs
are relatively simple actions. More complex changes such astraffic signals that rapidly adapt to
different traffic patterns, systems that smooth traffic flow and reduce traffic collisions and
2015 Urban Mobility SCOn9C@/d 12
communication technologies that assist travelers (in all modes) and the transportation network in
achieving goals are also apart ofthe 'get the best bang for the buck' approach.
• A6dcapadtyinchtice|con.idors—HandUn8morefrekgh1orpersontmve|nnheeways,stneets,/ai|
lines, buses orintermodal facilities often requires "more." important corridors orgrowing regions
can benefit from more street and highway lanes, new or expanded public transportation facilities,
and larger bus and rail fleets. Some of the "more" will also be in the form of advancements in
connected and autonmmousvehides—cans,trucks, buses and trains that communicate with each
other and with the transportation network — that will reduce crashes and congestion.
w Provide choices This might involve different travel routes, travel modes or lanes that involve a toll
for high-speed and reliable service. These options allow travelers and shippers tocustomize their
travel plans. There is much more transportation information available on websites, smartphones
and apps, radio, TV and in their car or at their transit stop; the information involves displays of
existing travel times, locations of roadwork or crashes, transit ridership and arrival information and
avariety oftrip planner resources. They allow travelers tomake real-time decisions about when to
depart on a trip, what route or mode to take, whether they are interested in paying a toll in order to
guarantee an arrival time or perhaps just sleep in for a while and telecommute on a particularly bad
day. In the past, this information was more difficult to find, tough to understand or was not
updated very frequently. Today's commuters have much better information, delivered when and
where its needed inaformat they can use tomake decisions
• Change the usage patterns — There are solutions that involve changes in the way employers and
travelers conduct business to avoid traveling in the traditional "rush hours." Flexible work hours,
internet connections or phones allow employees to choose work schedules that meet family needs
and the needs oftheir jobs. These are not typically agency -led oragency-directed strategies —they
are workers and managers getting together to identify virtuous combinations of work hours,
commute modes, office space arrangements and electronic communication mechanisms.
Companies have seen productivity increase when workers are able to adjust their hours and
commute trips to meet family orother obligations. Those companies also save on parking space and
office requirements and see less staff turnover and, therefore, lower recruiting and training costs.
m Diversify the development patterns — These typically involve denser developments with a mix of
jobs, shops and homes, so that more people can walk, bike or take transit to more, and closer,
destinations. Sustaining the quality -of -life and gaining economic development without the typical
increment of congestion in each of these sub -regions appears to be part, but not all, of the mobility
solution. Analytical advancements in fields of transportation, land development, education and
other information sources mean that home purchasers have much more information about their
commute options and the expectations they should have. Arange ofhome types, locations and
prices when matched with more information about, for example, historic travel times, elementary
and secondary education quality, entertainment and cultural sites provides the type of information
that consumers want.
w Realistic expectations are also part ofthe solution. Large urban areas will becongested. Some
locations near key activity centers in smaller urban areas will also be congested. Identifying
solutions and funding sources that meet a variety of community goals is challenging enough without
attempting to eliminate congestion in all locations at all times. Congestion does not have to be an
all -day event, and in many cases improving travel time awareness and predictability can be a
positive first step towards improving urban mobility.
Case studies, analytical methods and data are available to support development of these strategies and
monitor the effectiveness of deployments. There are also many good state and regional mobility reports
that provide ideas for communicating the findings ofthe data analysis.
2015 Urban Mobility Scorecard 13
Analysis Using the Best Congestion Data
& Analysis Methodologies
The base data fort he 2015 Urban Mobility Scorecardcamefrom IN RIX, the U.S. Department of
Transportation and the states (Z3). Several analytical processes were used todevelop the final
measures, but the biggest improvement in the last two decades is provided by the INRIX data. The
speed data covering most travel on most major roads in U.S. urban regions eliminates the difficult
process of estimating speeds and dramatically improves the accuracy and level of understanding about
the congestion problems facing US travelers.
The methodology is described in a technical report (5) that is posted on the mobility report website:
* The |NR|Xtraffic speeds are collected from a variety ofsources and compiled in their Historical
Profile database. Commercial vehicles, smart phones and connected cars with location devices feed
time and location data points to|NR|X.
m The proprietary process filters inappropriate data (e.g., pedestrians walking next to a street) and
compiles a clataset of average speeds for each road segment. TTI was provided a clataset of 15 -
minute average speeds for each link ofmajor roadway covered in the Historical Profile database
(approximately 13million miles in2O14).
m Traffic volume estimates were developed with a set of procedures developed from computer
models and studies ofreal-world travel time and volume data. The congestion methodology uses
daily traffic volume converted to 15 -minute volumes using a national traffic count dataset (6).
w The 15 -minute INRIX speeds were matched to the 15 -minute volume estimates for each road
section onthe FHVVAmaps.
w An estimation procedure was also developed for the sections of road that did not have INRIX data.
As described in the methodology website, the road sections were ranked according to volume per
lane and then matched with a similar list of sections with INRIX and volume per lane data (as
developed from the FHWA clataset) (5).
2015 Urban Mobflity Scorecard 14
"What Gets Measured, Gets Done"
Many of us have heard this saying, and it isvery appropriate when discussing transportation system
performance measurement. Performance measurement a1the national level isgaining momentum.
Many state and local transportation agencies are implementing performance measurement activities to
operate their systems asefficiently aspossible with limited resources.
The Moving Ahead for Progress inthe JI*Century Act (K1AP-21)was signed into law onJuly 6,2O12to
fund surface transportation. Among other aspects, MAP -21 establishes performance-based planning and
programming to improve transportation decision-making and increase the accountability and
transparency ofthe Federal highway funding program (7).
As part of thetransition to a performance and outcome -based Federal highway funding program, MAP -
21 establishes national performance goals in the following areas (7):
• Safety
• Infrastructure condition
w Congestion reduction
• System reliability
• Freight movement and economic vitality
* Environmental sustainability
• Reduced project delivery delays
MAP -21 requirements provide the opportunity to improve agency operations. While transportation
professionals will calculate the required MAP -21 performance measures, there is also an opportunity to
develop processes and other measures tobetter understand their systems. The requirements of MAP -
21 are specified through a Rulemaking process. At the time of this writing, the Notice of Proposed
Rulemaking (NPRK4)for system performance measures (congestion, reliability) has not been released by
the United States Department ofTransportation (USDOT).
While the specific requirements of MAP -21 related to system performance measures are not yet known,
the data, measures, and methods in the Urban Mobility Scorecord provide transportation professionals
with a 33 -year trend of foundational knowledge to inform performance measurement and target setting
at the urban area level. The measures and techniques have stood the test of time to communicate
mobility conditions and potential solutions.
"Don't Let Perfect be the Enemy of Good"
Occasionally there is reluctance at transportation agencies to dive in and begin performance
measurement activities because there is a concern that the data or methods are just not good enough.
Over the years, the Urban Mobility Report (and now the Scorecord) has taken advantage of data
improvements — and associated changes in analysis methods — and the use of more powerful
computational methods (for example, geographic information systems). Such adaptations are typical
when conducting on-going performance reporting. As the successful 33 -year data trend of UMRIUMS
suggests, changes can be made as improvements become available. The key is to get started!
2015 Urban Mobility Scorecard 15
The national economy has improved since the last Urban Mobility fcorecord and unfortunately
congestion has gotten worse. This has been the case in the past, and it appears that the economy -
congestion linkage is as dependable as gravity. Some analysts had touted the decline indriving per
capita and dip in congestion levels as a sign that traffic congestion would, in essence, fix itself. That is
not happening.
The other seemingly dependable trend — not enough of any solution being deployed — also appears to
beholding inmost growing regions. That isreally the lesson from this series cfreports. The mix of
solutions that are used is relatively less important than the amount of solution being implemented. All
of the potential congestion -reducing strategies should be considered, and there is a role and location for
most ofthe strategies.
w Getting more productivity out of the existing road and public transportation systems is vital to
reducing congestion and improving travel time reliability.
* Businesses and employees can use avariety ofstrategies tomodify their work schedules,
traveling times and travel modes to avoid the peak periods, use less vehicle travel and increase
the amount ofelectronic "tmvei"
• in growth corridors, there also may be a role for additional capacity to move people and freight
more rapidly and reliably.
m Some areas are seeing renewed interest in higher density living in neighborhoods with a mix of
residential, office, shopping and other developments. These places can promote shorter trips
that are more amenable to walking, cycling or public transportation modes.
The 2015 Urban Mobility Scorecord points to national measures of the congestion problem for the 471
urban areas in2O14:
* $16Obillion ofwasted time and fuel
• Including $28billion ofextra truck operating time and fuel
• Anextra 69billion hours oftravel and 3.1billion gallons offuel consumed
The average urban commuter in2Ol4:
spent an extra 42 hours of travel time on roads than if the travel was done in low-volume
conditions
used 19extra gallons offuel
which amounted toanaverage value of$86Oper commuter
Traffic congestion has grown since the low point in 2009 during the economic recession. An additional
600 million hours and 700 million gallons of fuel were consumed in 2014 than in 2009. Congestion, in
terms of average extra hours and gallons of fuel consumed by the average commuter, has not returned
to pre -recession levels in 60 of the 101 urban areas that were intensively studied. But there have been
increases in the extra hours of travel time and gallons those commuters suffer showing that the
economic recession has not been a permanent cure for traffic congestion problems.
States and cities have been addressing theconges ionprob|emstheyfacewithavahetyofrLntegies
and more detailed data analysis. Some of the solution lies in identifying congestion that is undesirable —
that which significantly diminishes the quality of life and economic productivity — and some lies in using
the smart data systems and range of technologies, projects and programs to achieve results and
communicate the effects to assure the public that their project dollars are being spent wisely.
2015 Urban Mobility Scorecard 17
National Congestion Tai ..
Very Large Urban Areas over 3 million population. Medium Urban Areas—over 500,000 and less than 1 million population.
Large Urban Areas—over 1 million and less than 3 million population. Small Urban Areas—less than 500,000 population.
Yearly Delay per Auto Commuter—Extra travel time during the year divided by the number of people who commute in private vehicles in the urban area.
Travel Time Index—The ratio of travel time in the peak period to the travel time at free-flow conditions. A value of 1.30 indicates a 20 -minute free-flow trip takes 26 minutes in the
peak period.
Excess Fuel Consumed—Increased fuel consumption due to travel in congested conditions rather than free-flow conditions.
Congestion Cost—Value of travel time delay (estimated at $17.67 per hour of person travel and $94.04 per hour of truck time) and excess fuel consumption (estimated using state
average cost per gallon for gasoline and diesel).
Note: Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6"` and 12`h. The
actual measure values should also be examined. The best congestion comparisons are made between similar urban areas.
R
Table 1.
What Congestion
Means to You, 2014
Yearly Delay per Auto
Excess Fuel per Auto
Congestion
Cost per
Urban Area
Commuter
Travel Time Index
Commuter
Auto Commuter
Tours
Rank
Value
Rank
Gallons
Rank
Dollars
Rank
Very Large Average (15 areas)
63
1.32
27
1,433
Washington DC -VA -MD
82
1
1.34
8
35
1
1,834
1
Los Angeles -Long Beach -Anaheim CA
80
2
1.43
1
25
11
1,711
3
San Francisca-Oakland CA
78
3
1.41
2
33
3
1,675
4
New York -Newark NY -NJ -CT
74
4
1.34
8
35
1
1,739
2
Boston MA -NH -RI
64
6
1.29
17
30
4
1,388
9
Seattle WA
63
7
1.38
3
28
8
1,491
5
Chicago IL -IN
( 61
8
1 1.31
14
29
5
1,445
7
Houston TX
61
8
1.33
10
1 29
5
1,490
6
Dallas -t=ort Worth -Arlington TX
53
11
1.27
19
1 22
23
1,185
14
Atlanta GA
52
12
1.24
25
20
44
1,130
22
Detroit MI
52
12
124
25
25
11
1,183
15
Miami FL
52
12
1.29
17
24
15
1,169
17
Phoenix -Mesa AZ
51
17
1.27
19
25
11
1,201
13
Philadelphia PA -NJ -DE -MD
48
22
1.24
25
23
18
1,112
26
San Diego CA
42
41___i
1.24
25
11
92
887
61
Very Large Urban Areas over 3 million population. Medium Urban Areas—over 500,000 and less than 1 million population.
Large Urban Areas—over 1 million and less than 3 million population. Small Urban Areas—less than 500,000 population.
Yearly Delay per Auto Commuter—Extra travel time during the year divided by the number of people who commute in private vehicles in the urban area.
Travel Time Index—The ratio of travel time in the peak period to the travel time at free-flow conditions. A value of 1.30 indicates a 20 -minute free-flow trip takes 26 minutes in the
peak period.
Excess Fuel Consumed—Increased fuel consumption due to travel in congested conditions rather than free-flow conditions.
Congestion Cost—Value of travel time delay (estimated at $17.67 per hour of person travel and $94.04 per hour of truck time) and excess fuel consumption (estimated using state
average cost per gallon for gasoline and diesel).
Note: Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6"` and 12`h. The
actual measure values should also be examined. The best congestion comparisons are made between similar urban areas.
R
N
Large Average (31 areas)
San Jose CA
Riverside -San Bernardino CA
Austin TX
Portland OR -WA
Denver -Aurora CO
Oklahoma City OK
Baltimore MD
Minneapolis -St. Paul MN
Las Vegas -Henderson NV
Orlando FL
Nashville -Davidson TN
Virginia Beach VA
San Antonio TX
Charlotte NC -SC
Indianapolis IN
Louisville -Jefferson County KY -IN
Memphis TN -MS -AR
Providence RI -MA
Sacramento CA
St. Louis MO -IL
San Juan PR
Cincinnati OH -KY -IN
Columbus OH
Tampa -St. Petersburg FL
Kansas City MO -KS
Pittsburgh PA
Cleveland OH
Jacksonville FL
Milwaukee WI
Salt Lake City -West Valley City UT
Richmond VA
Yearly Delay per Auto
Commuter
Travel Time Index
Excess Fuel per Auto
Commuter
Congestion Cost per
Auto Commuter
Hours
Rank
Value
Rank
gallons
Rank
Dollars
Rank
45
1.23
21
$1,045
67
5
1.38
3
28
8
1,422
8
59
10
1.33
10
18
62
1,316
10
52
12
1.33
10
22
23
1,159
20
52
12
1.35
7
29
5
1,273
11
49
19
1 1.30
16
24
15
1,101
28
49
19
1 1.19
42
23
18
1,110
27
47
23
1.26
21
j 21
32
1,115
25
47
23
1.26
21
18
62
1,035
36
46
27
1.26
21
21
32
984
42
46
27
1.21
34
21
32
1,044
34
45
29
1.21
34
22
23
1,168
18
45
29
1.19
42
19
51
953
46
44
33
1.25
24
20
44
1,002
38
43
35
1.23
29
17
70
963
44
43
35
1.18
46
23
18
1,060
30
43
35
1.20
37
22
23
1,048
32
43
35
1.19
42
21
32
j 1,080
29
43
35
1.20
37
21
32
951
47
43
35
1.23
29
19
51
958
45
43
35
1.16
65
21
32
1,020
37
43
35
1.31
14
24
15
1,150
21
41
45
1.18
46
21
32
989
40
41
45
1.18
46
20
44
933
49
41
45
1.21
34
18
62
907
57
39
51
1A5
76
18
62
933
49
39
51
1.19
42
21
32
889
59
38
55
1.15
76
22
23
887
61
38
55
1.18
46
15
78
842
72
38
55
1.17
54
22
23
987
41
37
66
1.18
46
22
23
1,059
31
34
77
1.13
88
14
84
729
82
Large Urban Areas -over T million and less than 3 million population.
