Loading...
The URL can be used to link to this page
Your browser does not support the video tag.
Home
My WebLink
About
Back-Up Document FR/SR
8/27/2015 New study confirms Miami traffic is really jammed up I Miami Herald Traffic _ AUGUST 25, 2015 New study confirms Miami traffic is really jammed up HIGHLIGHTS Miami is among the urban areas with the worst traffic congestion in the nation But it ranks better than Los Angeles and Washington D.C. Cities with better economies generate more traffic 04,105011101.10; 4641,11 kop, 111 , 40,""',1,100.01001! 44. 4, 444414, 04 INirgoecOtf,,„ M41 411111 „ 1',11ft,0111010-0401,140400,410$011,1t41? 01111,4111144001*000141 44,4 IN !finfilIVOIMIIIHNINfiefilkig„ http://www.miamiherald.com/news/traffic/article32365728.html 1/7 8/27/2015 New study confirms Miami traffic is really jammed up I Miami Herald 1 of 5 ALFONSO CHARDY achardy@elNuevoHerald.com 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 201 5 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 12th place of 15 urban areas with the worst traffic congestion in the nation. The 2012 report listed Miami in 11th 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 period. 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 and the goods and services and the commuters are out there moving on the roads now." Miami's drop in one rank does not mean traffic here is improving. "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 noticing," Schrank said. Over the years, traffic congestion in the region has fluctuated. In 2000, the Miami/South Florida region placed 12th and in 2008 it was 15th. 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, but generally showcase 15 because they are somewhat similar to each other in traffic conditions and sprawl. The Miami area is one of them. http://www.miamiherald.com/newsttraffic/article32365728.html 2/7 8/27/2015 New study confirms Miami traffic is really jammed up 1 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 and Seattle. Chicago and Houston were tied for eighth place. Riverside -San Bernardino ranked 10th place, Dallas -Fort Worth 11th and Miami tied for 12th place with several urban areas including Atlanta, Detroit and Austin. In the report, an urban area includes suburbs or municipalities around the urban core of a major city. For example, data for Miami/South 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 you at 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 like Miami are getting worse sort of at the same rate or altogether." The price tag for those 52 hours of wasted commuter time is $1,169, said Schrank. "This is the amount that a commuter would lose per year because they had to drive in congestion," Schrank said. Another measure of how congestion makes driving worse is the so-called freeway planning time index. This refers to the time that a driver needs to get from one place to another to keep an appointment on time or to catch a plane. That index value for the Miami area is 2.85. "The higher that number, the less reliable your freeways are, meaning you get on them you have no idea how long it's going to take you to get some place" Schrank said. "The lower that number, the more reliable, the more like that same average trip you make all the time." The number for Miami, noted Schrank, means commuters must leave early for appointments because of congestion. "If you had a 20-minute trip you could make at night in 20 minutes, in order to make sure you got there — maybe you're trying to go to the airport where you can't afford to be late — you'd have to allow yourself between 50 and 60 minutes to make sure you got there." FOLLOW ALFONSO CHARDY ON TWITTER @ALFONSOCHARDY http://www.miamiherald.com/news/traffic/article32365728.html 3/7 8/27/2015 New study confirms Miami traffic is really jammed up I Miami Herald "ELATEIIII, CO' TE T ~ Uber, Lyft appear tub8|DSouth Florida tnstay ^ VV8Ot to find traffic bliss in Miami -Dade? There's an app for that ^ BrovvardtOMiami-Dade iSone Ofnation's most crowded commutes - In dovvDt0vvn Miami's B[ioke|[ rising lowers lnOrn over choking traffic MORE TRAFFIC You ~.y ^~^~.~ Cable TV Is Ov|nB' Here's What Comes Next The Motley Fool YWCg|gbs Who Did Full -Frontal Nudity ouSgo$ Jennifer Lopez's 2O15Met Gala Look mtEvery Single Angle |nSty|v.00m Cm|abs you didn't know passed away: #1?|oShocking LifeDaily Sponsored Links by Taboola httpmw°w.miam|mermuzvmmrwsmomc/amclenzansrza.htm| *n 8/27/2015 New study confirms Miami traffic is really jammed up I Miami Herald 9 Conlnlgntm Add 8C0nnnlerA— Sort by Oldest Nevada Del Rio ' Key Biscayne, Florida ` How much did that study cost the tax payers, morning traffic 0Oh/would have been free local - - CkeCkthe overtime taxpGp8[S pay in your [jtV . right before holidays, for the |@vv. Like-Rep|y- 4-Aug 2O.2O153:O8am Michelle Rojas | thought the same thing. Please say we did not pay for this study. LMAO! yNioco Mann - University of Miami We needed 8 "S1udv" to tell US something we already know? Well, at least now it's "official": 1r8MiC sucks in M|8n0i Like-Rep|y- e��3-Aug 20.2O154l)Oum Frank Dowd And our "elected offi[j8iS"continue toapprove projects that negatively impact the quality Oflife 0f most d8decounty citizens. Traffic has only gotten worse with Oomajor p|8nStVinnprove the iDfra81ru(ture. Permits to build on any empty parcel has been the norm. ike Vidal - Chief Technology Officer (CTO) at Executive Learning Systems iifflffl It's called concurence, and it is in the books as oridinace, which our fearless leaders conveniently ignore. Jorge Martinez The Only thing that would vVO[h is 8' [D@GS transportation of bus SyStGnl [UDDiOg OO 8 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 ���2-Aug 20.2O15710em Ro||Bach,raa Adding more roads and widening existing highways is avery short term S|O|utiOD.VVSare going k} have to bite the bullet and pony Vpfor Fe@| nlaDStransit solutions. NBT' Rapid Bus Transit iS viable alternative tO MmtrOraii | just hope K4OXreally makes this happen OOtheir network. Like - Reply - Aug 28.20157:23am ottpmoww.miamimermu.commew,uamv/amclv32365728mm1 mr 8/27/2015 New study confirms Miami traffic is really jammed up I Miami Herald mm cha0 G.�mm - KJianni. Florida Untill or unless somebody does something about the lights, any talk of fixing our traffic prolems are just talk. What |SSOdifficult about timing the traffic lights? Liko-Rep|y- 2-Aug 20.20157:38am m " DMicCo Mann - University of Miami The one at 5th and Alton ShUUk1 get top p[|Urdv for longer e8SU\m8St runs. But no, that vvOU|U be too difficult in this Biza[[O VVOr|U burg. Mitchell Gam - K4i8nni Florida N1iCC0 M8OO No matter where you live in Dade COuOh/. you can point to One light that regularly messes upthe flow Vftraffic, but iSnever fixed. AJnl0St GS if nobody is watching or eV8O trying. YamnD OAi - N1ienni. Florida everybody knew that. now what? | hope a0nle0ne do something. Like - Reply - Aug 20.2O158:34am What about does that drive to the front 0fall ready establish line k)gain three 0rfour vehicle SpOtS. not only messing the already established line, but also blocking @ SeCOOd lane of traffic. Like - Reply - Aug 2U.20151O:05am Axg|Fnodim | feel 8whole lot better having the obvious confirmed with 8study. And | feel even better knowing that I live in a pueblito where the primary news outlet considers this is front page worthy. Like - Reply -23hm FacohonkCnmmemo Pluyin Sponsored Content HOtl01v T 10 " U b UU'0'm1 so h ttp://www.miam!herald.com/news/ti,affic/article32365728.htm1 http://www.mimnimeralu.cnmmewsmamc/aromenzam57zemm| 6/7 8/27/2015 New study confirms Miami traffic is really jammed up 1 Miami Herald http://www.miamiherald.com/news/trafficiarticle32365728.html 7/7 /a1,,2111 1111111111011,111)11101,1110))11 )111111111, ',"!1110[110011111k,,,,,, ' " 11",1,10,11$11,11114,P12114,10,„ RBAN MOBILI Texas A&M _All Transportation Institute l'01,0,10 11,;,‘1,,,,,,',10111111111111„,"1,„ AUGUST 2015 Oh1111111111 ,,'„ ',FIHHIHHI'll'I'I'P'11111111111W044411, A 0 Published jointly by The Texas A&M Transportation Institute and INRIX David Schrank Research Scientist Bill Eisele Senior Research Engineer Tim Lomax Research Fellow And Jim Bak Research Analyst Texas A&M Transportation Institute The Texas A&M University System mobility.tamu.edu INRIX, Inc. inrix.com August 2015 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. Acknowledgements 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 Fette, Michelle Hoelscher and Rick Davenpo John Henry —Cover Artwork Dolores Hott and Nancy Pippin —Printing and Distribution Rick Schuman and Myca Craven of IINRIX—Technical Support and Media Relations 2015 Urban Mobility Scorecard ii Table of Contents 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 10 The Future ofCongestion 11 Congestion Relief —AnOverview ofthe Strategies 12 Analysis Using the Best Congestion Data & Analysis Methodologies 14 National Performance Measurement 15 Concluding Thoughts 17 References 39 2015 Urban Mobility Scorecard iii 2#15 0rban �ob^U^ty Scorecard The national congestion recession isover. Urban areas ofv0sizes ore experiencing the challenges seen in the early 2000s — population, jobs and therefore congestion are increasing. The i1S.economy has regained nearly a// of the 9 million 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: http:11mob8/ty. tomu.edu/ums. The data from 198Itn 2014 (see Exhibit 1) show that, short ofmajor economic problems, congestion will continue to increase if projects, programs and policies are not expanded. • The problem isvery large. |n2Ol4,congestion caused urban Americans totravel anextra 6.9billion hours and purchase an extra 3.1 billion gallons of fuel for a congestion cost of $160 billion. 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 lOO largest metro areas saw increased traffic congestion, from 2O1Zto20l3only 61cities experienced increases. * In order to reliably arrive on time for important freeway trips, travelers had to allow 48 minutes to make a trip that takes ZO minutes in light traffic. * Employment was upbymore than SOO,000jobs from 20I3toZ014(l); iftransportation investment continues to lag, 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 MobilityScorecards. 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 tobecloser. This involves everyone agencies, businesses, manufacturers, commuters and travelers. Each region should use the combination ofstrategies that match its goals and vision. The recovery from economic recession has proven that the problem will not solve itself. Exhibit 1. Major Findings of the 2015 Urban Mobility Scorecard (471 U.S. Urban Areas) (Note: See page 2for description of changes since the 2012 report) Measures of... 1982 2000 2010 2013 2014 —mdivWua|CnnDesuk,n Yearly delay per auto commuter (hours) 18 37 40 42 42 Travel Time Index 1.09 1.19 1.20 121 1.22 Planning Time Index (Freeway only) — — — — 241 "wasted''fuel per auto commuter (gaUond 4 15 15 19 19 Congestion cost per auto commuter <2U14 $400 $DlO $950 $950 $960 —The Nation's Congestion Problem Travel delay (billion hours) 1.8 52 6.4 6.8 6.9 "Wasted" fuel (billion gallons) 0.5 2.1 2.5 ].l 3.1 Truck congestion cost (billions ofZOl4doUard — — — $28 Congestion cost (billions ofZOI4dollars) $42 $114 $149 $156 $160 Yearly delay per auto commuter— The extra time spent during the year traveling at congested speeds ratherthan free -flow speeds by private vehicle drivers and passengers who typically travel in the peak periods. Travel Time Index (Tn)—The ratio uftmwe|time i^the peak period totravel time at free -flow conditions. ATravel Time Index ofz.so indicate, a zo'minute free -flow trip take, zs minutes in the peak period. Planning Time Index (pTV—The ratio oftravel time onthe worst day ufthe month totravel time iofree-flow conditions. wastedfuc|—cxtra fuel consumed during congested travel. Congestion cost — The yearly value ufdelay time and wasted fuel bvall vehicles. Truck congestion cost The yearly value ofoperating time and wasted fuel for commercial trucks. Exhibit 2. National Congestion Measures, 1982to2014 Delay Per Total Cost Travel Time Commuter Total Delay Fuel Wasted (Billions of Year Index (Hours) (Billion Hours) (Billion Gallons) 2014DoUam) 2014 1.22 42 6.9 3.1 $160 2013 1]1 42 6.8 3.1 $ISG 2012 121 41 67 3,0 $154 2011 1.21 41 6.6 2.5 $152 2010 1.20 40 6.4 2.5 $148 2009 l.20 40 6.3 2.4 $147 2008 l2I 42 6.6 2.4 $152 2007 121 42 6.6 2.3 $154 2006 121 42 6.4 2.8 $149 2005 1.21 41 6.3 2.7 $143 2004 121 41 6.1 2.6 $136 2003 1.20 40 5.9 2.4 $128 2002 1.20 39 5.6 2.3 $124 I00I 1.19 38 5.3 22 $119 2000 1.19 37 5.2 ll $114 1999 1.18 36 4.9 2.0 $106 1998 1.18 35 4.7 1.8 $101 1997 1.17 34 4.5 17 $97 1996 1.17 32 42 1.6 $93 1995 1.16 31 4.0 1.5 $87 1994 1.15 30 3.8 1.4 $82 1993 1.15 29 3.6 1.4 $77 1992 1,14 28 3.4 13 $73 1991 1.14 27 3.2 1.2 $69 1990 L13 26 3.0 1.2 $65 1989 1.13 25 2.8 1.1 $62 1988 1.I2 24 27 IO $58 1987 1l2 23 2.5 0.9 $SS 1986 1.11 22 2.4 0.8 $52 1985 Lll 21 2.3 OJ $51 1984 1.10 20 2.1 8.6 $48 1983 1.10 19 2.0 0.5 $45 Notes: See Exhibit 1for explanation ofmeasures. For more congestion infonnationandforcon8estioninformationonyourcity, see Tables 1to4 and http://mobi|ity.tamu.edu/ums. 2015 Urban Mobility Scorecard 2 �� Turn^m� � �~K�m��st^K�n ��a � � (And the New Data Providing a a Into ^ The 2015 Urban Mobility Scorecard is the 4 1h that TTI and INRIX (2) have prepared. The data behind the 2015 Urban Mobility Scorecord 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 9OOmillion speeds onl3million miles ofU.S. streetsandhighways—anawesome 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 ofthe 2O1SUrban Mobility Scorecordare summarized below. • Congestion estimates are presented for each ofthe 471U.