Yearly Delay per Auto Commuter -Extra travel time during the year divided by the number of people who commute in private vehicles in the urban area.
Travel Time Index -The ratio of travel time in the peak period to the travel time at free-flow conditions. A value of 1.30 indicates a 20 -minute free-flow trip takes 26 minutes in the
peak period.
Excess Fuel Consumed -increased fuel consumption due to travel in congested conditions rather than free-flow conditions.
Congestion Cost -Value of travel time delay (estimated at $17.67 per hour of person travel and $94.04 per hour of truck time) and excess fuel consumption (estimated using state
average cost per gallon for gasoline and diesel).
Note: Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6'h and 12`h. The
actual measure values should also be examined. The best congestion comparisons are made between similar urban areas.
Medium Urban Areas -over 500,000 and less than I million population.
Yearly Delay per Auto Commuter -Extra travel time during the year divided by the number of people who commute in private vehicles in the urban area.
Travel Time lndox-A value of 1.30 indicates a 20 -minute free-flow trip takes 26 minutes in the peak period.
Excess Fuel Consumed -increased fuel consumption due to travel in congested conditions rather than free-flow conditions.
Congestion Cost -Value of travel time delay and excess fuel consumption (estimated using state average cost per gallon for gasoline and diesel). 1h th
Note: Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6 and 12`". The
0 actual measure values should also be examined. The best congestion comparisons are made between similar urban areas.
Table
1. What Congestion Means to YOU, ZU14, L;ontmueu
Yearly Delay per Auto
I
Excess Fuel per Auto
Congestion Cost per
Urban Area
Commuter
Travel Time Index
Commuter
Auto Commuter
Hours
Rank
Value
Rank
Gallons
Rank
dollars
Rank -
Medium Average (33 areas)
37
1.18
18
$870
Honolulu Hl
50
18
1.37
5
26
10
1:125
24
Bridgeport -Stamford CT -NY
49
19
1.36
6
22
23
1,174
16
Baton Rouge LA
47
23
1.22
32
25
11
1,262
12
Tucson AZ
47
23
1.22
32
23
18
1,128
23
Hartford CT
45
29
120
37
21
32
1,038
35
New Orleans LA
45
29
1.32
13
22
23
1,161
19
Tulsa OK
44
33
1,17
54
20
44
984
42
Albany NY
42
43
1.17
54
21
32
991
39
Charleston -North Charleston SC
41
45
1.23
29
20
44
1,047
33
Buffalo NY
40
49
1.17
54
21
32
918
53
New Haven CT
40
49
1.16
65
19
51
932
51
Grand Rapids Ml
39
51
1.17
54
19
51
854
68
Rochester NY
39
51
1.16
65
20
44
889
59
Columbia SC
38
55
1.15
76
19
51
951
47
Springfield MA -CT
38
55
1.14
81
19
51
831
75
Toledo OH -Ml
38
55
1.18
46
20
44
920
52
Albuquerque NM
36
70
1.16
65
19
51
886
63
Colorado Springs CO
35
72
1.16
65
17
70
772
78
Knoxville TN
35
72
1.14
81
17
70
849
70
Wichita KS
35
72
1.17
54
18
62
837
73
Birmingham AL
34
77
1.14
81
16
75
891
51
Raleigh NC
34
77
1.17
54
13
86
734
81
El Paso TX -NM
33
81
1.16
65
16
75
760
79
Omaha NE -IA
32
83
1.16
65
17
70
707
84
Allentown PA -NJ
30
86
1A 7
54
15
78
1 694
87
Cape Coral FL
30
86
1.17
54
13
86
669
88
McAllen TX
30
86
1.15
76
13
86
649
89
Akron OH
27
89
1.12
91
15
78
634
90
Sarasota -Bradenton FL
26
90
1.16
65
12
91
589
92
Dayton OH
25
91
1.12
91
13
86
590
91
Fresno CA
23
92
1.11
97
11
92
495
96
Provo -Orem UT
21
94
1.12
91
15
78
708
83
Bakersfield CA
19
96L1.12
91
9
96
512
94
Medium Urban Areas -over 500,000 and less than I million population.
Yearly Delay per Auto Commuter -Extra travel time during the year divided by the number of people who commute in private vehicles in the urban area.
Travel Time lndox-A value of 1.30 indicates a 20 -minute free-flow trip takes 26 minutes in the peak period.
Excess Fuel Consumed -increased fuel consumption due to travel in congested conditions rather than free-flow conditions.
Congestion Cost -Value of travel time delay and excess fuel consumption (estimated using state average cost per gallon for gasoline and diesel). 1h th
Note: Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6 and 12`". The
0 actual measure values should also be examined. The best congestion comparisons are made between similar urban areas.
Small Average (22 areas)
Jackson MS
Little Rock AR
Pensacola FL -AL
Spokane WA
Worcester MA -CT
Anchorage AK
Boise City ID
Poughkeepsie -Newburgh NY -NJ
Madison W1
Boulder CO
Salem OR
Beaumont TX
Eugene OR
Greensboro NC
Corpus Christi TX
Oxnard CA
Brownsville TX
Winston-Salem NC
Laredo TX
Stockton CA
Lancaster -Palmdale CA
Indio -Cathedral City CA
101 Area Average
Remaining Areas Average
All 471 Area Ave[2g2_
e 1. What Congestion Means to You, 2014, Continued
Yearly Delay per Auto Excess Fuel per Auto
Commuter Travel Time Index Commuter
Congestion Cost per
Auto Commuter
Hours
Rank
Value
Rank
Gallons Rank
Dollars
Rank
30
1.14
14
$705
38
55
1.13
88
15
78
878
64
38
55
1.14
81
13
86
853
69
38
55
1.17
54
18
62
849
70
38
55
1.17
54
23
18
911
55
38
55
1.12
91
18
62
865
67
37
66
1.20
37
19
51
913
54
37
66
1.16
65
18
62
j 833
74
37
66
1.12
91
17
70
867
66
36
70
1.18
46
19
51
911
55
35
72
1.20
37
19
51
752
80
35
72
1.16
65
21
32
876
65
34
77
1.15
76
15
78
800
77
33
81
1.18
46
19
51
804
76
32
83
1.10
99
14
84
703
85
31
85
1.13
88
16
75
697
86
23
92
1.14
81
8
97
494
97
21
94
1.14
81
11
92
494
97
19
96
1.11
97
7
98
415
99
18
98
1.16
65
10
95
496
95
18
98
1.14
81
7
98
516
93
17
100
1.10
99
5
100
349
100
6
101
1.05
101
2
101
149
101
52
1.26
23
$1,190
16
1.09
7
$370
42
1.22
19
$960
Very Large Urban Areas -over 3 million population. Medium Urban Areas -over 500,000 and less than 1 million population.
Large Urban Areas -over I million and less than 3 million population. Small Urban Areas -less than 500,000 population.
Yearly Delay per Auto Commuter -Extra travel time during the year divided by the number of people who commute in private vehicles in the urban area.
Travel Time Index -The ratio of travel time in the peak period to the travel time at free-flow conditions. A value of 1.30 indicates a 20 -minute free-flow trip takes 26 minutes in the
peak period.
Excess Fuel Consumed -increased fuel consumption due to travel in congested conditions rather than free-flow conditions,
Congestion Cost -Value of travel time delay (estimated at $17.67 per hour of person travel and $94.04 per hour of truck time) and excess fuel consumption (estimated using state
average cost per gallon for gasoline and diesel). 1h
Note: Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6and 12t'. The
actual measure values should also be examined. The best congestion comparisons are made between similar urban areas.
14
Urban Area
Table
2. What Congestion
Travel Delay
Means to Your Town, 2014
Excess Fuel Consumed
Truck Congestion
Cost
Total Congestion
Cost
(1,000 Hours) Rank
(1,000 Gallons) Rank
($ million) Rank
($ million)
Rank
Very Large Average (15 areas)
231,970
99,490
$885
$5,260
New York -Newark NY -NJ -CT
628,241
1
296,701 1
2,779
1
14,712
1
Los Angeles -Long Beach -Anaheim CA
622,509
2
195,491 2
1,721
2
13,318
2
Chicago IL -IN
302,609
3
147,031 3
1,482
3
7,222
3
Washington DC -VA -MD
204,375
4
88,130 6
710
6
4,560
5
Houston TX
203,173
5
94,300 4
1,118
4
4,924
4
Miami FL
195,946
6
90,320 5
736
5
4,444
6
Dallas -Fort Worth -Arlington TX
186,535
7
79,392 7
702
7
4,202
7
Philadelphia PA -NJ -DE -MD
157,183
8
77,456 8
683
9
3,669
8
Phoenix -Mesa AZ
155,730
9
75,938 9
692
8
3,641
9
Detroit MI
155,358
10
73,645 10
567
11
3,514
10
Boston MA -NH -RI
153,994
11
71,602 11
426
15
3,363
11
Atlanta GA
148,666
12
57,113 14
434
13
3,214
13
San Francisco -Oakland CA
146,013
13
62,320 12
360
18
3,143
14
Seattle WA
139,842
14
62,136 13
645
10
3,294
12
San Die2o CA
79,412
20
20,742 36
192
35
1,658
21
Very Large Urban Areas -over 3 million population.
Medium Urban Areas -over 500,000 and less than 1 million population.
Large Urban Areas -over 1 million and less than
3 million population.
Small Urban Areas -less than
500,000 population.
Travel Delay -Extra travel time during the year.
Excess Fuel Consumed -Value of increased fuel consumption due to travel in
congested conditions rather than free-flow
conditions (using state average cost per gallon).
Truck Congestion Cost -Value of increased travel
time and other operating costs
of large trucks (estimated at $94.04 per
hour of truck time) and
the extra
diesel consumed (using
state average cost per gallon).
Congestion Cost -Value of delay and fuel cost (estimated at $17.67 per hour of person
travel, $94.04 per hour of truck time and state average
fuel cost).
Note:Please do not place too much emphasis on
small differences in the rankings. There may be little difference in congestion between areas ranked (far
example) 6"' and 12t.
The
actual measure values should also be examined.
The best congestion comparisons are
made between similar urban areas.
to
Urban Area
Travel Delay
1 1
Excess Fuel Consumed
Truck Congestion
Cost
Total Congestion
Cost
(1,000 Hours) Rank (1,000 Gallons) Rank
($ millionf-Rank
($ million)
Rank
Large Average (31 areas)
55,390
25,690
$235
$1,280
San Jose CA
104,559
15
43,972
16
240
28
2,230
15
Minneapolis -St. Paul MN
99,710
16
38,542
19
327
20
2,196
17
Riverside -San Bernardino CA
99,058
17
30,732
23
361
17
2,201
16
Denver -Aurora CO
91,479
18
44,922
15
319
21
2,061
19
Baltimore MID
87,620
19
38,661
18
427
14
2,075
18
Portland OR -WA
72,341
21
39,611
17
375
16
1,763
20
Tampa -St. Petersburg FL
j 71,628
22
31,654
22
237
30
1,589
24
St. Louis MO -1L
69,350
23
1 32,991
21
j 328
19
1 1,637
22
San Antonio TX
64,328
24
28,809
25
251
27
1,462
25
Las Vegas -Henderson NV
63,693
25
30,001
24
158
45
1,375
26
San Juan PR
60,301
26
33,418
20
j 437
12
1,605
23
Sacramento CA
60,220
27
26,289
26
189
36
1,334
27
Orlando FL
52,723
28
23,938
31
212
33
1,207
28
Austin TX
51,116
29
21,654
33
182
39
1,140
31
Cincinnati OH -KY -IN
48,485
30
25,086
28
238
29
1,159
29
Virginia Beach VA
48,274
31
20,085
37
112
52
1,020
36
Indianapolis IN
46,435
32
25,066
29
259
26
1,142
30
Oklahoma City OK
45,652
33
21,027
35
166
43
1,030
34
Kansas City MO -KS
45,570
34
21,349
34
226
32
1,085
32
Cleveland OH
45,051
35
25,547
27
182
39
1,046
33
Pittsburgh PA
44,758
36
24,107
30
171
42
1,030
34
Columbus OH
40,025
37
19,870
38
162
44
921
41
Nashville -Davidson TN
38,977
39
19,093
39
285
22
1,013
38
Memphis TN -MS -AR
37,824
40
18,440
42
229
31
939
40
Providence RI -MA
37,809
41
18,853
41
121
49
846
45
Milwaukee WI
37,659
42
21,957
32
266
25
984
39
Louisville -Jefferson County KY -IN
35,622
45
17,841
43
186
38
860
43
Charlotte NC -SC
34,153
46
13,760
50
131
47
770
47
Jacksonville FL
29,680
48
12,063
53
101
57
659
49
Salt Lake City -West Valley City LIT
26,925
51
16,304
46
267
24
779
46
Richmond VA
26,104
53
10,802
55
68
69
558
54
Very Large Urban Areas -over 3 million population. Medium Urban Areas -over 500,000 and less than 1 million population.
Large Urban Areas -over I million and less than 3 million population. Small Urban Areas -less than 500,000 population.
Travel Delay -Extra travel time during the year.
Excess Fuel Consumed -Value of increased fuel consumption due to travel in congested conditions rather than free-flow conditions (using state average cost per gallon).
Truck Congestion Cost -Value of increased travel time and other operating costs of large trucks (estimated at $94.04 per hour of truck time) and the extra diesel consumed (using
state average cost per gallon).
Congestion Cost -Value of delay and fuel cost (estimated at $17.67 per hour of person travel, $94.04 per hour of truck time and state average fuel cost).
NotePlease do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 61h and 12`h , The
actual measure values should also be examined. The best congestion comparisons are made between similar urban areas.
Urban Area
Travel Delay
Excess Fuel Consumed
Truck Congestion
Cost
Total Congestion
Cost
(1,000 Hours) Rank
(1,000 Gallons) Rank
($ million) Rank
($ million) Rank
Medium Average (33 areas)
1 20,000
9,815
$94
$475
New Orleans LA
39,159
38
18,895
40
281
23
1,014
37
Bridgeport -Stamford CT -NY
37,119
43
16,586
45
194
34
898
42
Tucson AZ
35,993
44
17,477
44
176
41
856
44
Tulsa OK
30,341
47
14,128
47
107
54
682
48
Hartford CT
28,296
49
13,406
51
115
50
656
50
Honolulu HI
27,672
50
14,118
48
74
63
616
53
Buffalo NY
26,851
52
14,053
49
103
56
620
52
Baton Rouge LA
23,163
54
12,104
52
189
36
623
51
Raleigh NC
23,128
55
9,159
62
71
66
504
55
Grand Rapids MI
21,536
56
10,552
56
58
74
470
59
Rochester NY
20,582
57
10,550
57
73
64
469
61
Albuquerque NM
20,452
58
10,961
54
112
52
501
56
Albany NY
20,409
59
10,164
58
88
58
479
58
Birmingham AL
19,385
60
9,105
63
139
46
501
56
El Paso TX -NM
19,127
61
9,360
60
77
62
439
62
Springfield MA -CT
18,431
62
9,335
61
54
77
408
64
Charleston -North Charleston SC
18,422
63
9,024
64
126
48
470
59
Omaha NE -IA
18,224
64
9,535
59
57
75
407
65
Allentown PA -NJ
17,114
65
8,743
65
66
70
393
67
Wichita KS
16,860
66
8,594
66
88
58
407
65
New Haven CT
16,430
67
7,949
69
69
67
384
68
Columbia SC
16,315
68
8,018
68
104
55
409
63
McAllen TX
16,226
69
7,336
73
49
83
355
72
Colorado Springs CO
16,058
70
7,700
71
50
81
356
71
Toledo OH -Ml
15,905
71
8,451
67
79
61
381
69
Knoxville TN
14,946
72
7,180
74
87
60
367
70
Dayton OH
14,604
74
1 7,434
72
69
67
346
73
Sarasota -Bradenton FL
14,053
75
6,574
76
46
84
312
75
Cape Coral FL
12,959
78
5,637
83
44
85
288
79
Akron OH
12,283
81
6,586
75
50
81
284
80
Fresno CA
11,823
83
5,682
80
23
95
251
85
Provo -Orem UT
8,178
86
5,677
81
115
50
270
83
Bakersfield CA
8,001
89
3,743
90
65
71
215
87
Travelbelay-Extratravel time during the year.