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. Formoreinfo/mationabout|NR|X,gotowww.in/ix.com. • This data improvement created significant difference in congestion estimates compared with past Repo rtsIScore cards — more congestion overall, a higher percentage of congestion on streets and different congestion estimates for many urban areas. Ashas been our practice, past measure values were revised toprovide 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 indifferent ways for several cities. m The measure of the variation in travel time from day-to-day now uses a more representative trip- basedprocess(4)ratherthantheo|ddatasptthatusedindividua|road|inks. 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 to work only one day per month (on -time for 19 out of 20 work days each month). For example, a PT| value of 1.80 indicates that a traveler should a||ovv 36 minutes to make an important trip that takes 20 minutes in low traffic volumes. The new values are lower, and closer toreal-world experience. w Many of the slow speeds that were formerly considered 'too slow to be a valid observation' are now being retained inthe |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 onthe performance measures and data can befound at: http://mobi|ity.tamu.edu/methodo|ogy/ 2015 Urban Mobility Scorecard ne a,--,emf Congestion Pml 1&,s In the biggest regions and most congested corridors, traffic jams 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 small accident or stalled vehicle can result in major delays. Some key measures are listed below. See data for your city at http;ymobi|ity.tamu.edu/ums/congestion data. Congestion costs are increasing. The congestion "invoice"for the cost ofextra time and fuel inthe 47l U,S.urban areas was (all values inconstant 20l4doUars): • |o2Ol4—$16Obillion * |nZO0O—$ll4billion m |nl982— $42biUion Congestion wastes amassive amount oftime, fuel and money. |nZ0l4: 0 6.9 billion hours of extra time (more than the time it would take to drive to Pluto and back, if there was anoad). m 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 oftax 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 forS3 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 anextra 42hours traveling in2O14upfrom 18hours in1982. w Wasted 19 gallons of fuel in 2014 — a week's worth of fuel for the average U.S. driver up from 4 gallons inI9OZ. • |nareas with over one million persons, 20I4auto commuters experienced: o an average of63 hours of extra travel time o aroad network that was congested for 6hours ofthe average weekday • had a congestion tax of $1,440 Congestion isalso aproblem stother 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. 2015Lrb@n Mobility Scorecard 5 ore etail out Congestion Pro le 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. Exhibit 3. Congestion Growth Trend — Hours of Delay per Auto Commuter 0 0 40 0 20 10 0 Small Medium Large popujation Group Ve s 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). ° 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. Exhibit 4. Percent of Delay for Each Day Exhibit 5. Percent of Delay for Hours of Day 8% 4% C% Mon Tue Wed Thu Fri Sat Sun C% m n � M Mid 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). • Approximately4O percent ofdelay occurs in off-peak hours. ° Freeway delay ismuch less ofthe problem inareas under 1million population. � Exhibit6. Percent ofDelay ' Road Type and Time ofDay Peak Freeways 05 Urban Areas Over 1K4 Population Off Peak Freeways 10% Urban Areas Under 1M Population 2015[rb8O Mobility Scorecard 7 About 2G96oftrips are in severe congestion ..... Uncongested Rush Hour Congestion ° Severe and extreme congestion levels affected only I in 9 trips in 1982, but I in 4 trips in 2014. ~ The most congested sections of road account for QU%ofpeak period delays, but only have 206 of the travel (Exhibit 7). Exhibit 7. Peak Period Congestion in 2014 —bmtthosexvorst trips experience 8O%ofthe extra travel time. Truck Congestion • Trucks account for l8percent ofthe urban "congestion invoice"ahhoughtheyonk/repreuent7 percent oturban 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 Truck 2015Urb@D Mobility Scorecard 8 Since the Congestion Decline During the Recession .... m American motorists are enduring about 5 percent more delay than the pre -recession peak in 2007. (ExhibitJ) w While this is associated with a "good thing" -- economic and population growth in our major metro areas— it is also clearthis growth is outpacing the investment in infrastructure and programs to address the increased demand onthe 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; animmediate 'snapbacKwas seen inmore than one -quarter ofthe studied regions. 0 22areas still have lower total annual delay than inIOO7/O.(Exhibit 9) 0 In contrast tototal 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 2OO7/8values in3orfewer years. (Exhibit8) Exhibit 9. Number of Years Before Congestion Returned to Pre -Recession Levels Total Urban Area Delay Delay Per Urban Auto Commuter 4or5 Years s 4 or 5 Years El" � � I r (6Areas) Zero or1 Year (9) 6or7 Years 2015 Urban Mobility Scorecard 9 The Trouble ,,,,'��~th PUann^n: Your Tr^ We've all made urgent trips —catching an airplane, getting to a medical appointment or picking up a child atdaycare ontime. VVeknow weneed toleave alittle early tomake sure weare 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 a"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 lOillustrates this problem. Say your typical trip takes ZOminutes when there are few other cars onthe road. That isrepresented bythe 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 2Sminutes inthe 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 aUowtue|iab�arrive on�ime. And, asshovvninExhibit 10(red ba�,itimt 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 inareas with fewer than lmillion people —l4and l8 minutes longer in the morning and evening peaks. Data for individual urban areas is presented in Table 3 (in the back ufthe report). Exhibit 10. How Much Extra Time Should You Allow to Be'On-Time'? Areas with More Than 1 Million Population ��P|amm|ngTlnle Average Time 'e, Low -Volume Time Evening Areas with Less Than 1 Million Population 70 50 60 40 20 0 Moming �� Midday 2015 Urban Mobility Scorecard The Future of Congestion Before the economic recession, congestkznwasincmasinXatbetween2and4percen<everyyear— which meant that extra travel time for the average commuter increased slightly less than l 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 ofanimbalance between travel demand and the supply of transportation capacity — whether that is freeway lanes, bus seats orrail cars. As the number ofresidents orjobs goes upinanimproving economy, orthe miles ortrips that those people make increases, the road and transit systems also need to, in some combination, either expand or operate more efficiently. Asthe rising congestion levels inthis report demonstrate, however, this isan infrequent occurrence. Travelers are not only paying the price for this inadequate response, but traffic congestion can also become a drain nnfurther economic growth. As one estimate of congestion in the near future, this report uses the expected population growth and congestion trends from the period of sustained economic growth between 2000 and 2005 to get an idea ofwhat 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. w 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 incongestion for each urban area. The congestion estimate for any single region will be affected by the funding, project selections and 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 2020. * The national congestion cost will grow from $I6Obillion to$l92billion in2O2O(in ZOl4doUao). ° Delay will grow to 8.3 billion hours in I020. m Wasted fuel will increase to 3.8 billion gallons in 2020, ° The average commuter's congestion cost will grow to $1,100 in 2020 (in 2014 dollars). ° The average commuter will waste 47hours and 2lgallons in2O2U. 2015UdJ8n Mobility Scorecard 11 Congestion "'eU^mf �n vvervie of the Strate W � .es 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. |tisdearthat 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|ity'of-|ife. There will also be a range of congestion targets. Many large urban areas, for example, use a target 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 isone part oftheir qua|hy' of-|ifegoa|s'andhavehigherspeedexpectations. 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 isalocal decision. The Urban Mobility Scorecard uses one consistent, easily understood comparison level. But that level isnot 'the gua|,'itisonly an expression ofthe problem, The Scorecard isonly one ofmany pieces ofinformation that should be considered when determining how much ofthe problem tosolve. Better data can play avaluable role in all ofthe analyses. Advancements involume 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, posdbiUtiesandopportunities—vvhere'when,howandhowoftenmobi|hyprob|emsoczur—andmoves 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 onthe possible solutions, places they have been implemented and the effects estimated in this report can be found on the website http://mobilitv.tamu.edU/SOILltions None of these ideas are the whole mobility solution, but they can all play a role. m Get as much service as possible from what we have — Many low-cost improvements have broad public support and can be rapidly 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 Scorecard 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. • A6dcapadtyinchtica|con'i6ors—HandUngmo/efrekghtorpeoont/ave|onfreewa\n,stneets'mi| lines, buses orintermnda|facilities often requires "more." Important corridors orgrowing regions can benefit from more street and highway lanes, new or expanded public transportation facilities, and|a/Oerbosandrai|Ueets.5omeofthe"more^vviUa|sobeinthefo/mofadvancementsin connected and autonomous vehicles — cars, 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, TVand intheir car orattheir 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'scommuters 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 u/agency-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 toadjust their hours and commute trips to meet family urother 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. * Realistic expectations are also part ofthe solution. Large urban areas will becongested. Some locations near key activity centers insmaller urban areas will also becongested. 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 aU'dayevent,andinmanycasesimprovingtrave|timeawanenessandpedictabi|hycanbea 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 �naxUys8^ s sing the 'iltest Con,,-lest~on �naUys`s etho'oUKD ^es a a The base data fort he 2015 Urbon Mobility Scorecard came from IN RIX, the U.S. Department of Transportation and the states (2,3). 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 elir-ninates the difficult process ofestimating speeds and dramatically improves the accuracy and level of understanding about the congestion problems facing UStravelers. The methodology is described in a technical report (5) that is posted on the mobility report website: http://mobiUty.tamu.edu/ums/methodo|ogy/. ° The INRIX traffic speeds are collected from a variety of sources 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 dataset of average speeds for each road segment TT| was provided a datasetof lS' minuteaveragespeedsfo/each|inkofmajorroadwaycuveedintheHicLorica|P/ofi|edatabase (approximately 13million miles in2014). 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 lS'minute volumes using national traffic count dataset (6). w The lS'minute |NR|X speeds were matched to the lS'minutevo|ume estimates for each road section onthe FHVVAmaps. w An estimation procedure was also developed for the sections of road that did not have |NR|Xdata. As described in the methodo|ogyvvebsite, 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 FHVVAdataset) (5). 2015 Urban Mob9ity Scorecard 14 "What Gets Measured, Gets Done" Many of us have heard this saying, and it is very appropriate when discussing transportation system performance measurement. Performance measurement atthe 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 2l*Century Act (K1AP-2l)was signed into law onJuly E,Z0IZto 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). Aspartofthetransbiontoaperformanceandoutcome'basedFedera|hkghmmyfundingprugnam'yWAP' 21estab|ishpsnationa|performanceXoa|sinthefoUoeingareas(7): * Safety • Infrastructure condition w Congestion reduction ° System reliability * Freight movement and economic vitality * Environmenta|sustainabi|ity ° Reduced project delivery delays MAP-21 requirements provide the opportunity to improve agency operations. While transportation professiona|svviUca|cu)atetherequiredMAP'2lperfo/mancemeasunes,theneba|soanopportunhyto develop processes and other measures to better understand their systems. The requirements ofMAP- 21 are specified through a Rulemaking process. At the time of this writing, the Notice of Proposed Ku|emaking (NPRM) for system performance measures (congestion, reliability) has not been released by the United States Department of Transportation (USDOT). VVhi|ethespeci0crequirementsofK8AP'21re|atedtosystemperfonnanoemeasunsarenctyetknmwn, the data, measures, and methods in the Urban Mobility Scorecard 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 Scorecard) 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. Asthe successful 33-yeardata trend ofUMR/UMS suggests, changes can be made as improvements become available. The key is to get started! 