Excess Fuel Consumed -Value of increased fuel consumption due to travel in congested conditions rather than free-flow conditions (using state average cost per gallon).
Truck Congestion Cost -Value of increased travel time and other operating costs of large trucks (estimated at $94.04 per hour of truck time) and the extra diesel consumed (using
state average cost per gallon).
Congestion Cost -Value of delay and fuel cost (estimated at $17.67 per hour of person travel, $94.04 per hour of truck time and state average fuel cost). Ih
Note:Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6 and 12t'. The
actual measure values should also be examined. The best congestion comparisons are made between similar urban areas,
.. -
Small Average (22 areas)
Little Rock AR
Worcester MA -CT
Spokane WA
Poughkeepsie -Newburgh NY -NJ
Jackson MS
Boise City ID
Madison WI
Pensacola FL -AL
Beaumont TX
Corpus Christi TX
Greensboro NC
Anchorage AK
Salem OR
Eugene OR
Oxnard CA
Winston-Salem NC
Stockton CA
Lancaster -Palmdale CA
Boulder CO
Laredo TX
Brownsville TX
Indio -Cathedral City CA
101 Area Total
101 Area Average
Remaining Area Total
Remaining Area Average
All 471 Area Total
All 471 Area Average
:i'- • �.N- vim• •.. i + •
Travel Delay
Excess Fuel Consumed
Truck Congestion
Cost
Total Congestion
Cost
(1,000 Flours)
Rank
(1,000 Gallons)
Rank
' ($ million)
Rank
( million)
Rank
8,170
3,850
36
190
14,799
73
5,262
84
61
72
i 336
74
13,143
76
6,432
77
52
80
302
77
13,004
77
7,928
70
59
73
312
75
12,843
79
5,723
79
55
76
299
78
12,287
80
4,897
86
53
78
282
82
11,963
82
5,673
82
40
87
269
84
11,159
84
5,773
78
72
65
283
81
11,017
85
5,120
85
38
89
247
86
8,028
87
3,629
92
40
87
190
88
8,012
88
4,110
88
! 26
94
179
90
7,887
90
3,534
93
( 27
93
176
91
7,371
91
3,847
89
38
89
181
89
6,948
92
4,254
87
( 41
86
175
92
6,354
93
3,728
91
32
92
155
93
6,282
94
2,241
95
16
97
134
96
6,111
95
2,400
94
21
96
135
95
5,115
96
2,102
98
53
78
148
94
4,181
97
1,228
100
10
99
88
99
4,080
98
2,204
96
10
99
89
98
3,919
99
2,130
97
34
91
107
97
3,511
100
1,866
99
14
98
81
100
1,685
101
660
101
9
101
40
101
6,036,500
2,697,300
24,360
138,400
59,800
26,700
240
1,370
906,200
424,200
4,040
21,170
2,400
1,140
11
57
6,942,700
3,121,500
28,400
159,600
14,710
6.610
60
340
Very Large Urban Areas -over 3 million population. Medium Urban Areas -over 500,000 and less than 1 million population.
Large Urban Areas -over 1 million and less than 3 million population. Small Urban Areas -less than 500,000 population.
Travel Delay -Extra travel time during the year.
Excess Fuel Consumed -Value of increased fuel consumption due to travel in congested conditions rather than free-flow conditions (using state average cost per gallon).
Truck Congestion Cost -Value of increased travel time and other operating costs of large trucks (estimated at $94.04 per hour of truck time) and the extra diesel consumed (using
state average cost per gallon).
Congestion Cost -Value of delay and fuel cost (estimated at $17.67 per hour of person travel, $94.04 per hour of truck time and state average fuel cost).
Note:Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6" and 12". The
actual measure values should also be examined. The best congestion comparisons are made between similar urban areas.
H
Value Rank
•
Freeway Commuter Stress
Index
._2
Very
Very Large Average (15 areas)
3.06
1.37
1.44
Los Angeles -Long Beach -Anaheim CA
3.75
1
1.57
1
1.63
2
Washington DC -VA -MD
3.48
2
1.40
10
1.52
7
Seattle WA
3.41
4
1.47
5
1.59
4
San Francisco -Oakland CA
3.30
6
1.49
4
1.64
1
Chicago IL -IN
3.16
10
1.39
11
1.45
17
New York -Newark NY -NJ -CT
3,15
11
1.38
13
1.44
18
Houston TX
113
12
1.43
7 I
1.47
13
Miami FL
2.85
15
1.28
21
1.30
78
Boston MA -NH -RI
2.81
17
1.38
13
1.47
13
Detroit Ml
2.80
18
1.26
23
1.28
80
Phoenix -Mesa AZ
2.66
21
1.24
28
1.34
64
San Diego CA
2.66
21
1.25
26
132
75
Dallas -Fort Worth -Arlington TX
2.65
23
1.34
18
138
49
Atlanta GA
2.48
30
1.25
26
134
64
Philadelphia PA -NJ -DE -MD
2.41
33
1.19
32
125
84
Very Large Urban Areas -over 3 million population.
Medium Urban Areas -over
500,000 and less than 1 million population.
Large Urban Areas -over 1 million and less than 3 million population,
Small Urban Areas -less than 500,000 population,
Freeway Planning Time Index -A travel time reliability measure that represents the total travel time that should be planned for a trip to be late for only 1 work trip per month. A PTI
of 2.00 means that 40 minutes should be planned for a 20 -minute trip in light traffic (20 minutes
x 2.00 = 40 minutes).
Freeway Travel Time Index -The ratio of travel time in the peak period to the travel
time at low volume conditions.
A value of 1.30 indicates a 20
-minute free-flow trip takes 26
minutes in the peak period (20 minutes x 1.30 = 26 minutes). Note that the TTI reported
in
Table 3 is only for freeway facilities to compare to the
freeway -only PTI values.
Freeway Commuter Stress Index - The travel time index calculated for only the peak direction in each peak period
(a measure of the extra travel time for a commuter).
Note: Please do not place too much emphasis on small differences in the rankings.
There may be little difference in
congestion between areas
ranked (for example) 6th
and 12th. The
actual measure values should also be examined.
a
N)
Table
3. How Reliable is Freewa Travel in
Your Town,
2014, Continued
-7-
y Planning Tim
Freeway e Index
Fre w. a n n, �nq Tune
Freeway Travel Time Index
Freeway Commuter
Index
Stress
I
Urban Area
_ �a
_�
Value
Value
Ran
Rank
Value
Rank i
Value
Rank
2.46
1.42
9
1.37
1.48
12
CD Large Average (31 areas)
Portland OR -WA
3.27
7
8
1.43
7
1.52
7
San Jose CA
3.24
9
1.36
16
1 �54
6
Riverside -San Bernardino CA
3.21
135
17
1 A2
23
=-
Denver -Aurora CO
2.97
13
14
1.38
13
1,44
18
San Juan PR
2.93
15
1.26
23
1.34
64
co
0
Baltimore MD
2.85
20
1.32
20
1.37
53
0
Minneapolis -St. Paul MN
2.72
1.21
30
1.29
79
(D
Charlotte NC -SC
2.61
24
25
1.50
1
325
1.59
4
0
Z)
Austin TX
2.58
2.58
1.19
32
1.24
85
Sacramento CA
252
29
1.17
37
1.23
88
Virginia Beach VA
2.42
32
45
1.44
18
Louisville -Jefferson County KY -IN
1.19
32
12 4
85
Tampa -St. Petersburg FL
2.39
34
1,15
45
1.19
92
Cincinnati OH -KY -IN
2*37
35
36
1.18
35
1.26
81
Nashville -Davidson TN
2.36
I
37
1.16
40
1.22
89
Orlando FL
2.34
39
1.14
50
1.18
96
Jacksonville FL
2.27
42
1.18
35
1.21
90
Providence RI -MA
2.25
44
1.12
58
1.42
23
Columbus OH
2.21
8
46
1.15
45
1.51
9
Las Vegas -Henderson NV
47
IA3
54
1.40
34
St. Louis MO -IL
216
49
1.11
62
1.4 2
23
Salt Lake City -West Valley City UT
213
1.11
62
1 1.41
27
Indianapolis IN
2,12
51
51
1.33
19
1
1.36
55
San Antonio TX
2-12
55
1.14
50
1.42
23
Memphis TN -MS -AR
2 ' 08
55
1
1.15
45
1.43
21
Oklahoma City OK
2.08
59
1.11
62
1.38
49
Kansas City MO -KS
1.99
60
1.17
37
1.19
92
Milwaukee WI
1.97
62
1.10
69
1.38
49
Cleveland OH
1.96
77
1.14
50
1.43
21
Pittsburgh PA
I 1.80
80
1.07
79
1.35
61
Richmond VA
1.76
3 million population.
Medium
Small
Urban Areas -over 500,000 and less than
Urban Areas -less than 500,000 population.
be for a trip to be late
1 million population.
for only 1 work trip per
month. A PTI
Very Large Urban Areas -over 3 million population.
Large Urban Areas -over I million and less than
Freeway Planning Time Index -A travel time reliability
measure that represents the total travel time
trip in light traffic (20 minutes x
that should planned
2.00 = 40 minutes).
of 2.00 means that 40 minutes should be planned
for a 20 -minute
to the travel time at
low volume
conditions.
A value of 1.30 indicates
a 20 -minute free-flow trip
takes 26
Freeway Travel Time Index -The ratio of travel
time in the peak period
Note that the TTI reported in Table
3 is only for freeway facilities to compare to the
freeway -only PTI values.
minutes in the peak period (20 minutes x 130 = 26
minutes).
for only the peak direction
in
each peak period (a measure of the extra travel
time for a commuter).
Freeway commuter Stress Index - The travel time
emphasis on
index calculated
small differences in the rankings. There
may be
little difference
in congestion between areas
ranked (for example)
6th and 12th. The
Note: Please do not place too much
actual measure values should also be examined.
a. il M-Iff.097.220in. If
Urban Area
Freewa Planning
Time Index
Freeway
Travel Time Index
Freeway Commuter Stress
Index
Value Flank
Value Rank
Value Rank
Medium Average (33 areas)
2.08
1.14
1.38
New Orleans LA
3.46
3
1.45
6
1.49
11
Bridgeport -Stamford CT -NY
3.32
5
1.39
11
1.50
10
Baton Rouge LA
2.80
18
1.21
30
1.24
85
Honolulu HI
258
25
1.51
2
1.62
3
Charleston -North Charleston SC
2.54
28
1.16
40
1.47
13
Hartford CT
2.30
38
( 1.16
40
( 1.20
91
Colorado Springs CO j
2.21
44
1.13
54
1.39
46
Buffalo NY
2.13
49
1.12
58
j 1.41
27
Raleigh NC
2.11
53
1.12
58
1.40
34
Tucson AZ
2.11
53
1.14
50
1.47
13
Toledo OH -Ml
2.07
57
1.07
79
1.41
27
New Haven CT
2.05
58
1.12
58
1.40
34
Albany NY
1.97
60
1.11
62
1.40
34
Birmingham AL
1.96
62
1.08
75
1.36
55
Bakersfield CA
1.95
64
1.07
79
1.34
64
Wichita KS
1.93
65
1.11
62
1.40
34
Grand Rapids MI
1.89
67
1.06
86
1.41
27
Columbia SC
1.88
68
1.08
75
1.38
49
Albuquerque NM
1.87
69
1.08
75
1.39
46
Rochester NY
1.83
72
1.09
72
1.40
34
Sarasota -Bradenton FL
1.83
72
1.03
96
1.40
34
Akron OH
1.82
74
1.06
86
1.34
64
Knoxville TN
1.82
74
1.07
79
1.36
55
Allentown PA -NJ
1.78
78
1.09
72
1.40
34
El Paso TX -NM
1.73
81
1.17
37
1.16
97
Tulsa OK
1.73
81
1.08
75
1.40
34
Fresno CA
1.72
84
1.06
86
1.33
73
Cape Coral FL
1.70
87
1.04
95
1.40
34
Dayton OH
1.68
88
1.05
92
1.34
64
Omaha NE -IA
j 1.65
90
1.10
69
1.39
46
Springfield MA -CT
1.65
90
1.05
92
1.36
55
McAllen TX
1.62
92
1.16
40
1.34
64
Provo -Orem UT
1.53
94
1.03
96
1.34
64
Medium Urban Areas -over 500,000 and less than 1 million population.
Freeway Planning Time Index -A PTI of 2.00 means that 40 minutes should be planned for a 20 -minute trip in light traffic (20 minutes x 2.00 = 40 minutes).
Freeway Travel Time Index -A value of 1.30 indicates a 20 -minute free-flow trip takes 26 minutes in the peak period (20 minutes x 1.30 = 26 minutes).
Freeway Commuter Stress Index - The travel time index calculated for only the peak direction in each peak period (a measure of the extra travel time for a commuter).
N Note: Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6"' and 12". The
00 actual measure values should also be examined.
N)
C-1)
Urban Area
Table
3. How Reliable is Freeway Travel in
Freeway Planning Time Index
Your Town, 2014, Continued
Freeway Travel Time Index
Freeway Commuter Stress
Index
Value Rank
Value Rank
Value Rank
Small Average (22 areas)
1.76
1.09
1.30
Boulder CO
2.48
30
1.27
22
126
81
Stockton CA
227
39
1.13
54
115
99
Anchorage AK
2.26
41
1.26
23
1.19
92
Boise City ID
2.23
43
1.15
45
1.14
101
Oxnard CA
2.15
48
1.11
62
1.36
55
C/)
Madison Wl
1.92
66
1.13
54
1.41
27
0
0
Little Rock AR
1.85
70
1.11
62
1.15
99
Spokane WA
1.84
71
1.07
79
1.41
27
Winston-Salem NC
1.81
76
1.06
86
1.33
73
Jackson MS
1.78
78
1.07
79
1.36
55
Eugene OR
1.73
81
1.09
72
1.41
27
Poughkeepsie -Newburgh NY -NJ
1.72
84
1.05
92
1.35
61
Worcester MA -CT
1.71
86
1.06
86
1.34
64
Beaumont TX
1.68
88
1.16
40
1 1.16
97
Salem OR
1.62
92
1.06
86
1,40
34
Corpus Christi TX
1.47
95
1.10
69
1.35
61
Pensacola FL -AL
1.47
95
1.02
99
1.40
34
Greensboro NC
1.44
97
1.03
96
1.32
75
Laredo TX
1.44
97
1.23
29
1.19
92
Lancaster -Palmdale CA
1.41
99
1.02
99
1.32
75
Brownsville TX
1.35
100
1.07
79
137
53
Indio -Cathedral Cit CA
1.32
101
1.01
101
1.26
81
101 Area Average
2.66
1.28
1.40
Remaining Area Average
1.74
1.08
1.21
All 471 Area Average
2.41
1.23
1.35
Very Large Urban Areas -over 3 million population. Medium Urban Areas -over 500,000 and less than 1 million population,
Large Urban Areas -over 1 million and less than 3 million population. Small Urban Areas -less than 500,000 population.
Freeway Planning Time Index -A travel time reliability measure that represents the total travel time that should be planned for a trip to be late for only 1 work trip per month. A Pl
of 2.00 means that 40 minutes should be planned for a 20 -minute trip in light traffic (20 minutes x 2.00 = 40 minutes).
Freeway Travel Time Index -The ratio of travel time in the peak period to the travel time at low volume conditions. A value of 1.30 indicates a 20 -minute free-flow trip takes 26
minutes in the peak period (20 minutes x 1.30 = 26 minutes). Note that the TTI reported in Table 3 is only for freeway facilities to compare to the freeway -only PTI values.
Freeway Commuter Stress Index - The travel time index calculated for only the peak direction in each peak period (a measure of the extra travel time for a commuter).
Note: Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6"' and 12"'.
actual measure values should also be examined.
The
,♦i �,• ' 1 it
2015 Urban Mobility Scorecard 30
Annual Hours of Delay
Annual Congestion Cost
Total
Per Auto
Total
$ per Auto
Urban Area
(000)
Commuter
(Million $)
Commuter
Aberdeen -Bel Air S -Bel Air N MD
4,533
20
112
489
Abilene TX
1,039
9
24
201
Aguadilla-Isabela-San Sebastian PR
4,840
16
130
424
Albany GA
1,342
13
31
301
Alexandria LA
1,376
15
34
368
Altoona PA
1,095
13
24
291
Amarillo TX
3,087
14
72
322
Ames IA
452
4
9
82
Anderson IN
1,317
14
31
329
Anderson SC
1,057
13
27
323
Ann Arbor MI
8,658
28
194
621
Anniston AL
987
11
23
260
Antioch CA
4,448
15
100
347
Appleton WI
2,896
12
73
307
Arecibo PR
1,931
13
51
354
Asheville NC
7,849
26
178
590
Athens -Clarke County GA
2,340
17
52
371
Atlantic City NJ
6,514
24
152
561
Auburn AL
1,272
15
30
356
Augusta -Richmond County GA -SC
12,338
30
282
689
Avondale -Goodyear AZ
2,893
13
70
310
Bangor ME
822
14
19
322
Barnstable Town MA
7,520
29
163
627
Battle Creek MI
1,128
13
25
291
Bay City MI
957
13
23
320
Bellingham WA
1,460
12
33
278
Beloit W I -IL
420
6
11
160
Bend OR
1,164
12
31
329
Benton Harbor -St. Joseph -Fair Plain MI
774
15
18
355
Billings MT
1,595
12
35
268
Binghamton NY -PA
2,679
16
64
382
Bismarck ND
969
10
21
220
Blacksburg VA
695
7
15
149
Bloomington IN
1,036
9
24
204
Bloomington -Normal IL
1,495
10
33
233
Bonita Springs FL
6,731
19
148
424
Bowling Green KY
1,219
14
29
325
Bremerton WA
3,265
16
77
379
Bristol TN -VA
923
12
22
289
Brunswick GA
888
11
20
252
Burlington NC
1,176
9
26
192
Burlington VT
1,983
17
46
382
Camarillo CA
1,229
17
27
368
Canton OH
4,761
16
107
367
Cape Girardeau MO -IL
676
10
15
214
Carbondale IL
855
11
20
264
Carson City NV
681
7
15
149
Cartersville GA
858
13
20
301
Casa Grande AZ
537
6
14
163
2015 Urban Mobility Scorecard 30
Table r r- ,ofor r, 1 (continued)
Annual Hours of DelayAnnual CongestionCosl
2015 Urban Mobility Scorecard 31
Total
Per Auto
Total
$ per Auto
Urban Area
(000)
Commuter
(Million )
Commuter
Casper WY
792
10
21
265
Cedar Rapids IA
1,479
7
31
153
Champaign IL
1,966
13
46
291
Charleston WV
3,399
21
78
481
Charlottesville VA
1,349
13
29
275
Chattanooga TN -GA
11,261
28
294
730
Cheyenne WY
914
11
24
295
Chico CA
829
8
19
179
Clarksville TN -KY
2,051
12
52
298
Cleveland TN
983
13
22
294
Coeur d'Alene ID
1,850
17
41
385
College Station -Bryan TX
2,588
14
63
344
Columbia MO
1,884
14
42
304
Columbus GA -AL
4,190
15
93
325
Columbus IN
681
8
16
191
Concord CA
21,712
35
466
752
Concord NC
2,562
12
59
269
Conroe -The Woodlands TX
3,744
14
83
307
Conway AR
770
10
17
229
Corvallis OR
608
6
15
149
Cumberland MD -WV -PA
908
14
23
345
Dalton GA
1,171
13
26
291
Danbury CT -NY
2,937
16
68
382
Danville IL
539
9
13
207
Danville VA -NC
734
9
16
202
Davenport IA -IL
5,335
18
120
402
Davis CA
553
7
13
169
Daytona Beach -Port Orange FL
4,944
23
114
524
Decatur AL
753
10
17
237
Decatur IL
1,119
11
27
266
DeKalb IL
641
8
14
187
Deltona FL
2,561
13
59
296
Denton -Lewisville TX
11,039
29
263
683
Des Moines IA
6,142
12
129
260
Dothan AL
1,236
15
30
370
Dover DE
1,332
11
31
249
Dover -Rochester NH -ME
906
10
20
219
Dubuque IA -IL
768
11
16
221
Duluth MN -WI
2,462
20
56
451
Durham NC
9,575
26
206
558
Eau Claire WI
1,145
10
30
275
EI Centro -Calexico CA
439
4
10
87
EI Paso de Robles-Atascadero CA
314
4
8
106
Elkhart IN -MI
2,107
14
52
337
Elmira NY
762
11
18
250
Erie PA
3,445
17
87
419
Evansville IN -KY
3,742
16
89
370
Fairbanks AK
635
9
15
212
Fairfield CA
1,980
14
42
303
Fajardo PR
547
6
15
151
Fargo ND -MN
5,255
26
110
551
Farmington NM
1,046
12
28
336
2015 Urban Mobility Scorecard 31
Table 4. Key Congestion Measures for 370 Urban Areas, 2014 (continued)
Annual Hoursof Delay Annual Congestion Css ,
2015 Urban Mobility Scorecard 32
Total
Per Auto
Total
$ per Auto
Urban Area
(000)
Commuter
(Million $)
Commuter
Fayetteville NC
6,163
18
131
393
Fayetteville -Springdale -Rogers AR -MO
7,564
24
167
520
Flagstaff AZ
872
10
28
335
Flint MI
9,342
25
214
570
Florence AL
1,232
14
28
326
Florence SC
1,104
11
28
272
Florida-Imbrey-Barceloneta PR
892
12
24
310
Fond du Lac WI
498
6
13
160
Fort Collins CO
5,606
19
122
425
Fort Smith AR -OK
2,062
16
46
358
Fort Walton Beach -Navarre -Wright FL
4,897
23
107
494
Fort Wayne IN
9,252
28
212
641
Frederick MD
2,405
16
59
394
Fredericksburg VA
4,004
25
95
607
Gadsden AL
962
14
23
342
Gainesville FL
3,404
17
75
369
Gainesville GA
2,137
15
49
343
Galveston TX
505
6
11
122
Gastonia NC -SC
2,656
15
60
339
Gilroy -Morgan Hill CA
1,474
14
33
311
Glens Falls NY
1,222
17
29
391
Goldsboro NC
705
11
16
244
Grand Forks ND -MN
714
7
16
164
Grand Junction CO
1,363
10
30
212
Great Falls MT
776
11
17
234
Greeley CO
1,596
13
36
285
Green Bay WI
3,728
17
95
431
Greenville NC
1,525
11
34
255
Greenville SC
10,389
24
260
602
Guayama PR
1,193
14
32
383
Gulfport MS
4,463
19
98
411
Hagerstown MD -WV -PA
3,223
16
80
392
Hammond LA
757
10
19
239
Hanford CA
106
1
4
37
Harlingen TX
1,530
10
34
228
Harrisburg PA
10,342
23
254
562
Harrisonburg VA
815
10
18
237
Hattiesburg MS
1,159
13
26
298
Hazleton PA
656
13
15
283
Hemet CA
495
3
11
62
Hickory NC
4,423
19
98
427
High Point NC
2,866
16
63
345
Hinesville GA
462
7
10
169
Holland MI
1,688
15
37
341
Hot Springs AR
732
11
15
232
Houma LA
2,424
16
60
397
Huntington WV -KY -OH
3,280
16
77
362
Huntsville AL
7,253
23
159
510
Idaho Falls ID
621
6
14
135
Iowa City IA
740
6
16
125
Ithaca NY
867
16
20
370
Jackson MI
1,182
13
26
280
2015 Urban Mobility Scorecard 32
Table 4. Key Congestion Measures for 370 Urban areas, 2014 (continued)
Annual Flours
of Delay
Annual Congestion
Cost
Total
Per Auto
Total
$ per Auto
Urban Area
(000)
Commuter
(Million $)
Commuter
Jackson TN
1,024
13
28
367
Jacksonville NC
1,428
13
31
284
Janesville W l
611
8
16
209
Jefferson City MO
607
8
14
172
Johnson City TN
1,594
12
37
272
Johnstown PA
711
10
16
235
Jonesboro AR
1,089
15
24
338
Joplin MO
1,252
15
29
335
Juana Diaz PR
907
11
24
296
Kailua (Honolulu County) -Kaneohe HI
1,254
10
29
227
Kalamazoo MI
5,136
23
115
515
KankakeelL
873
10
22
244
Kennewick -Richland WA
2,780
12
67
281
Kenosha WI
1,133
8
30
219
Killeen TX
2,533
11
58
254
Kingsport TN -VA
1,665
15
40
357
Kingston NY
1,482
17
34
394
Kissimmee FL
7,814
22
185
517
Kokomo IN
1,174
12
27
264
La Crosse WI -MN
1,350
12
35
323
Lady Lake -The Villages FL
606
5
14
111
Lafayette IN
2,473
15
59
363
Lafayette LA
7,047
26
194
715
Lafayette -Louisville -Erie CO
1,083
12
23
264
Lake Charles LA
2,352
15
64
414
Lake Havasu City AZ
358
4
11
114
Lake Jackson -Angleton TX
694
9
16
205
Lakeland FL
4,022
14
96
331
Lancaster PA
7,807
18
187
441
Lansing MI
7,742
24
168
513
Las Cruces NM
1,126
8
32
220
Lawrence KS
1,430
13
34
310
Lawton OK
838
8
19
187
Lebanon PA
580
7
14
166
Leesburg -Eustis -Tavares FL
1,279
9
31
203
Leominster -Fitchburg MA
1,546
13
34
283
Lewiston ID -WA
579
9
14
200
Lewiston ME
722
11
18
273
Lexington Park -Cal -Cher Ranch Est MD
743
15
16
329
Lexington -Fayette KY
8,250
27
199
656
Lima OH
938
12
25
325
Lincoln NE
5,544
19
124
428
Livermore CA
1,395
16
31
358
Lodi CA
571
8
13
179
Logan UT
793
8
25
234
Lompoc CA
440
6
10
126
Longmont CO
1,238
12
27
266
Longview TX
1,512
15
35
342
Longview WA -OR
985
15
24
367
Lorain -Elyria OH
2,550
14
58
308
Lubbock TX
2,933
12
67
269
Lynchburg VA
2,328
18
50
387
2015 Urban Mobility Scorecard 33
Table 4. Key Congestion Measures for 37BUrban Areas, 2Q14(cxontinued)
Annual Hours ofDelay
Annual Congestion Cost
Total
Per Auto
Total
$ per Auto
Urban Area
(000)
Commuter
(Million $)
Commuter
Macon GA
2.271
15
51
337
Madera CA
300
4
8
87
Manchester NH
2.302
13
53
311
Mandeville -Covington LA
1.753
18
45
470
Manhattan KS
478
5
11
109
Mankato MN
002
8
13
182
Mansfield OH
838
10
19
232
Manteca CA
623
7
16
177
Marysville WA
2.630
18
02
389
Mauldin -Simpsonville SC
886
7
22
169
Mayaguez PR
1.468
13
39
353
McKinney TX
1.811
0
43
215
Medford OR
1.989
11
47
267
Merced CA
1.317
Q
33
318
Michigan City -La Porte IN -Ml
844
12
21
207
Middletown (}H
850
8
20
182
Midland MI
735
10
18
238
Midland TX
972
7
25
188
Mission Viejo -Lk Forest -San Clemente CA
17.389
28
361
500
Missoula MT
1.443
15
32
334
Mobile AL
10.390
38
236
670
Modesto CA
6.656
18
159
421
K8onenaen-CaliforniaPA
563
8
13
183
Monroe LA
1.820
14
45
358
Monroe MI
829
g
10
201
Montgomery AL
0.494
24
149
553
Morgantown WV
1.065
14
24
311
Morristown TN
1.001
19
24
458
Mount Vernon VVA
057
15
21
367
Muncie IN
1.063
11
25
247
Murrieta -Temecula -Menifee CA
3.084
7
72
162
Muskegon MI
2.697
18
59
348
Myrtle Bnouh-3ooaeteeSC-NC
7.452
30
188
754
Nampa ID
2.109
13
47
283
Napa CA
1.178
13
26
390
Nashua NH -MA
3.372
14
78
324
New Bedford MA
1.503
iU
34
219
Newark OH
021
7
14
167
North port -Pod Charlotte FL
1.806
10
41
216
Norwich -New London CT -RI
3.017
28
OA
451
Ocala FL
1.994
12
47
278
Odessa TX
1.605
13
39
330
Ogden -Layton UT
10.408
18
339
581
Olympia -Lacey WA
3.920
20
94
481
Oshkosh WI
513
O
13
155
Owensboro KY
1.010
13
27
335
Palm Coast -Daytona Bch -Port Orange FL
9.849
20
230
471
Panama Qty FL
3.395
21
77
485
PorkeryburgVVVcC}H
965
14
22
317
Pascagoula MS
778
14
18
323
Peoria IL
4^743
17
110
391
Petaluma CA
834
Q
15
201
2015 Urban Mobility Scorecard 34
Table 4. Key Congestion Measures for 370 Urban Areas, 2014 (continued)
Annual Hours of Delay
Annual Congestion Cost
Total
Per Auto
Total
$ per Auto
Urban .Area
(000)
Commuter
(Million )
Commuter
Pine Bluff AR
626
7
14
160
Pittsfield MA
556
7
12
150
Pocatello ID
656
9
15
199
Ponce PR
1,862
13
50
336
Port Huron MI
1,209
13
28
297
Port St. Lucie FL
8,123
19
189
448
Porterville CA
228
3
6
73
Portland ME
2,973
14
70
332
Portsmouth NH -ME
1,479
15
33
349
Pottstown PA
948
9
22
199
Prescott Valley -Prescott AZ
1,156
12
27
285
Pueblo CO
1,690
11
38
250
Racine WI
1,412
10
37
256
Radcliff -Elizabethtown KY
918
10
21
221
Rapid City SD
1,153
12
27
281
Reading PA
5,183
19
125
465
Redding CA
2,093
16
46
345
Reno NV
8,300
20
179
428
Roanoke VA
4,585
20
105
465
Rochester MN
1,581
13
34
282
Rock Hill SC
1,355
12
35
311
Rockford IL
7,221
23
173
558
Rocky Mount NC
714
11
15
228
Rome GA
1,029
16
24
361
Round Lk Bch -McHenry -Grayslake IL -WI
402
1
10
34
Saginaw MI
2,082
17
46
364
Salinas CA
2,037
10
47
233
Salisbury MD -DE
1,164
11
27
258
San Angelo TX
899
8
20
188
San German -Cabo Rojo -Sabana Grnd PR
749
6
20
159
San Luis Obispo CA
822
10
18
218
Santa Barbara CA
3,993
20
89
434
Santa Clarita CA
3,703
15
86
341
Santa Cruz CA
3,806
21
82
444
Santa Fe NM
1,790
19
42
437
Santa Maria CA
1,890
13
43
299
Santa Rosa CA
5,915
19
128
407
Saratoga Springs NY
843
11
20
267
Savannah GA
8,013
28
179
619
Scranton PA
8,297
21
188
473
Seaside -Monterey CA
1,606
13
35
287
Sheboygan WI
523
7
13
177
Sherman TX
735
9
19
228
Shreveport LA
8,412
27
222
713
Sierra Vista AZ
565
7
13
156
Simi Valley CA
690
5
14
110
Sioux City IA -NE -SD
598
5
14
127
Sioux Falls SD
2,743
15
66
368
Slidell LA
791
8
21
212
South Bend IN -MI
5,205
18
125
425
South Lyon -Howell MI
2,376
18
65
505
Spartanburg SC
3,250
16
82
406
2015 Urban Mobility Scorecard 35
Table 4. Key Congestion measures for 370 Urban Areas, 2014 (continued)
Annual Hours of Delay
Annual Congestion Cost
Total
Per Auto
Total
$ per Auto
Urban Area
(000)
Commuter
(million $)
Commuter
Springfield IL
2,222
13
51
287
Springfield MO
7,403
25
166
556
Springfield OH
796
9
18
195
St. Augustine FL
1,055
13
23
275
St. Cloud MN
2,190
19
51
438
St. George UT
1,146
10
32
281
St. Joseph MO -KS
936
10
24
263
State College PA
516
5
11
116
Sumter SC
927
12
24
308
Syracuse NY
9,443
22
224
530
Tallahassee FL
5,846
28
130
621
Temple TX
1,014
11
26
267
Terre Haute IN
1,812
19
43
452
Texarkana TX -AR
1,014
12
25
294
Texas City TX
1,917
16
42
349
Thousand Oaks CA
5,486
25
116
527
Titusville FL
542
7
13
159
Topeka KS
2,533
16
62
388
Tracy CA
126
1
3
38
Trenton NJ