2015 Urban Mobility Scorecard 15 ~.�~ oncoU u"��in � Thoughts s The national economy has improved since the last Urban Mobility Scorecord, and unfortunately congestion has gotten worse. This has been the case inthe past, and itappears that the economy - congestion Unkageisasdependab|eas8ravity. 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 heholding inmost growing regions. That isreally the lesson from this series ofreports. The mix of solutions that are used is relatively less important than the amount ofsolution being implemented. All ofthepotentia|congestion'reducingst/ategiesshou|dbeoonsidered'andthereisaro|eand|ocationfur most ofthe strategies. ° 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 of electronic "travel." • 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 towalking, cycling orpublic transportation modes. The 2015 Urban Mobility Scorecord points to national measures of the congestion problem for the 471 urban areas inI014: * $lG0billion ofwasted time and fuel * Including $28 billion of extra truck operating time and fuel * An extra 6.9 billion hours of travel and 3.1 billion gallons of fuel consumed The average urban commuter in2OI4: m spent an extra 42 hours of travel time on roads than if the travel was done in low -volume conditions ° used l9extra gallons offuel * which amounted toanaverage value of$96Oper commuter Traffic congestion has grown since the low point in2O09during 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 consurned 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 the congestion problems they face with avariety ofstrategies and more detailed data analysis. Some ofthe solution lies in identifying congestion that is undesirable — that which significantly diminishes the quality oflife and economic productivity — and some lies inusing the smart data systems and range oftechnologies, projects and programs to achieve results and communicate the effects to assure the public that their project dollars are being spent wisely. 2015[rban Mobility Scorecard 17 pjeoe-joiS Al!l!qokV a A� � es Table 1. What Congestion Means toYou, 2O14 Yearly Delay per Auto Excess Fuel per Auto Congestion Cost per Urban Area Commuter Travel Time Index Commuter Auto Commuter Hours Ronk Value Rank Gallons Runk DnUan, Rank Very Large Average (1Sareas) 83 1.32 27 1.433 VVushingtonDC'VA'MD 82 1 1�34 8 35 1 1.834 1 Los Angeles -Long Beach -Anaheim CA RU 2 148 1 25 11 1.711 3 San Francisco -Oakland CA 78 3 1.41 2 83 3 1.075 4 New York -Newark NY'NJ'CT 74 4 1.34 8 35 1 1.739 2 Bn»\nnMA-NH'R| 04 0 129 17 30 4 1.388 9 Seattle WA 03 7 1.30 3 20 x 1.491 5 Chicagn|L.|N 01 D 1.31 14 28 5 1.445 7 Houston TX 61 8 1.33 10 29 5 1.490 h Dallas -Fort Worth -Arlington TX 53 11 1.27 19 22 23 1.185 14 Atlanta GA 52 12 124 25 20 44 1.130 22 Do<mi1Mi 52 12 124 25 25 11 1.103 15 Miami FL 52 12 128 17 24 15 1.169 17 Phoenix -Mesa AZ 51 17 127 18 25 11 1.201 13 Philadelphia PA -NJ -DE -MD 48 22 124 25 23 18 1.112 26 Very Large Urban Areas —over omillion population. Medium Urban Areas —over nun.mmand less than / million population. Large Urban Areas —over I million and less than amillion population. Small Urban Areas —less than s0000npopulation. 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. Avalue n|1.nnindicates azo'mmutefree-flow trip takes zominutes inthe peak period. Excess Fuel Consumed —Increased fuel consumption due to travel in congested conditions rather than free -flow conditions. Congestion Cost —Value mtravel 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 Note: Please do not |«»»a,mm*and u/v«eVace too much emphasis onumunume�n,c,mmewomnsx Txnmmay ue|��mne�noemmnsexuooumveonomoumnked(mrexamp/e)a~and 12~ The actual measure values should also ueexamined. The best congestion comparisons are made between similar urban areas. !/!qopV ueq-ln g�0,7 1_zz Table 1. What Congestion Means to You, 2014, Continued Urban Area Large Average (31areas) San Jose CA Riverside -San Bernardino CA Austin TX Portland OR -WA Denver -Aurora CO Oklahoma City 0K Baltimore MD Minneapo|in'SLPaul MN Las Vegas -Henderson NV Orlando FL NachviUe'DaviduonTN Virginia Beach VA San Antonio TX ChadoN*NC'8C Indianapolis IN Louisville -Jefferson County KY -IN Memphis TN -MS -AR ProvidencoR|'MA Sacramento CA St. Louis MO -IL San Juan PR CinninnegiOH'KY'|N Columbus OH Tompa'SLPetersburg FL Kansas City MO -KS Pittsburgh PA Cleveland OH Jacksonville FL Milwaukee WI Salt Lake City -West Valley City LIT 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 Ronk OoUa,y Runk 45 1.23 21 *1.045 67 5 1.30 3 28 8 1.422 8 59 10 133 10 18 62 1.310 10 52 12 1.33 10 22 23 1.159 20 52 12 1.85 7 29 5 1,273 11 49 19 1.30 10 24 15 1.101 28 48 19 119 42 23 18 1.110 27 47 23 1.20 21 21 32 1.115 25 47 23 1.28 21 18 02 1.035 36 40 27 1.26 21 21 32 984 42 46 27 121 34 21 32 1.044 34 45 29 121 34 22 23 1.108 10 45 29 1.18 42 18 51 953 40 44 33 1.25 24 20 44 1.002 38 45 35 1.23 28 17 70 903 44 43 85 118 46 23 18 1.060 30 43 35 120 37 22 23 1.048 32 43 35 119 42 21 32 1.080 29 43 55 1.20 37 21 32 951 47 43 35 123 29 19 51 958 45 43 35 110 05 21 32 1.020 37 43 35 1.31 14 24 15 1.150 21 41 45 118 40 21 32 989 40 41 45 118 40 20 44 933 49 41 45 121 34 18 02 907 57 39 51 115 70 18 82 333 49 39 51 119 42 21 32 889 58 30 55 1.15 76 22 23 807 61 30 55 118 40 15 70 842 72 38 55 1]7 54 22 28 907 41 37 80 118 40 22 23 1.059 31 34 77 113 80 14 84 729 02 Large Urban Areas —over 1million and less than nmillion population. Yearly Delay per Auto Commuter —Extra travel time during the year divided by the number of people who commute in private vehiclesmthe urban area. Travel Time Index —The n«/vv,truve|omrinmopoxxponodmmctmvu/nmemore-flownonu/uonx. Avalue or1.aoindicates oun'mioutefree-flow trip takes zaminutes mthe peak ponuu. Excess Fuel Consumed —Increased fuel consumption due to travel in congested conditions rather than free -flow conditions. Congestion Cost —Value v|travel time delay (estimated ot $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 d/expV. Note: Please unnot place too much emphasis vnsmall differences mthe rankings. There may be little difference in congestion between areas ranked (forexample) 6" and 12". The actual measure values should also ueexamined. The best congestion comparisons are made between similar urban areas. 