6,970
24
157
532
Turlock CA
111
1
3
31
Tuscaloosa AL
2,563
17
61
403
Twin Rivers-Highstown NJ
1,178
17
26
384
Tyler TX
2,028
14
53
379
Uniontown-Connellsville PA
453
9
10
200
Utica NY
2,288
19
53
433
Vacaville CA
665
7
14
143
Valdosta GA
1,246
15
29
351
Vallejo CA
3,828
21
83
456
Vero Beach -Sebastian FL
1,475
18
35
418
Victoria TX
1,014
14
24
336
Victorville-Hesperia CA
4,286
12
102
292
Villas NJ
800
12
19
286
Vineland NJ
1,150
11
26
262
Visalia CA
1,980
8
46
190
Waco TX
2,039
11
52
276
Waldorf MD
1,713
14
41
326
Walla Walla -WA -OR
258
4
7
118
Warner Robins GA
1,646
11
36
247
Waterbury CT
3,851
20
90
458
Waterloo IA
532
4
11
88
Watsonville CA
1,118
14
25
315
Wausau WI
868
11
22
283
Weirton -Steubenville WV -OH -PA
742
10
18
239
Wenatchee WA
772
10
19
251
West Bend WI
658
9
17
229
Westminster-Eldersburg MD
1,101
14
27
354
Wheeling WV -OH
954
11
24
275
Wichita Falls TX
1,031
10
25
239
Williamsport PA
1,045
20
23
434
Wilmington NC
4,905
20
106
435
Winchester VA
977
13
22
293
2015 Urban Mobility Scorecard 36
Table 4.
Key Congestion Measures for 370 Urban Areas, 2014 (continued)
Annual Hours of Delay
Annual Congestion
Cost
Total
Per Auto
Total
per Auto
Urban Area (000)
Commuter
(Million $)
Commuter
Winter Haven FL
2,888
13
71
329
Yakima WA
2,187
15
52
368
Yauco PR
443
5
12
121
York PA
3,801
15
90
368
Youngstown OH -PA
7,744
20
181
466
Yuba City CA
1,212
9
30
227
Yuma AZ -CA
1,531
11
41
292
Zephyrhills FL
602
12
14
274
2015 Urban Mobility Scorecard 37
1 . Current Employment Statistics, U.S. Bureau of Labor Statistics, U.S. Department of Labor,
Washington D.C., http://www.bls.gov/ces/home.htm
I � i i I I i I I I i i , i I I i 111111 1 i I ill 1 11 M11 III , 1111waW11
3. Federal Highway Administration. "Highway Performance Monitoring System," 1982 to 2010
Data. November 2012. Available: htt :11www.fhE2 Itio
p wa.dotaolvl n/homs.cfm
2olicvinforme
4. SHRP2 Project C11, Chapter 3. Reliability Analysis Tool: Technical Documentation and
User's Guide Prepared by: Cambridge Systematics, Inc. and Weris, Inc. Prepared for:
Transportation Research Board July 2013. Available:
htto.-Ilwww,tiDics.usltoolsldocumentsISHRP-CI I-ReLgLtEty-Tech-Doc-and®User-Guide. p_cif
5. Urban Mobility Scorecard Methodology. Texas A&M Transportation Institute, College
Station, Texas. 2015. Available: http.'Amobility.-tamu.-edulums/methodoloflY
6. Development of Diurnal Traffic Distribution and Daily, Peak and Off -Peak Vehicle Speed
Estimation Procedures forAir Quality Planning. Final Report, Work Order B-94-06,
Prepared for Federal Highway Administration, April 1996
7. Moving Ahead for Progress in the 21st Century Act (MAP -21): A Summary of Highway
Provisions. United States Department of Transportation, Federal Highway Administration,
Office of Policy and Governmental Affairs, July 17, 2012. Available:
http://www.fhwa.dot.gov/map21 /sum ma rvi nfo.cfrn.
2015 Urban Mobility Scorecard 39
Appendix A
The procedures used in the 2015 Urban Mobility Scorecard have been developed by the Texas A&M
Transportation Institute over several years and several research projects. The congestion estimates for
all study years are recalculated every time the methodology is altered to provide a consistent data
trend. The estimates and methodology from this report should be used in place of any other previous
measures. All the measures and many of the input variables for each year and every city are provided in
aspreadsheet that can bedownloaded at .
This appendix documents the analysis conducted for the methodology utilized in preparing the 2015
Urban Mobility Scorecord This methodology incorporates private sector traffic speed data from |NR|X
for calendar year 2014 into the calculation of the mobility performance measures presented in the initial
calculations. The roadway inventory data source for most of the calculations is the Highway
Performance Monitoring System from the Federal Highway Administration (1). Adetailed description of
that da1esetoan be found a��.
There are several changes to the UMS methodology for the 2015 Urban Mobility Scorecard. The largest
changes have to do with the reliability measure (Planning Time Index), estimates of daily truck volumes,
and the ever-increasing INRIX speed data set size. These changes are documented in more detail in the
following sections of the Methodology. Here are brief summaries of what has changed:
Estimates ufhourly truck volume were developed and incorporated. In past reports, trucks
were assumed tohave the same patterns ascar travel.
The measure of the variation in travel time from day-to-day now uses a more representative
trip -based process rather than the old clataset that used individual road links. The Planning
Time Index (PTI) is based on the ideas that travelers want to be on-time for an important
trip 19 out of 20 times; so one would be late to work only one day per month (on-time for
19out ofthe ZOwork days each month). For example, a PTI value of1.00indicates that a
traveler should allow 36 minutes to make an important trip that takes 20 minutes in low
traffic volumes.
Speeds supplied by INRIX are collected every 15 -minutes from a variety of sourceseveryday
oftheyearonmostmejornoads.K4anymfthes|owspeedsformer|yoonsidered^toos|mmto
beavalid observation" are now being retained inthe |NR|Xdetaset.Experience and
increased travel speed sample sizes have increased the confidence in the data.
A,|
20l5Urban D�obil�vScorecard K8cthoduloQy
The Urban Mobility Scorecord (UMS) procedures provide estimates of mobility at the areawide level.
The approach that is used describes congestion in consistent ways allowing for comparisons across
urban areas or groups of urban areas.
Calculation procedures use a clataset of traffic speeds from INRIX, a private company that provides
travel time information toavariety ofcustomers. |NR|X's2Ol4data isanannual average oftraffic
speed for each section of road for every 15 minutes of each day for a total of 672 day/time period cells
(24 hours x 7 days x 4 periods per hour).
INRIX's speed data improves the freeway and arterial street congestion measures in the following ways:
m "Real" rush hour speeds used to estimate a range of congestion measures; speeds are measured
not estimated.
Overnight speeds were used to identify the free -f low speeds that are used as a com parison
standard; low-volume speeds on each road section were used as the comparison stondord
The volume and roadway inventory data from FHWA's Highway Performance Monitoring System
(HPMS) files were used with the speeds to calculate travel delay statistics; the best speed data is
combined with the best volume information to produce high-quolity congestion measures.
The following steps were used to calculate the congestion performance measures for each urban
roadway section.
1. Obtain HPK8Straffic volume data by road section
J. Match the HPMS road network sections with the INRIX traffic speed clataset road sections
3. Estimate traffic volumes for each hour time interval from the daily volume data
4. Calculate average travel speed and total delay for each hour interval
5. Establish free-flow (ie,low volume) travel speed
6. Calculate congestion performance measures
7. Additional steps when volume data had nospeed data match
The mobility measures require four data inputs:
• Actual travel speed
• Free-flow travel speed
w Vehicle volume
m Vehicle occupancy (persons per vehicle) to calculate person -hours of travel delay
2015 Urban Mobility Scorecard Methodology A-2
The 2014 IN RIX traffic speed data provide abetter data source forth e first two inputs, a ctu a I and free-
flow travel time. The VMSanalysis requires vehicle and person -volume estimates for the delay
calculations; these were obtained from FHVV/'sHPW15dataset. The geographic referencing systems are
different for the speed and volume clatasets, a geographic matching process was performed to assign
traffic speed data to each HPMS road section for the purposes of calculating the performance measures.
When INRIX traffic speed data were not available for sections of road or times of day in urban areas, the
speeds were estimated. This estimation process isdescribed inmore detail inStep 7.
The HPMS dataset from FHWA provided the source for traffic volume data, although the geographic
designations inthe HPMSdatasetare not identical tothe |NR}Xspeed data, The daily traffic volume
data must be divided into the same time interval as the traffic speed data (hour intervals), While there
are some detailed traffic counts on major roads, the most widespread and consistent traffic counts
available are average daily traffic (ADT)counts. The hourly traffic volumes for each section, therefore,
were estimated from these ADT counts using typical time -of -day traffic volume profiles developed from
continuous count locations orother data Sources. The section "Estimation ofHourly Traffic Vo|umes"
shows the average hourly volume profiles used in the measure calculations.
Volume estimates for each day of the week (to match the speed database) were created from the
average volume data using the factors inExhibit A,1. Automated traffic recorders from around the
country were reviewed and the factors in Exhibit A-1 are a "best -fit" average for both freeways and
major streets. Creating an hourly volume to be used with the traffic speed values, then, is a process of
multiplying the annual average by the daily factor and by the hourly factor.
Exhibit A-1. Day of Week Volume Conversion Factol
Adjustment Factor
Day of Week (to convert average annual volume into
day of week volume)
�
Monday toThursday
+S%
Friday
+10K
Saturday
-10%
Sundav
|
-20%
2015 Urban Mobili�y Scorecard Methodology &-3
Step 2. Combine the Road Networks for Traffic Volume and Speed Data
'The second step was to combine the road networks for the traffic volume and speed data sources, such
that anestimate oftraf0cspeedandtrafMovo|umevvosavai|ab|eforeachroadwoysegmentineach
urban area. The combination (also known as conflation) of the traffic volume and traffic speed networks
was accomplished using Geographic Information Systems (G|6)tools. The |NR|Xspeed network was
chosen as the base network; an ADT count from the HIPIVIS network was applied to each segment of
roadway inthe speed network. The traffic count and speed data for each roadway segment were then
combined into areawideperformance measures.
Step 3. Estimate Traffic Volumes for Shorter Time Intervals
The third step was to estimate traffic volumes for 15 -minute time intervals for each day of the week to
match with the time aggregation nfthe speed data.
Typical time -of -day traffic distribution profiles are needed to estimate hourly traffic flows from average
daily traffic volumes. Previous analytical efforts"z have developed typical traffic profiles atthe hourly
level (the roadway traffic and inventory databases are used for a variety of traffic and economic
studies). These traffic distribution profiles were developed for the following different scenarios
(resulting in 16 unique profiles):
w Functional class: freeway and non -freeway
w Day type: weekday and weekend
m Traffic congestion level: percentage reduction in speed from free-flow (varies for freeways and
streets)
* Directionality: peak trafficinthe morning (AM), peak trafficinthe evening (P&1),approximately
equal traffic ineach peak
The 16 traffic distribution profiles shown in Exhibits A-2 through A-6 are considered to be very
comprehensive, as they were developed from 713 continuous traffic monitoring locations in urban areas
of 37 states.
I Roadway Usage Patterns: Urban Case Studies. Prepared for Volpe National Transportation Systerns Center and
Federal Highway Administration, July 2Z'1994.
zDevelopment of Diurnal Traffic Distribution and Daily, Peak and Off-peak Vehicle Speed Estimation Proceduresfor
Air Quality Planning. Final Report, Work Order V94-06 Prepared for Federal Highway Administration, April l996.
12%
10%
W 8%
E
> 6%
.9
2%
0%
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Hour of Day
--f-AM Peak, Freeway Weekday --ff-PM Peak, Freeway Weekday
- AM Peak, Non
-Freeway Weekday
,�kX—PM Peak, Non -Freeway Weekday
Exhibit A-3. Weekday Traffic Distribution Profile for Moderate Congestion
12%
10%
8%
E
0 6%
4%
4) 2%
LI
0%
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Hour of Day
-4— AM Peak, Freeway Weekday -G-PM Peak, Freeway Weekday
2015 Urb(m Mobility Scorecard Methodology A-5
httr.)://mjLo)b�iIit ti:�M-Li.edLt/ums/cori�estion-data/
12%
10%
51761111c 4 '!I • 11 E IIIIN I III: !III! 11111 1 11 . - I ..
0%
0:00 2:00 4:00
6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Hour of Day
-4-AM Peak, Freeway Weekday -0-PM Peak, Freeway Weekday
�',,AM Peak, Non -Freeway Weekday -44-PM Peak, Non -Freeway Weekday
12%
10%
2%
0%
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Hour of Day
-0- Freeway Weekend -0-Non-Freeway Weekend
2015 Urban Mobility Scorecard Methodology
littp:Ilmobility.tamu.edu/ums/congestion-data/
m
om
10%
Exhibit A-6. Weekday Traffic Distribution Profile for Severe Congestion and
Similar Speeds in Each Peak Period
2%
0%
mm nm mm mm 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
-�—Freeway --O-Non-Freeway
The next step inthe traffic flow assignment process istodetermine which ofthe 16traffic distribution
pro0|esshou|dheassignedtoeachXDNetworkroadvvay|ink("XDNetwork"isthe"Beographv"usedby
INRIX to define the roadways), such that the hourly traffic flows can be calculated from traffic count
data supplied by HPMS. The assignment should be as follows:
w Functional class: assign based onHPK45functional road class
o Freeway -access-controlled highways
• Non -freeway - all other major roads and streets
w Day type: assign volume profile based oneach day
o Weekday (Monday through Friday)
o Weekend (Saturday and Sunday)
0 Traffic congestion level: assign based on the peak period speed reduction percentage calculated
from the private sector speed data. The peak period speed reduction is calculated as follows:
1) Calculate a simple average peak period speed (add up all the morning and evening peak
period speeds and divide the total by the 8 periods in the eight peak hours) for each XD Network
20.15 Urban.Mobility Scorccat-d Methodology A-7
path using speed data from 6a.m.tolOam.(morning peak period) and 3 p.m.to7pm.
(evening peak period).
2) Calculate a free-flow speed during the light traffic hours (e.g., 10 p.m. to 5 a.m.) to be used as
the baseline for congestion ca|cu|a1ions.
3) Calculate the peak period speed reduction by dividing the average combined peak period
speed bythe free-flow speed.
Ai,eragePeak,
Speed = Period d (Eo A-1)
�
ReducmoFactor freeFxom/Speed
[10p.ma.to5 a.00.)
For Freeways:
o speed reduction factor ranging from 90% to1O0%(no tolow congestion)
o speed reduction factor ranging from 75%tn90%(moderate congestion)
o speed reduction factor less than 75Y6(severe congestion)
For Non -Freeways:
o speed reduction factor ranging from 80%to100%(no tolow congestion)
o speed reduction factor ranging from 65Y6to8O%(moderate congestion)
o speed reduction factor less than 65%(severe congestion)
w
Directionality: Assign this factor based on peak period speed differentials in the private sector
speeddatoset. The peak period speed differential iscalculated asfollows:
1) Calculate the average morning peak period speed (6 a.m. to 10 a.m.) and the average evening
peak period speed (9p.m.to7p.m.)
2)Assign thepeakpehodvo|umecurvebasedonthespeeddiffenaodai The lowest speed
determines the peak direction. Any section where the difference inthe morning and evening
peak period speeds is 6 mph or less will be assigned the even volume distribution.
2015 Urban Mobility Scorecard Methodology A,0
New to the 201S Urban Mobility Scorecord is the use of truck -only volume curves. Themixed+mhic|e
process is repeated to create 15 -minute truck volumes from daily truck volumes. However, much ofthe
necessary information (e.g., facility type, day type, and time of day peaking) have already been
determined inthe mixed -vehicle volume process. The eight truck -only profiles used to create the 15 -
minute truck volumes are shown in Exhibits A-7 through A-9. The truck -only profiles are identical for all
congestion levels.
�
�
0x00 2m0 4:00 s:OD 8:00 10:00 12:00 14:00 zsou 18:00 20:00 zzon
Hour mDay
BMW
&0%
--G—AM Peak --*—PM Peak --,-AM -PM Peak
111111111
0:00 zom 4m0 sxm 8x00 MOO 12x00 z*oo asoo 18:00 zoM 22:00
Hour of Day
2015 UrbanScorecard Methodology A-9
0%
Qmo Izm 4:00 6:00 8:00 10:00 12:00 14:08 16:00 18:00 2OOW 22:00
~4—Freeway --w—Non-Freeway
Step 4. Calculate Travel Time
The hourly speed and volume data were combined to calculate the total travel time for each 15 -minute
time period. The 15 -minute volume for each segment was multiplied by the corresponding travel time
to get a quantity of vehicle -hours; these were summed for all 24 hours across the entire urban area.
The calculation ofcongestion measures required establishing a congestion threshold, such that delay
was accumulated for any time period once the speeds are lower than the congestion threshold. There
has been considerable debate about the appropriate congestion thresholds, but for the purpose of the
UK4Smethodo|o8y the data were used to identify the speed at low volume conditions (for example, 10
p.m.to5amj. This speed is relatively high, but varies according to the roadway design characteristics.
An upper limit of 65 mph was placed on the freeway free-flow speed to maintain a reasonable estimate
of delay; no limit was placed on the arterial street free-flow speeds.
The mobility performance measures were calculated using the equations shown in the next section of
this methodology once the 15 -minute clataset of actual speeds, free-flow travel speeds and traffic
volumes was prepared.
20/5Ut-bun Mobility Sco/-ecurd Methodology A,lO
0; H^ ,r0,1 21miT•nor a•'r �.
The UMS methodology analyzes travel on all freeways and arterial streets in each urban area. In many
cases, the arterial streets are not maintained by the state DOT's so they are not included in the roadway
network GIS shapefile that is reported in HPMS (all roadway classes will eventually be added to the GIS
roadway shapefiles by the state DOTs as mandated by FHWA). A technique for handling the unmatched
sections of roadway was used in the 2015 UMS. The percentage of arterial streets that had INRIX speed
data is approximately 75 percent across the U.S. while the freeway match percentage is approximately
90 percent.
After the original conflation of the volume and speed networks in each urban area was completed, there
were unmatched volume sections of roadway and unmatched INRIX speed sections of roadway. After
reviewing how much speed data was unmatched in each urban area, it was decided that unmatched
data would be handled differently in urban areas over under one million in population versus areas over
• a1•.�
Areas Under One Million Population
The HPMS volume data for each urban area that was unmatched was separated into freeway and
arterial street sections. The HPMS sections of road were divided by each county in which the urban area
was located. If an urban area was located in two counties, the unmatched traffic volume data from each
county would be analyzed separately. The volume data were then aggregated such that it was treated
like one large traffic count for freeways and another for street sections.
The unmatched speed data were separated by county also. All of the speed data and free-flow speed
data were then averaged together to create a speed profile to represent the unmatched freeway
sections and unmatched street sections.
The volume data and the speed data were combined and Steps 1 through 6 were repeated for the
unmatched data in these smaller urban areas.
2015 Urban Mobility Scorecard Methodology A-11
lzttp://inobility.tan�u.edu/�in�slcon ,estian-data/
Areas Over One Million Population
in urban areas with populations over one million, the unmatched data were handled in one or two steps
depending onthe area. The core counties of these urban areas (these include the counties with at least
15 to 20 percent of the entire urban area's VIVIT) were treated differently because they tended to have
more unmatched speed data available than some of the more suburban counties.
|nthe suburban counties (non-core),where less than l5orZOpercent ufthe area's VK8Twas ina
particular county, the volume and speed data from those counties were treated the same as the data in
smaller urban areas with populations below one million discussed earlier. Steps 1through Gwere
repeated for the non-core counties ofthese urban areas.
in each of the core counties, all of the unmatched HPIVIS sections were gathered and ranked in order of
highest traffic density (VIVIT per lane -mile) down to lowest for both freeways and arterial streets. These
sections ofroadway were divided into three groups. The top J5percent ofthe |ane-mi|es,with highest
traffic density, were grouped together into the first set, The next Z5percent were grouped into a
second set and the remaining lane -miles were grouped into athird set.
Similar groupings were made with the unmatched speed data for each core county for both functional
classes ofroadway. The roadway sections ofunmatched speed data were ordered from most congested
toleast congested based ontheir Travel Time Index value. Since the lane -miles ufroadway for these
sections were not available with the INRIX speed data, the listing was divided into the same splits as the
traffic volume data (J5/25/5Opercent). (The Travel Time Index was used instead ofspeed because the
TTI includes both free-flow and actual speed).
The volume data from each of the 3 groups were matched with the corresponding group of speed data
and steps 1 through 6 were repeated for the unmatched data in the core counties.
20/5Urban Mobility Scorecard Methodology A-12
This section summarizes the methodology utilized to calculate many of the statistics shown in the Urban
Mobility Scorecard and is divided into three main sections containing information on the constant
values, variables and calculation steps of the main performance measures of the mobility database. Not
all ofthe measures are reported inthe 3DlSUrban Mobility Scorecard. |nsome cases, the measures
below were last reported in the 2012 Urban Mobility Report (UMR); this is noted in the pages that
WON
1. National Constants
2. Urban Area Constants and Inventory Values
3. Variable and Performance Measure Calculation Descriptions
1) Travel Delay
2) Annual Person Delay
3\ Annual Delay per Auto Commuter
4) Total Peak Period Travel Time (last reported in2O22UMR)
5\ Travel Time Index
6) Commuter Stress Index
7\ Planning Time Index
8) Carbon Dioxide (CO2) Production and Wasted Fuel (CO2 last reported in 2012 UMR)
9) Total Congestion Cost and Truck Congestion Cost
10\ Truck Commodity Value (last reported in2O120MR)
11) Number of Rush Hours
12\ Percent ofDaily and Peak Travel inCongested Conditions
13\ Percent ofCongested Travel
Generally, the sections are listed in the order that they will be needed to complete all calculations.
&-l�
20/5[�bon&/ob/8ty3corMethodology
The congestion calculations utilize the values in Exhibit A-10 as national constants—values used in all
urban areas to estimate the effect of congestion.
ConsValue
Constant
Vehicle Occupancy 1,25 persons per vehicle
Average Cost of Time ($2014) (2) $17.67 per person hour'
Commercial Vehicle Operating Cost ($2014) (3) $94.04 per vehicle hour'
Total Travel Days (7x52) 364 days
zAdjusted annually using the Consumer Price Index.
Vehicle Occupancy
The average number of persons in each vehicle during peak period travel is 1.25.
Working Days and Weeks
With the addition ofthe |NR|Xspeed data inthe 3D11UMR the calculations are based nnofull year uf
data that includes all days mfthe week rather than just the working days. The delay from each day of
the week ismultiplied by5Zwork weeks toannualize the delay. Total delay for the year isbased on364
total travel days inthe year.
Average Cost of Time
The 2014 value of person time used in the report is $17.67 per hour based on the value of time, rather
than the average or prevailing wage rate (2).
Commercial Vehicle Operating Cost
Truck travel time and operating costs (excluding diesel costs) are valued at $94.04 per hour (3).
A�l4
20/5L�bun&&r6i�h/Scorecard D�cthodu|ogy
In addition to the national constants, four urbanized area or state specific values were identified and
used in the congestion cost estimate calculations.
Daily Vehicle -Miles ofTravel
The daily vehicle-rniles of travel (DVIVIT) is the average daily traff ic (ADT) of a section of roadway
multiplied bythe length (in miles) qfthat section ofroadway. This allows the daily volume ofall urban
facilities to be presented in terms that can be utilized in cost calculations. DVIVIT was estimated for the
freeways and principal arterial streets located in each urbanized study area. These estimates originate
from the HPIVISdatabase and other local transportation data sources.
Population, Peak Travelers and Commuters
Population data were obtained from a combination of U.S. Census Bureau estimates and the Federal
Highway Administration's Highway Performance Monitoring System (HPK4S) (14). Estimates of peak
period travelers are derived from the National Household Travel Survey (NHTS) (5) data on the time of
day when trips begin. Any resident who begins atrip, byany mode, between 6am.and 1Oam.nr3
p.m.and 7p.m.iaapeak-period traveler. Data are available for many ofthe major urban areas and a
few ofthe smaller areas. Averages for areas ofsimilar size are used incities with nospecific data. The
traveler estimate for some regions (e.g.,high tourism areas) may not represent all of the transportation
users on an average day. The same NHTS data were also used to estimate the commuters who were
traveling during the peak periods by private vehicle—a subset of the peak period travelers.
Fuel Costs
Statewide average fuel cost estimates were obtained from daily fuel price data published by the
American Automobile Association (AAA) (6). Values for gasoline and diesel are reported separately.
Truck Percentage
The percentage of passenger cars and trucks for each urban area was estimated from the Highway
Performance Monitoring Systern clataset (1). The values are used to estimate congestion costs and are
not used to adjust the roadway capacity.
2015 Urban Mobility Scorecard Methodology A,15
The major calculation products are described in this section. In some cases the process requires the use
of variables described elsewhere in this methodology.
Travel Delay
Most of the basic performance measures presented in the 2015 Urban Mobility Scorecord are developed
in the process of calculating travel delay—the amount of extra time spent traveling due to congestion.
The travel delay calculations have been greatly simplified with the addition of the INRIX speed data, This
speed data reflects the effects of both recurring delay (or usual) and incident delay (crashes, vehicle
breakdowns, etc.). The delay calculations are performed at the individual roadway section level and for
each hour of the week. Depending on the application, the delay can be aggregated into summaries such
asweekday peak period, weekend, weekday off-peak period, etc. Any observed speed faster than the
free-flow speed is changed to the free-flow speed so that delay is zero, rather than providing a 'delay
credit' (negative delay value) to the calculation.
(Dally,VoTiclo -Mi' es)
Daily Vehicle -Hours
of Delay Sp eed
Annual Person Delay
_(Dof Travel
2i"'Vehice- "" i "I s
This calculation is performed to expand the daily vehicle -hours of delay estimates for freeways and
arterial streets toayearly estimate ineach study area. Tocalculate the annual person -hours ofdelay,
multiply each day -of -the -week delay estimate by the average vehicle occupancy (1.25 persons per
vehicle) and by52weeks per year (Equation A,3).
Annual Daily � ~ 1.25 Persons
Persons -Boors= of Delay �oo X52Weeks X ( per Vehicle ` '
of Delay Fnvys and Arterial Streets
Annual Delay per Auto Commuter
Annual delay per auto commuter is a measure of the extra travel time endured throughout the year by
auto commuters who make trips during the peak period. The procedure used inthe Urban Mobility
Scorecard applies estimates of the number of people and trip departure times during the morning and
2015 Urban Mobility Scorecard Methodology A-lh
evening peak periods from the National Household Travel Survey (5) to the urban area population
estimate to derive the average number of auto commuters and number of travelers during the peak
periods (7).
The delay calculated for each commuter comes from delay during peak commute times and delay that
occurs during other times ofthe day, All nfthe delay that occurs during the peak hours ofthe day (6:0O
a.m. to 10:00 a.m. and 3:00 p.m. to 7:00 p.m.) is assigned to the pool of commuters. |naddition tothis,
the delay that occurs Outside of the peak period is assigned to the entire population of the urban area.
Equation A~4shows how the delay per auto commuter iscalculated. The reason that the off-peak delay
is also assigned to the commuters is that their trips are not limited to just peak driving times but they
also contribute to the delay that occurs during other times of the weekdays and the weekends.