2015 Urban Mobility Scorecard Table 1. What Congestion Means to You, LU14, continues Urban Area 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 Medium Average (33 areas) 37 1.18 18 $870 Honolulu HI 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 1.20 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 MI 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 -MI 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 58 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 1.17 54 15 78 694 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 96 1.12 91 9 96 512 94 Medium Urban Areas -over 500,000 and less than 1 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 -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). tv 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 121h. The actual measure values should also be examined. The best congestion comparisons are made between similar urban areas. pa8oaao0S Abigob uagan gko N Tab Urban Area 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 WI 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 Average e 1. What Congestion Means to You, 2014, Continued Yearly Delay per Auto Excess Fuel per Auto Congestion Cost per Commuter Travel Time Index Commuter 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 62 833 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 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 $94.04 per hour of truck time) and excess fuel consumption (estimated using state average cost per gallon for gasoline and diesel). Note: Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6`h and 12`h. The actual measure values should also be examined. The best congestion comparisons are made between similar urban areas. p,ieoajoo,o Xl!l!qopV ueqjngko,7 Table 2. What Congestion Means toYour Town, 2D14 Truck Congestion Total Congestion Urban Area Travel Delay Excess Fuel Consumed Coot Cost (1.000Huv,u) Rank (1.000GaUons) Rank ($miUion) Runk ($miUion) Rank Very Large Average (15areas) 221.970 99.480 $805 *5.260 New York -Newark NY'NJ'CT 628.241 1 286.701 1 2.778 1 14.712 1 Los Angeles -Long Beach -Anaheim CA 022.508 2 195.491 2 1.721 2 13.318 2 Chicago IL -IN 302.809 3 147.031 3 1.482 3 7.222 3 VVanhingtonDC'VA'MD 204.375 4 88.130 6 710 O 4.500 5 Houston TX 203.173 5 94.300 4 1.118 4 4.924 4 Miami FL 195.946 0 90.320 5 736 5 4.444 0 Dallas -Fort Worth -Arlington TX 180.535 7 79.382 7 702 7 4.202 7 Phi|ado|phiuPA'NJ'DE'MD 157.108 8 77.456 0 003 9 3.809 8 Phnonix'MenaAI 155.730 9 75.838 8 092 O 3.841 8 Detroit M| 155.350 10 73.645 10 567 11 3.514 10 BoNnnMA'NH'R| 153.884 11 71.802 11 426 15 3.303 11 Atlanta GA 148.000 12 57.113 14 434 13 3.214 13 San Francisco -Oakland CA 140.013 13 02.320 12 380 18 3.143 14 Seattle WA 139.842 14 82.136 13 645 10 3.294 12 San Diego CA 79.412 20 20.742 36 192 35 1.050 21 Very Large Urban Areas -over amillion population. Medium Urban Areas -over noo.nnoand less than / million population. Large Urban Areas -over / million and less than amillion population. Small Urban Areas -less than sno.onopopulation. r,a"o/ 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 atsy4.o*per hour o,truck time) and the extra diesel consumed (using state average cost per y»n»^). Congestion Cost -Value o,delay and fuel cost (estimated at $1rorper hour mperson travel, ay*.n*per hour v,truck time and state average fuel cost). mmr:p|oouogonot place too much emphasis onsmall differences inthe rankings. There may be little difference in congestion between areas ranked (forexample) 6` and 12 Ih The actual measure values should also be examined. The best congestion comparisons are made between similar urban areas. N) � � un Urban Area Table 2. What Congestion Means to Your Town, 2014, Continued m Large Average (31 areas) - San Jose CA K8inneapo|io'3LPaul MN Riverside -San Bernardino CA -= Denver -Aurora CO ` Baltimore MID Port|and0R-VVA Tampa'S1Petersburg FL ` St. Louis MO -IL w San Antonio TX � ~� Las Vegas -Henderson NV San Juan PR Sacramento CA OdandoFL Austin TX QnoinnadOH'KY'|N Virginia Beach V8 Indianapolis IN Oklahoma City 0K Kansas City MO -KS Cleveland OH Pittsburgh PA Columbus 0H NanhviKe'DavidnnnTN Memphis TN -MS -AR PmvidenueR|'MA MikwaukeeVV| Louisville -Jefferson County KY -IN Chadott*NC'SC JaoknnnviUoFL Salt Lake City -West Valley City UT Richmond VA Travel Delay Excess Fuel Consumed Truck Congestion Cost Total Congestion Cost (1.000Hnu,o) Ranh (1.0006aUnnn) Rank ($miUion) Rank ($miUiun) Rank 55.580 25.680 $235 $1.200 104.558 15 43.972 16 240 28 2.230 15 99.710 18 38.542 19 327 20 2190 17 99.050 17 30.732 23 301 17 2.201 16 91.479 18 44.822 15 319 21 2.061 18 87.020 19 38.801 10 427 14 2.075 18 72.341 21 38.611 17 375 16 1.763 20 71.628 22 31.854 22 237 30 1.508 24 69.350 23 32.891 21 320 18 1.637 22 84.328 24 20.009 25 251 27 1.402 25 03.693 25 30.001 24 158 45 1.375 26 60.301 20 33.418 20 437 12 1.605 23 00.220 27 26.289 20 189 36 1.334 27 52.723 28 23.938 31 212 33 1.207 28 51.116 28 21.054 33 182 39 1.140 31 40,485 30 25.080 20 238 28 1.150 29 40.274 31 20.005 37 112 52 1.020 30 48.435 32 25.066 29 259 26 1.142 30 45.652 33 21.027 35 100 43 1.030 34 45.570 34 21.349 34 220 82 1.085 32 45.051 35 25.547 27 182 39 1.040 33 44.758 30 24.107 30 171 42 1.030 34 40.025 37 18.870 38 102 44 921 41 38.977 39 13.093 39 285 22 1.013 38 37.024 40 10.440 42 229 81 939 40 37.809 41 18.853 41 121 48 846 45 57.859 42 21.957 32 206 25 984 39 35.022 45 17.841 43 180 38 880 43 34.153 48 13.700 50 131 47 770 47 28.680 48 12.063 53 101 57 859 49 26.825 51 16.304 46 207 24 779 48 20.104 53 10.802 55 68 88 550 54 Very Large Urban Areas -over omillion population. Large Urban Areas -over 1million and less than amillion population. Travel Delay -Extra travel time during the year. Excess Fuel Consumed -Value mincreased fuel consumption due mtravel / congested conditions rather than free -flow conditions (using state average cost per oanon). Truck Congestion Cost -Value of increased travel time and other operating costs of large trucks (estimated atmy4.o*per hour mtruck time) and the extra diesel consumed (using state average ,t per gallon). Congestion Cost -Value mdelay and fuel cost (estimated at $17.67 per hour of person travel, $94.04 per hour of truck time and state average fuel cost). mvteam^euonot place too much emphasis vosmall differences mthe rankings. There may be little difference in congestion between areas ranked (forexample) 6" and 12". The actual measure values should also bvexamined, The best congestion comparisons are made between similar urban areas. Medium Urban Areas -over mm.mmand less than 1million population. pa000iooS A1111goW uogan 9I-o NJ Urban Area Table 2. What Congestion Means to Your Town, 2014, Continued Travel Delay (1,000 Hours) Rank Excess Fuel Consumed (1,000 Gallons) Rank Truck Congestion Cost ($ million) Rank Medium Average (33 areas) 20,000 9,815 $94 New Orleans LA 39,159 38 18,895 40 281 Bridgeport -Stamford CT -NY 37,119 43 16,586 45 194 Tucson AZ 35,993 44 17,477 44 176 Tulsa OK 30,341 47 14,128 47 107 Hartford CT 28,296 49 13,406 51 115 Honolulu HI 27,672 50 14,118 48 74 Buffalo NY 26,851 52 14,053 49 103 Baton Rouge LA 23,163 54 12,104 52 189 Raleigh NC 23,128 55 9,159 62 71 Grand Rapids MI 21,536 56 10,552 56 58 Rochester NY 20,582 57 10,550 57 73 Albuquerque NM 20,452 58 10,961 54 112 Albany NY 20,409 59 10,164 58 88 Birmingham AL 19,385 60 9,105 63 139 El Paso TX -NM 19,127 61 9,360 60 77 Springfield MA -CT 18,431 62 9,335 61 54 Charleston -North Charleston SC 18,422 63 9,024 64 126 Omaha NE -IA 18,224 64 9,535 59 57 Allentown PA -NJ 17,114 65 8,743 65 66 Wichita KS 16,860 66 8,594 66 88 New Haven CT 16,430 67 7,949 69 69 Columbia SC 16,315 68 8,018 68 104 McAllen TX 16,226 69 7,336 73 49 Colorado Springs CO 16,058 70 7,700 71 50 Toledo OH -MI 15,905 71 8,451 67 79 Knoxville TN 14,946 72 7,180 74 87 Dayton OH 14,604 74 7,434 72 69 Sarasota -Bradenton FL 14,053 75 6,574 76 46 Cape Coral FL 12,959 78 5,637 83 44 Akron OH 12,283 81 6,586 75 50 Fresno CA 11,823 83 5,682 80 23 Provo-Orem UT 8,178 86 5,677 81 115 Bakersfield CA 8,001 89 3,743 90 65 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, $94.04 per hour of truck time and state average fuel cost). Note:Please do not place too much emphasis on small differences in the rankings. There may be little difference in congestion between areas ranked (for example) 6t and 12`". The actual measure values should also be examined. The best congestion comparisons are made between similar urban areas. 23 34 41 54 50 63 56 36 66 74 64 52 58 46 62 77 48 75 70 58 67 55 83 81 61 60 67 84 85 81 95 50 71 Total Congestion Cost ($ million) Rank $475 1,014 898 856 682 656 616 620 623 504 470 469 501 479 501 439 408 470 407 393 407 384 409 355 356 381 367 346 312 288 284 251 270 215 37 42 44 48 50 53 52 51 55 59 61 56 58 56 62 64 59 65 67 65 68 63 72 71 69 70 73 75 79 80 85 83 87 paeoaaoos ii!l!goW uegan 560Z Urban Area Table 2. What Congestion Means to Your Town, 2014, Continued Travel Delay (1,000 Hours) Rank Excess Fuel Consumed (1,000 Gallons) Rank Truck Congestion Cost $ million) Rank Total Congestion Cost $ million) Rank 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 8,170 14,799 73 13,143 76 13,004 77 12,843 79 12,287 80 11,963 82 11,159 84 11,017 85 8,028 87 8,012 88 7,887 90 7,371 91 6,948 92 6,354 93 6,282 94 6,111 95 5,115 96 4,181 97 4,080 98 3,919 99 3,511 100 1,685 101 3,850 5,262 84 6,432 77 7,928 70 5,723 79 4,897 86 5,673 82 5,773 78 5,120 85 3,629 92 4,110 88 3,534 93 3,847 89 4,254 87 3,728 91 2,241 95 2,400 94 2,102 98 1,228 100 2,204 96 2,130 97 1,866 99 660 101 36 61 72 52 80 59 73 55 76 53 78 40 87 72 65 38 89 40 87 26 94 27 93 38 89 41 86 32 92 16 97 21 96 53 78 10 99 10 99 34 91 14 98 9 101 190 336 74 302 77 312 75 299 78 282 82 269 84 283 81 247 86 190 88 179 90 176 91 181 89 175 92 155 93 134 96 135 95 148 94 88 99 89 98 107 97 81 100 40 101 101 Area Tota! 101 Area Average Remaining Area Total Remaining Area Average All 471 Area Total All 471 Area Average 6,036,500 59,800 906,200 2,400 6,942,700 14,710 Very Large Urban Areas -over 3 million population. Large Urban Areas -over 1 million and less than 3 million 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 121h. The actual measure values should also be examined. The best congestion comparisons are made between similar urban areas. 2,697,300 24,360 138,400 26,700 240 1,370 424,200 4,040 21,170 1,140 11 57 3,121,500 28,400 159,600 6,610 60 340 Medium Urban Areas -over 500,000 and less than 1 million population. Small Urban Areas -less than 500,000 population. pieoaloos if,illmon uegin 91-0E Urban Area Very Large Average (15 areas) Los Angeles -Long Beach -Anaheim CA Washington DC -VA. -MD Seattle WA San Francisco -Oakland CA Chicago IL -IN New York -Newark NY -NJ -CT Houston TX Miami FL Boston MA -NH -RI Detroit MI Phoenix -Mesa AZ San Diego CA Dallas -Fort Worth -Arlington TX Atlanta GA Philadelphia PA -NJ -DE -MD Table 3. How Reliable is Freeway Travel in Your Town, 2014 Freeway Planning Time Index Value Rank 3.06 3.75 3.48 3.41 3.30 3.16 3,15 3.13 2.85 2.81 2.80 2.66 2.66 2.65 2.48 2.41 Freeway Travel Time Index Value Rank 1.37 Freeway Commuter Stress Index Value Rank 1.44 1 1.57 1 1.63 2 2 1.40 10 1.52 7 4 1.47 5 1.59 4 6 1.49 4 1.64 10 1.39 11 1.45 17 11 1.38 13 1.44 18 12 1.43 7 1.47 13 15 1.28 21 1.30 78 17 1.38 13 1.47 13 18 1.26 23 1.28 80 21 1.24 28 1.34 64 21 1.25 26 1.32 75 23 1.34 18 1.38 49 30 1.25 26 1.34 64 33 1.19 32 1.25 84 1 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 121h. The actual measure values should also be examined. ra cv r- oo oo ,T 00 CD ,t LO 00 00 UP c- cn co c) oo cn co r- uo oo TT' - N. CO uoN COCO,— COO) COCO CDO)N 00NN LONN •crO t N CO co cn N 00 c- c- CD 04• a.) N 00 00 00 00 00 u0 2 e 0 0 L_ 0 LL a) -172 CO 71) 0 E ets 0 a) too r- co r- co co CD CD 00 N r- 1.0 N LO u0 CD CD u0 00 u0 ‘t N CD CD u0 N r- cn CD CD cv cv co (0(0 ..qt co op cr (0(0 u0 t uo c- u0 QD 00 QD u0 r- N 00 a) L0 cc, QD c- CD 0) (00) uo 00 CO cr 00 N (000 c- 00 ,t (0c- r- CD • • • • H CL E 0 0 0 c). 0. o 0 .o 20 '6 c • co c (1) .trs w (1, w (1.) cL — 0 c 10 c aa 0 0 co cc -0 10 ,,c0 c -c c > CO a) E O. 0 CC r- (00) co Nr u0 CD st (0(0 CD N (0(0 r- 0) CA QD r- CD V- T- in (00) CD N r- CD c-• c-N N 00 00 00 00 00 00 ct ,r Ls) uo uo uo (0(0 (.0 r- 00 ,r•r_ (0(0 oq T- (0(0 04 CA 0) r- (.0 ",r r- u0 c- 00 CO 00 cv cv oo co oo r- COD a) N CD CD (DO r- co uo uo uo co co co 00 N cv 7- V- T- V- T- CD CD CD. CD. CD. 00 c6 00 N CV cV CV N CV CV CV N CV cV cV cV CV c4 c4 c4' c4L e4 c4' Urban Area < 0 to 0 to c Z co co "5 •- c '-0 co 0 0 0_ 0 (1.) CO ...) = co '2 ,<asoWoci? a 0 E c ct, cs 0 CS = 0 0 a • =-. _C -J CL OD 'W CD OD a) C) H ›, c c Li_ a c Z CC (I) a..) _j c_J_,.><C)°>_. $2. 0 E, , 2 (DTAC.),22Fa)00> E C) cu 2 2 E .as r• d, . ,-= _8 c, a) _0 cu 5 (130 0 , 0 o _J _C CCi ea a) sa E c E al > (f) = (); 3 g i5 2 6 4 2015 Urban Mobility Scorecard > Z = a) .0 H GO 00 (f) (1,) , CO = m E H E E CL a) >, 0 -- m 0 0 0 2 co 0 -0 E ,D° Cs• dr- E > 9 2 a) .2 Es co a a) CD E c) a 0) 0 .0 0 0 w CO 0) CO 0 F. a) 0 > as 0 4.- o — (i) 0 ci) al -0 (yrs „rE 6_co m• 0 0 —01) r0 -co".