(Remaining Delay)
Delay per =Peak Period Dell f �u&�l � Auto Commuters Population
Auto Commuter
Totol Peok Period Trovel Time (Lost reported in the 2012 UMR)
Total travel time isthe sum oftravel delay and free-flow travel time. |nthe 2[U2Urbonmobility Report,
both quantities are calculated for freeways, arterial, collector, and local streets. Previously, peak period
travel time excluded collector and local streets because data were largely unavailable and incomplete.
Though still sparse, these data elements have been included this year, offering a refinement to previous
efforts. As data become more available, so will the measure's refinement.
For this report, the four roadway classifications have been grouped into two primary categories: primary
roads (freeways and arterials) and minor roads (collectors and local streets).
Total peak period daily delay is the amount of extra time spent traveling during the morning peak hours
of 6:00 a.m. and 10:00 a.m. and the evening peak hours of 3:00 p,m. and 7:00 p.m. due to congestion.
Equation A-5 is modeled after Equation A-2 but includes factors to convert daily delay into peak period
delay and vehicle -hours into a person hours.
| ! Daily >9��m"�n��
Pea�Per`nd
�25Persu
ua
of Travel � �O��7�� x
(Eq. A-5)Du0yD�ay = per �� �
Speed �re�-��m*����v
/ During the Peak
A�l7
20/JUrban 8&/b/Dty Scorecard Methodology
Total peak period free-flow travel time is the amount of time needed to travel the roadway section
length at the free-flow speeds (provided by INRIX for each roadway section) during the day's peak hours
(Equation A-6). Equation A,6converts vehicle hours toperson hours.
Peak Free -Flow 1 Daily Pei -cent ofVehicle
=—�1.25 Persons
Travel Time per Vehicle ��e�c\�����u eao��x
[Bg. A 6:)
(Person -Hours) 7ravelSpeed uTravel During the Peak
Peak period travel time is the sum of peak period delay and free-flow travel time for each roadway type
(both primary and minor roads) (Equation A-7). The metric considers commuters rather than the total
population to reflect actual travel time for those experiencing the worst congestion.
Road Minor RoadTot�DaDy Pw�c
Period Peak Delay Peak Delay I
0�
Travel Time � X (8g. A-7)
Auto Commuters Minutes
Commuter) Cmmmuter} l /
Travel Time Index
The Travel Time Index (TTI) compares peak period travel time to free-flow travel time. The Travel Ume
Index includes both recurring and incident conditions and is, therefore, anestimate ofthe conditions
faced by urban travelers. Equation A85 illustrates the ratio used to calculate the TTI. The ratio has units
oftime divided bytime and the Index, therefore, has nounits. This "unit|ess"feature allows the index
to be used to compare trips of different lengths to estimate the travel time in excess of that experienced
in free-flow conditions.
The free-flow travel time for each functional class is subtracted from the average travel time to estimate
delay. The Travel Time Index is calculated by comparing total travel time to the free-flow travel time
(Equations A-8 and A-9).
Travel Time Index =
Travel Time Index =
Peak Travel Time
Free -Flow Travel Time
DelayTtme f Free -Flow Travel Time
Free -Flow Travel Time
(Eq. A-9)
20/5Urban Mobility Scorecard Methodology A-10
The change in Travel Time Index values is computed by subtracting 1.0 from all the TTI values so that the
resulting values represent the change in extra travel time rather than the change in the numerical TTI
values. For example, the increase in extra travel time from a TTI of 1.25 to 1.50 is 100 percent (extra
travel time of 50 percent compared to 25 percent).
Comm Liter Stress Index
The Commuter Stress index (CSI) is the same as the TTI except that it includes only the travel in the peak
directions during the peak periods; the TTI includes travel in all directions during the peak period. Thus,
the CSI is more indicative of the work trip experienced by each commuter on a daily basis.
Planning Time Index (Freeway Only)
The Planning Time Index (PTI) was new beginning with the 2012 Urban Mobility Report. Results are
shown inTable 3ofthe 202SUrban Mobility Icorecord The PTI isbased onthe idea that travelers want
to be on-time for an important trip 19 out of 20 times; so one would be late to work only one day per
month (on-time for 19 out of2Owork days each munth). For example, a PTI value of 1.80 indicates that
a traveler should allow 36 minutes to make an important trip that takes 20 minutes in low traffic
volumes. The PTI values inTable 3are for freeways only.
The PTI is the 95th percentile travel time relative to the free-flow travel time as shown in Equation A-10.
The2Ol5UrbonMob0ityScurecordestimatesLhePT|fo/thpsusingoverage|ink(XDNetwork|ink)
freeway PTI values. Researchers compute these trip PTI estimates using Equation A-11, which is from the
Strategic Highway Research Program, 2 (SHRP2) Analytical Proceduresfor Determining the Impacts of
Reliability Mitigation Strategies (8).
9SthPercentile Travel Time
PlanningTIme
Index (PTI) Free -Flow Travel Time
(Eq. A-10)
PTItri°= (E�A-11)
Where:
PT|mn = PTI for atrip (reported for freeways inTable 3mfthe 2D25UMI);and
PT|o"k = Average of PTIs for all the XD Network links weighted by VIVIT in the urban area.
J0/JUrban Mobility Scorecard Methodology
Exhibit A-11 illustrates a distribution of travel times for a morning commute. Travel times can vary over
acalendar year; the extreme cases usually have identifiable causes. italso quantifies and illustrates the
ne|adonshipbetweenthehee-Mowtrav6tinne,averaQet/ave|time,O»mpercend|etrave|Lime,and95m
percentile travel time.
Carbon Dioxide /COz Production and Wasted Fuel /COzwas lost reported /n2O12UMR/
This methodology uses data from the United States Environmental Protection Agency's (EPA) MOtor
Vehicle Emission Simulator (K8OVES)model. MOVES isamodel developed bythe EPA toestimate
emissions from mobile sources. Researchers primarily used MOVES to obtain vehicle emission rates,
climate data, and vehicle fleet composition data.
The methodology uses data from three primary data sources: 1)the FHVVA's HPMS, 2) |NR|Xtraffic
speed data, and 3)EPA's MOVES model. Five steps are implemented \nthe methodology:
l. Group Similar Urban Areas — considers seasonal variations and the percentage of travel that
occurs with the air conditioner "on,"which impacts OJzproduction.
2. obtain [OzEmission Rates for Urban Area Group—emission rates (in grams per mile) were
created for each of the 14 groups from Step #1.
I Fit Curves to CO2 Emission Rates — curves were created relating speed and emission rates from
Step #2.
4. Calculate CO2 Emissions and Fuel Consumption During Congested Conditions — combine speed,
volume and emission rates to calculate emissions during congested conditions. Estimate fuel
consumption using factors that relate the amount of gas (or diesel for trucks) produced for the
CO2 emissions produced.
S. Estimate the CO2 Emissions and Fuel Consumption During Free-flow Conditions, and Estimate
Wasted Fuel and CO2 Due to Congestion — repeat the calculations from Step #4 using the free-
flow speeds when few cars are on the road. Free-flow results are subtracted from congested -
conditions results to obtain CO2 emissions and fuel wasted due to congestion.
2015 Urban.Mobili�y Scorecard Methodology A-2
I
20 30 40 50 60
Your Commute Time to Work
(minutes),
Exhibit A-11. Example of Morning Commute Travel Time Distribution
o
Is Your Morning Commute Time the Same Each Day? — No, It Varies!
20 minutes is your free-flow travel time (Commute time when few other cars are on the road)
# of Days
o
in the
Year
1 30 minutes is your average travel time (of all 250 morning workday trips in the year)
38 minutes is the worst day of the week- Allow this much time to only be late for
10a
workone day a week (sometimes called the 80m percentile travel time)
A
o
You have to leave home
0,
by 7a,m- to be sure that
L
80
de Holiday
Federal
q Federal Holiday — you are at your job by 8
--u speed
y o ed to we a m - (sometimes called
work you speed to vo ! I Several of your
-
n u le
in 22 minutes
n 22 ", I the 95th percentile travel
trips from April to
t ffl,
because traffic is
because
b ca ra IS
tine, allowing this much
August were
tight!
lightt time ensures you plan
delayed by
60
the worst day of the
construction
-for
month)- Note, that this is
2 lanes were 3 times yourfree-flow
closed by a travel time,
40t
multi -vehicle
crash on
Last before the December 8th Who forget
year, can
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that Jan 17th
downpour tan
was better then
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20
--
I
20 30 40 50 60
Your Commute Time to Work
(minutes),
Step 1. Group Similar Urban Areas
For some pollutants, the influence of weather conditions causes vehicle tail -pipe emissions to vary
considerably bylocation. Tail -pipe CDzemissions, however, are not directly influenced byweather
conditions, although they still vary by location because they are influenced by air conditioning use.
Traveling with the air conditioner turned "on" lowers fuel efficiency and increases CO2 emission rates.
Thus, locations with warmer climates typically have higher emission rates because more travel occurs
with the air conditioner turned "un."
It was not feasible to use emission rates for every county in the United States, yo researchers instead
created representative climate -type groups to account for the impact of climate on CO2 emission rates.
To create these groups, TTI researchers grouped the UIVIR urban areas based on similar, seasonal
"ACo n Fraction" (ACF) values -- a term used in MOVES to indicate the fraction of travel that occurs with
the air conditioner turned "on." For example, a vehicle traveling 100 miles with an ACF of 11 percent
would travel 11mfthose 1OOmiles with the air conditioner turned "on."
Because ACF is a factor of temperature and relative humidity, researchers collected hourly temperature
and relative humidity data fora county within each urban area included in TT|'s UMRhnm the MOVES
database. Researchers collected the climate data by county, rather than urban area (or city), because
the MOVES database only has climate data available bycounty.
For simplicity, one county per urban area (or city) was selected because the climate differences between
adjacent counties were not significant.
TTI researchers used methods similar to those used in MOVES to calculate the seasonal "ACon Fraction"
(A[F)for each county. Researchers developed seasonal ACFs based on hourly temperature and relative
humidity data from MOVES. They used this hourly data tocalculate hourly ACFs,which they then
weighted by hourly traffic volume data from MOVES and averaged for each month. Toproduce the
weighted seasona|A[Fs, researchers averaged these weighted month|yACFs over three-month periods
for the seasons defined byMOVES.
Togroup the counties (or urban areas) based onsimilar seasonal climates, researchers used
temperature and relative humidity scatter plots to visually identify which counties had similar climates.
To refine the tentative groups, researchers previewed each group's average seasonal ACF values and
removed any counties that differed from the group averages. The standard towhich researchers
Z015CJrhonMobility Scorccoo/Methodology A-22
allowed a county to vary from the average was approximately 5 to 10 percent or less. Researchers
determined this mar -gin for error during the grouping process based on the need to create a manageable
number ofgroups without sacrificing accuracy. Several counties did not share similar seasonal A[F
values with any group, so they retained their original values and would be calculated individually.
Exhibit A,12shows the groupings ofurban areas.
MGroup 4
Independent Groups:
IM Groups
Anchorage, AK
EN Group
Honolulu, Hi
EM Group
San Francisco, CA
M Gmmpx
SanJuan.PR
11111 Group
| Seattle, WA
TD researchers used MOVES to produce emission rates for different vehicle types and locations.
Researchers used these emission rates by combining them with volume and speed data to incorporate
COz emissions as described in Step 4. Researchers produced emission rates for every ACF value assigned
to the groups in Step 1. For each ACF value, researchers produced emission rates for each vehicle type,
fuel type, and road type used in the LIMR.
2015 Urban Mobility IeorecvrdMetbodology A-23
MOVES has many different vehicle classifications, but TTI's UMR has just three broad categories: light-
duty vehicles, medium -duty trucks, and heavy-duty trucks. To obtain emission rates, researchers
selected MOVES vehicle types that were most similar to the vehicle types of the UMR.
Multiple "SouroeTypes' from MOVES meet the description ofeach vehicle type used in TT|'s UMR (light-
duty vehicles,
|ightdutymehi6e4 medium -duty trucks, and heavy-duty trucks). For example, both the combination short -
haul and combination long-haul trucks qualify as heavy-duty trucks.
hort-hau|andcombinadon|ong-hau|tmcksquo|Kyasheavy+dutytrucks. Rather than weighting the emission
rates of every "SourceType," researchers selected a single "SourceType" to supply emission rates for
each UMR vehicle type because many "SourceTypes" have similar emission rates (light-duty vehicles are
anexception, hmmever). Tndetermine which "5ouroeType"would supply the emission rates for a
vehicle type, researchers chose the "SOUrceType" with the highest percentage of vehicle -miles of travel
(VMT) within each UMR vehicle type.
TU researchers used a different method for light-duty vehicles because not all "SouroeTypes within this
classification have similar emission rates. The light-duty vehicle classification consists ofpassenger cars,
passenger trucks, and light commercial trucks. Passenger trucks and light commercial trucks have
similar emission rates, but passenger car emission rates are substantially different. To create one set of
emission rates for this vehicle type (light-duty vehicles), researchers combined and weighted the
emission rates of two different "SourceTypes" — passenger cars (59%) and passenger trucks (41%).
Researche/susedon|ythepassengertruck"SourceType"tosupp|ytheemissionnatesforboth
passenger trucks and light commercial trucks because they have similar emission rates, and because
passenger trucks account for more VIVIT.
Emission rates also differ for specific fuel types, and TTI researchers selected afuel type for each vehicle
type based on fuel usage data in MOVES. Given that light commercial trucks account for asmall portion
of the light-duty vehicle population, researchers used the gasoline emission rates to represent all fuel
usage for light-duty vehicles when calculating emissions. Researchers used the diesel emission rates to
represent all fuel usage for medium-cluty trucks and heavy-duty trucks.
TTI researchers ran MOVES for the appropriate vehicle types, fuel types, and road types to obtain
emission rates ingrams per mile.
20/5{rbuoMmbilbvScororurdMethodology /\-24
TTI researchers developed curves to calculate emission rates for a given speed. Researchers later used
the equations for each curve to calculate emissions.
MOVES produces emission rates for speeds of 2.5 to 75 mph in increments of five (except for 2.5 mph).
Using Microsoft ExcelO, researchers initially constructed speed -dependent emission factor curves by
fitting one to three polynomial curves (spline) to the emission rate data from MOVES (see Exhibit A-13
example). Researchers compared emission rates generated with the polynomial spline to the underlying
MOVES -generated emission rates.
Exhibit A-13. Example Light-duty Vehicle Emission Rate Curve -set
Showing Three Emission Rate Curves
o
n 10 20 30 40 so 60 m 80
The polynomial spline that was deemed sufficiently accurate by researchers was a two -segment spline
using one 6t' -order polynomial for the 0 — 30 mph segment and another 6 th -order polynomial for the 30
—GOmphsegment Speeds over 5Oused the emission rates ofthe 3O-5Omph polynomial atGOmph.
Note that these speeds are averages, and variability with speed (slope) isne8|igab|efor speeds greater
than 6Omph. Lower average speeds have higher speed fluctations (or more stop -and -go), which causes
higher emission rates. From a[Ozperspective, these slower speeds are ofgreat concern. Because
there are fewer speed fluctuations at higher speeds, which results in a more efficient systemoperation,
it is desirable for urban areas to operate during the relatively free-flow conditions as much as possible.
Thus, the authors capped emissions generation at approximately 60 mph.
2015OrMobility Methodology A-25
To calculate emissions, researchers combined the emission rates with hourly speed data supplied by
|NR|Xand hourly volume data supplied byHighway Performance Monitoring System(HPIVIB).
Researchers used SAS@ to automate the process of calculating emissions. This processinvolves selecting
the appropriate emission rate equations (or curves), using the speed data to calculate emission rates,
and combining the volume data with the emission rates to calculate emissions.
The volume and speed data are structured for each 15 -minutes for each day of the week. This means
there will be a separate speed and volume value for light-duty vehicles, medium -duty trucks, and heavy-
dutytrucksforeach15-minu1esofeachdayoftheweek. Toaccount for the seasonal climate changes,
researchers calculated a separate emission rate for each season.
After calculating the emission rates, researchers combined these emission rates with the volume data to
calculate emissions for each season. Lastly, researchers sum the emissions ofeach season, vehicle type,
and day of the week to produce the annual emission estimates
Researchers produced the annual emission estimates for congested conditions, which includes fnee-
f|mw. Researchers used factors that relate CO2 emissions from a gallon of gasoline (8,887 grams
CO2/gallon) and diesel (10,180 grams CO2/gallon), in relation with the vehicle types and associated fuel
type used, to estimate fuel consumption during congestion conditions, which includes free-flow.
Estimate Wasted Fuel an -d CO2 Due to Congestion
Researchers repeated the calculations in Step #4 using the speeds when few cars are on the road to
estimate free-flow emissions and fuel consumption. Toestimate the COzemissionsfrom congestion,
researchers subtracted the free-flow condition emissions estimates from the congested -conditions
emissions estimate from Step #4. This isshown inEquation A,1Z. Toestimate wasted fuel due to
congestion, researchers subtracted the fuel consumed during free-flow from the fuel used during
congested conditions (Equation A,13).
Annual AdditionalCO2 Annual CO2 AnnualCO2Because of =Bm��Pm��—Bm����� (Eq. A-12)Congrestion in Congestion in Free-FloNv Conditions
A-2
2015Urbon/Wob/8ry3oorMethodology 6
Annual Fuel �������
Annual Fuel
Wasted ioCongestion = Consumedio — Womd�heCo�suzued (�g ��13)
Congestion ioFree-Flow Conditions
A Word about Assumptions in the [& and Fuel Methodology
Table 4 of the main 2012 Urban Mobility Report presents the results of the steps above. Table 4reports
the total millions of pounds Of CO2 emissions that occur during free-flow in each urban area, which is a
result nfStep l The additional results of Step 5 (additional emissions because of congestion) are
reported in Table 4 in pounds per auto commuter and millions of pounds for each urban area. As shown
in Table 4, the emissions produced during congestion are only about 3 percent (frorn all 498 urban
areas) of emissions produced during free-flow.
A number of national -level assumptions are used as model inputs (e.g., volume, speed, vehicle
composition, fuel types).This analysis also only includes freeways and principal arterial streets.
The assumptions allow for a relatively simple and replicable methodology for each urban area. More
detailed and localized inputs and analyses are conducted by local or state agencies; those are better
estimates Of CO2 production.
The analysis is based upon the urban area boundaries which are a function of state and local agency
updates. Localized CO2 inventory analyses will likely include other/all roadways (including collectors and
local streets) and will likely have a different area boundary (e.g., often based upon metropolitan
statistical area).
Finally, Step 5 uses the difference between actual congested -condition CO2 emissions and free-flow CO2
emissions and fuel consumption. According tothe methodology, this difference isthe "wasted"fuel and
"addbiona|"[Ozproduced due tocongestion. Some may note that ifthe congestion were not present,
speeds would be higher, throughput would increase, and this would generally result in lower fuel
consumption and CO2 emissions —thus the methodology could be seen as overestimating the wasted
fuel and additional [Dzproduced due tocongestion. Similarly, Uthere issubstantial induced demand
due to the lack of congestion, it is possible that more CO2 could be present than during congested
conditions because of more cars traveling atfree'Oovv. While these are notable considerations and may
be true for specific corridors, the UMS analysis is at the areawide level for all principal arterials and
freeways and the assumption is that overestimating and underestimating will approximately balance out
2015 Urban Mobility Scorecard Methodology }\-27
i
over the urban area. Therefore, the methodology provides a credible method for consistent and
replicable analysis across all urban areas.
Total Congestion Cost and Truck Fuel Cost
Two cost components are associated with congestion: delay cost and fuel cost. These values are
directly related to the travel speed calculations. The following sections and Equations A-14 through A-
16 show how to calculate the cost of delay and fuel effects of congestion.
Passenger Vehicle Delay Cost. The delay cost is an estimate of the value of lost time in passenger
vehicles in congestion. Equation A-14 shows how to calculate the passenger vehicle delay costs that
result from lost time.
Annual Psi -Veh Daily Psgr Vehicle Value of Vehicle Annual
Delay Cost Hours of Delay x Person Time x Occupancy X Conversion (Eq. A-14)
(Eq. A-4) ($ / hour) (pees/vehicle) Factor
Passenger Vehicle Fuel Cost. Fuel cost due to congestion is calculated for passenger vehicles in
Equation A-15. This is done by associating the wasted fuel, the percentage of the vehicle mix that is
passenger, and the fuel costs.
Annual Daily Fuel Percent of
= Wasted X Passenger X
Fuel Cost (Eq. A-13) Vehicles
Gasoline X Annual (Eq� A-15)
Cost Conversion Factor
Truck or Commercial Vehicle Delay Cost. The delay cost is an estimate of the value of lost time in
commercial vehicles and the increased operating costs of commercial vehicles in congestion. Equation
A-16 shows how to calculate the passenger vehicle delay costs that result from lost time.
Annual Comm -Veli Daily Corms Vehicle Value of Annual
Hours of Delay x Comm Vehicle Time x Conversion
Delay Cost (Eq. A-4) , ($ / hour) Factor (Eq. A-16)
20.15 Urban Mobility Scorecard Methodology A-28
htt-p://mobility.tallILI.edu/ums/con(-Y,egtioii-dat,t/
Truck or Commercial Vehicle Fuel Cost. Fuel cost due to congestion is calculated for commercial
vehicles in Equation A-16. This is done by associating the wasted fuel, the percentage of the vehicle mix
that iscommercial, and the fuel costs.
�v��w Percent �noumQ Daily Diesel
= �oo�a
-��)
�ae��mxt Wasted � Commercial � Cost X Conversion Factor [2q. &
(Eq. A-13) Vehicles
Total Congestion Cost. Equation A-I8combines the cost due totravel delay and wasted fuel to
determine the annual cost due to congestion resulting from incident and recurring delay.
Annual Cost
Annual Comm
Annual Comm
Due to =Vehicle
DelayCostf
Fuel Cost
f Veh DelkyCost f
Veh Fuel Coot (E q. A- 18)
Truck Commodity Volue(Lust reported in 2013UM8
The data for this performance measure came from the Freight Analysis Framework (FAF) and the
Highway Performance Monitoring System (HPMS) from the Federal Highway Administration. The basis
of this measure is the integration of the commodity value supplied by FAF and the truck vehicle -miles of
travel (VMT) calculated from the HPMS roadway inventory database.
There are 5 steps involved in calculating the truck commodity value for each urban area.
1. Calculate the national commodity value for all truck movements
2. Calculate the HPK8Struck VMTpercentages for states, urban areas and rural roadways
S. Estimate the state and urban commodity values using the HPMStruck VK8T percentages
4. Calculate the truck commodity value of origins and destinations for each urban area
5. Average the VMT-based commodity value with the origin/destination-based commodity value
for each urban area.
Step 1 - National Truck Commodity Value. The FAF(version 3)database has truck commodity values
that originate and end in 131 regions of the U.S. The database contains a 131 by 131 matrix of truck
goods movements (tons and dollars) between these regions. Using just the value of the commodities
that originate within the 131 regions, the value of the commodities moving within the 131 regions is
determined (if the value of the commodities destined for the 131 regions was included also, the
commodity values would bedoub|e-oounted). The EAFdatabase has commodity value estimates for
different years. The base year for FAF-3 is 2007 with estimates of commodity values in 2010 through
2O4Oin5'yearincrements.
20/5Urban 8do6ilitrScorecard Methodology A-29
Step 2 —Truck VIVIT Percentages. The HPMS state truck VMT percentages are calculated in Equation A-
19 using each state's estimated truck VMT and the national truck VMT. This percentage will be used to
approximate total commodity value at the state level.
State Truck State Truck VIVIT,
VMT Percentage = ( U. S. Truck VMT - X 100%
(Eq. A-19)
The urban percentages within each state are calculated similarly, but with respect to the state VMT. The
equation used for the urban percentage is given in Equation A-20. The rural truck VMT percentage for
each state is shown in Equation A-21.
(State Ub3n
State Urban Truck. VrIT X �100% (Eq. A-20)
Truck: VMT Percentage St Tru(_
k
Ti ate _ )
VI'JT
State Rural Truck = 100% — State Urban Truck (Eq. A-21)
VMT Percentage VIAT Percentage
The urban area truck VMT percentage is used in the final calculation. The truck VMT in each urban area
in a given state is divided by all of the urban truck VMT for the state (Equation A-20).
U 'ban
Area
Urban Area Truck
Truck
ck VIJT
(Eq. A-22)
VMT Percentage 'State
e Urb all)
Tn ck VL,`JT
Step 3 — Estimate State and Urban Area VIVIT from Truck VMT percentages. The national estimate of
truck commodity value from Step 1 is used with the percentages calculated in Step 2 to assign a VMT-
based commodity value to the urban and rural roadways within each state and to each urban area.
State Urban Truck U� S. Truck State Urban
VMT-Based. Commodity Value X Truck Percentage (Eq� A-23)
Commodity Value
State Rural Truck U� S. Truck State Rural
VMT-Based Commodity Value X , Truck Percentage (Eq. A-24)
Commodity Value
2015 Urban Mobility Scorecard Methodology A-30
htti)://mobilitv.tamu.edu/ums/con�4estion-data/
Urban Area Truck State Urban Urban Area �£u ��6\
VB�TBmseJ = Truck VMT-BasedX ,�^� ,
Truck VD&T9ercoxtage
Commodity Value Commodity Va ue
Step 4—Calculate OhQjn/Dest|nadon-BasedCommodity Value. The results inStep Sshow the
commodity values for the U.S. distributed based on the truck VIVIT flowing through states in both rural
portions and urban areas. The Step 3results place equal weighting onatruck mile inarural area and a
truck mile inanurban area. Step 4redistributes the truck commodity values with more emphasis placed
on the urban regions where the majority of the truck trips were originating or ending.
The value of commodities with trips that began or ended in each of the 131 FAF regions was calculated
and the results were combined toget atotal for the U.S. The percentage ofthe total U.S.origin/
destination -based commodity values corresponding to each of the FAF regions, shown in Equations A-26
andA,Z7,vvasca|cu|atedand1hesepercentageswereusedioredisthbutethena1iona|keight
commodity value estimated in Step 1 that were based only on the origin -based commodities, Equation
A-28 shows that this redistribution was first done at the state level by summing the FAF regions within
each state. After the new state commodity values were calculated, the commodity values were
assigned to each urban area within each state based on the new percentages calculated from the
ohgin/destinat\on-basedcommodity data. Urban areas not included inaFAFregion were assigned a
commodity value based on their truck VMT relative to all the truck VIVIT which remained unassigned to a
FAFregion (Equation A-29).
FAF Deuion
Value)
����e�h�o = x1O096 (Eq.A-26)
O/�'�a�e�Cmc�oz�d�v`J�1oe96 UCo modity ���U/U-�aueo
' ���~ \
med Co
FAF Region O/D -Based l���(�-B�d x D. S. O/D -Based (Eq. '
��
= Commodity Value Commodity Value 96 Commodity Value
O/D -Based = FAF Region + fAJF Region (Eq. A-28)
Commodity Value for State 1 Value from State 1 Value from State 1
7��
�oeBAFRe��
ou ��muog Unassigned
Ucban�reaQ/0~B�ed = �m�I FAF O/D -Based � VMT Percentage (Eq. ��2��
�oozmmdityl�Iuefrom State Coozmm«�tyV�um t
TruckRemaining TPercentage
/
A-3120I5{�bun&b/6/8Methodology
Step G—Final Commodity Value for Each Urban Area. The VMT-based commodity value and the O/D -
based commodity value were averaged for each urban area to create the final commodity value to be
presented inthe Urban Mobility Report.
Final Commodity Urban Area
Value for = VMT-Based f
Urban Area (Commodity Value
Urban Area
O/D -Based 2
Value
CCommodityvauu=
Number uf"Rush Hours" (Congested Hours),Congested LoneMJes,and Congested NM7
The number of"rush hours" (congested hours) is computed with a new method inthe 2O15 Urban
Mobility Scorecard. For each XID Network directional roadway link the 15 -minute average speeds during
the peak eight hours are evaluated for all five weekdays. If any 15 -minute speed is less than 90 percent
of the uncongested speed on a freeway, or less than 75 percent of the uncongested speed on an arterial,
the section of road is marked as "congested" for that 15 -minute period (9). if 30 percent of the urban
area freeway system is congested, the 15 -minute period is considered congested. Similarly, if 50 percent
of the arterial road sections across the urban area are congested, the associated 15 -minute period is
considered congested. The number of congested 15 -minute periods across the urban area (freeway or
arterial) are summed to determine the urban area congested hours ("rush hours") (10).
Congested lane -miles are similarly identified; speed below congestion threshold (90 percent/75 percent
of uncongested speed on freeways/arterials). These lane -miles are summed for those time periods
across the urban area separately for freeways and arterials. Congested vehicle -miles of travel is also
summed for each 15 -minute period for urban area freeways and arterial streets. These summations uf
peak period vehicle -miles of travel and lane -miles are compared with the peak -period totals to
determine the percent that is congested.
A,32
20/5[�6un/Uo6/8(yScorecard �8othodoiogy
RZWJ�
1 Federal Highway Administration. "Highway Performance Monitoring System,"18O2tn2O1OData.
November 2012. Available:
2 McFarland, VKF.M. Chui"The Value ofTravel Time: New Estimates Developed Using aSpeed
[hoiceModei^ Transportation Research Record N.l1l6,Transportation Research Board,
Washington, D.[,1887.
3 Ellis, David, "Cost Per Hour and Value ofTime Calculations for Passenger Vehicles and Commercial
Trucks for Use inthe Urban Mobility Report." Texas Transportation |nstitute,2UO9.
4 Populations Estimates. U.S.Census Bureau. Available:
5 2O09National Household Travel Survey, Summary ofTravel Tnends. Available:
6 American Automobile Association, Fuel Gauge Report. 2011. Available:
7 Means ofTransportation 1oWork. American Community Survey 2OO9. Available:
www.census.gov acs www
0 Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies. Strategic
Highway Research Program, 2 (SHRP2) Report S2-LO3-RR-1. National Research Council,
Transportation Research Board, Washington, DI,2O13.Available:
9 Turner, S,R.Wqargiotta,and lLomax. Lessons Learned:K8onUohngHighwayCongestion and
Reliability Using Archived Traffic DetectorData.FHVVA,HOP-0B-O03.Fede/a|HighwuyAdministration,
Washington, D.C., October 2004.
10 Estimates ofRelative Mobility inMajor Texas Cities, Texas Transportation |nstitute Research Report
313-1F, 1982.
2015 Urban Mobility Scorecard Methodology A'33