- °a) 0cL -7) s2 E § D= cig ,(„cj .-5 w E x -92 o • ,•2„ ssz) `1 01) c (!) 2 — 0 ‘c-q•-• > 0sa• T. 0 c 2:zcs 0-) .5; 3 -; 42 .;20 im„ 'E .0 N E w .c E 2 (NI = 2 74s3 ‘--(1) 73) -Ccc2c> a.) g001 0 a) xzEo 'c 'D H, ..c F. m , -- t-c<c3cpcx=c3 c." 0 I .-F„ -c E -c0 "C. 0 cn > " x N a) 9 c a) (u c T3 0 as u 76 a> 0 c .2 ocs > cds) 8 E <ri Es? < .1c 0:(00:0P-=0,13 ji9 9-2 E 7°D 0. E E s- a > E Da) Ec `al 1 E >. T. as D (a ca t() cs, as • a) c c a) css • - 0 >I'6LL E LL. Z 27 Town. 2014. Continued O>- as to LL a) • 0(nMMrCoI—ctMl-Tt V(nTtetN-O)(DTt TrTtInd'r-etcMTtTr(.oIn TrTt ✓ r 00 r C) Tr N M ,- N M M (n (O M N 7 d' M M (D In M O) M ti co CO 7t 10 (.O (O (NI I`-OO)r0rr00CD1tOrc00)OO1t(DO(OOMO1to)(O V V CD �7 NM et d" V MM Et MMet �t M. Mr Met MM MMM r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r C d E 1- a (a >. O R d� a > LL O O Tt co O O O) O N Li) o) N CO CO CO N CO (O 0) N N In CO 10 N 0) N O (O EtTtInLnLr)LntiL()CDN-N OOtitit`o)OI—N-MtiOCDO)COo) Trt 0) In O (O (.o co N N ,t ti N r O ti r (D O O o) M (O r 0) N- CO CO 1- 1-0 O (n (o co `tMNuor rrOrrOOr00000000r0000rOr0 ✓ r r r r 7- r r r r V- 7- r r r r r r r r r r r r r r r r r r r 13 O E H a) c co c 0 > co uo O 10 o) O .t 0) M M ti O O NLn I` CO 0) N N Tt V CO TT r `7 r O O O N Tt r N N M ,t ''1 (f) In Ln 10 (0 CD co CD CO CO (D ti r r r I` co c0 O O 00 10 O) 0) CD CONOO,t Or ,t0000UDL0M(NI co M N N N N N r Lf) N- CD In CO o) CO I"- CO CO N N CO CO CO N O CO In U) N CO r r00 o) CD 0) O) CO CO CO CO CO CO CO I- t`tif` co co (.O(O(n N N N N r r r T r r r r r r r r r 1- r r r Urban Area 0 } Z (4 a .c O o U 0 m _ mQ E� -C c _� Jul a) Z▪ g J F- a,Y � Q) rL Q cG0 Q� -DU OZ (9 ZQZ w ¢2 < co o o c °U oZZQO >z_cT) f° m tom ���U oOZ m~ 0 aO a), a) o cv o a)o oo= �.co o'� o E �a' ® ° �� ° o� °) o daa))ivoscoa©�m`5oaa)-."m.(2c°©-aoc`oYco CL —52mmEa�2 2ZCcCOS0i0coccH-Hz<Cmmi>00�CCC0D<`1¢wl—LLcDOO(n:EO o H 0 H N X O NC 0 0 0 ID 0 0 X 0 C N O .0 E U O C CD0 o 0 c\ C 0 0 U 0 O7 O E C a5 v O c0 U 0 C ▪ .0 S ' 'C • 0 v 0. 0 C o. N I b n O • O CL C_ C U . .co = C 0 O N > O C 0 _ O E. �NN �.0 C V-- 0 0. 0 C .0 C O C L O a) • 0 0 C O 6) '" -O N 7 N 0 C 0 6 U Oa E •E U c • O Y w .5 ID a v a) .0 E 0 - (6 a) cn .0 C a) •U E c E E om v noo>00O N N 7 coSD CI 0 "C O L E n co F O F 0 c) o I .C/23 o Q> X C o O o Q o E O d I C 0v O 'CS X 0 0 0 • C a) C 0 C O3 > Ed(n(7-a m E `O o N E C 7 O (0 < c m E ' E C c > E 0 ...., m ` 0 E• T, • a)0a) O a)O O O2'EEo LL LL LL Z C ID • N (o cz E E o U 0 O O 0 C N 0 0 0 E cc E m ro 0 (0 O O N m m N it 2015 Urban Mobility Scorecard 28 Table3. How Reliable isFreeway Travel inYour Town, 2Oi4,Continued 0 c%) E E C.) a) 0 a) 04 0 r- cD N c- r- cn r- 0) up r- r- CO CD CD CD LI) CV CD CV r- up N QD CO 0) 00 CO LO In CO UD UD CD (OH up H 0) cc, H L0 ct CO CD UD CD N CD CV co cs! m NI- M. c'') 7t `4- 0) M cN! 0 cv co NI cv 0) CO CD CV N CO CD CO CD CD CO CD 0) cn cv up cv -.4- CO c0 QD N- 00 1"-- 0) cc) 1- CO QD CD OD N CD CD CO CO LO r- cD r- r- co r- CD LED CD CO QD 0 04 OD CD CV r- cv cv T 7- r- 7- CD CD CD CD CD CD ,- CD r- CD CD CV CD CD CD . . . . . . . . . . . . Tt ri T- T- T- co co co) cl 0 0) ,- 00c0 CO 0 (0(0 ,: (0(0 C\I U) U) 0) CD (0(0 t t ‘,Y QD r- r- r- r- op CO 00 00 CD CD OD CD OD 0) 0 0 CO "0 •k- UD e4 c4 CON- CO CO N Lc) "1- CO CO C\J CO N r-- C‘i 7- 7- 7- T V- CID •t- Urban Area 2 > CC) Csi 0 -C 0 TO Z a) H X 1 73 Z (...) H < 0 E x To Y CC E , . .a.) < • - X u) -JZ 7 H -a — LI- c) >.°) 8 C) 2,16 t (2 2 6 i 45 15 (2C f) ig ;'), i)- ci.,`I 11 . •— c . ' 0 E o ..,,,, 0 E C 0 w 0 v) ;-_-_ a.) E c- as c) , z c -,, ca c.) 0- 0 a) c) 0 0 = cr)c,ca_—ca.),__co. E 0 .... 7 x = 0-, (1.) C6 0 CO V) 0 0 H 0 vi • 02. (c) c 0 0- ° E E a. a co o. 0. a) o c 02 0 0 o >„ (I) o C c _c qs o Tas rA 75 IS '8 2 c cu -U CD 0 0 -t-5 a 0 0 E ccs c) 0 1-5 •- cz, CD 0 ° b • - in en *Cs .- I 0 <1.) a a3 7.3 c c 0 0 < . r% (1.) C 92 0 -0 - C 0 CZ (0 0 (1.) C.) 0.92 1 I Ea) °C -C° 5 -`11 - E 5 ao > -E -° E i" >, 2 u > _o - , 0_ 2° 15 -Emc02.2 fs .— 0,1 'C cL) E> 0. 0. ."" - 2 925 0 0 cu Tis' as 2 o c .= az 7,0 0 'E2 .Z0.; c c - E -E'138EE")-5 .,-co E c o = -0 47... 0 C6 •.= j.) X c c) 0 > I Tua . a ) - 1 _o 0.0 0 a.) r-C1) E > .9 o E +30 j _Co 0 -0(C3(1) H- CC'''. C X 0 0) g 43 E 8 22) 0 >4 CNI C o Tti 0 02 C ° > > E E 06.)'2 -• 0 a> 21ELO-5' < en ., c c a = a (07) 2 '2 7-c-. -20. E E < a v) > a) - D (0 2 _c 0 2 s, (ZCLc) I-- C) -,E5 22 >s if as fa E PC V, CC CD (i) .5 C\ (1) 0.) a) ,a •- Eu.z 2015 Urban Mobility Scorecard 29 Urban Area Table 4. Key Congestion Measures for 370 Urban Areas, 2014 Annual Hours of Delay Annual Congestion Cost Total Per Auto Total $ per Auto (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 WI -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 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 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 El Centro -Calexico CA 439 4 10 87 El 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 Hours of Delay Annual Congestion Cost 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 Hours 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 WI 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 Kankakee IL 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-Ches 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 Tab|e/L Key Congestion Measures for 37OUrban Areas, 2O14(xonbnuod) Annual Hours ofDelay Annual Congestion Cost Total Per Auto Total $ per Auto Urban Area (OOO) Commuter (Million $) Commuter Macon GA 2.271 15 51 337 Madera CA 360 4 8 87 Manchester NH 2.302 13 53 311 MandeviU*-CovingtonLA 1.753 18 45 470 Manhattan KS 478 5 11 109 Mankato MN 602 8 13 182 Mansfield OH 838 10 19 232 Manteca CA 623 7 10 177 Marysville WA 2.630 16 02 389 Mauldin -Simpsonville SC 886 7 22 168 Mayaguez PR 1.408 13 39 353 McKinney TX 1.811 g 43 215 Medford OR 1.889 11 47 287 Merced CA 1.317 Q 33 218 Michigan City -La Porte |N'y@| 844 12 21 287 Middletown OH 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 10.396 30 238 870 Modesto CA 0.656 18 159 421 Mononsen'Ca|ifomioPA 583 8 13 183 Monroe LA 1.820 14 45 350 Monroe MI 829 Q 19 201 Montgomery AL 0.494 24 149 553 Morgantown WV 1.085 14 24 311 Morristown TN 1.001 19 24 458 Mount Vernon VVA 857 15 21 307 Muncie IN 1.063 11 25 247 yWunieta-Temecula'K8onifeoCA 3.084 7 72 162 Muskegon MI 2.697 10 59 348 Myrtle Beoch'SooasteeSC-NC 7.452 30 188 754 Nampa ID 2.109 13 47 283 Napa CA 1.178 13 28 290 NaahuaNH'M/\ 3.372 14 78 324 New Bedford MA 1.563 10 34 219 Newark OH 821 7 14 107 North Port -Port Charlotte FL 1.800 10 41 216 Norwich -New London CT -RI 8.017 20 83 451 Ocala FL 1.904 12 47 270 Odessa TX 1.605 13 39 330 Dgden'LoytonUT 10.408 18 339 581 O|ynpio'LaoeyVVA 3.929 20 94 481 Oshkosh WI 513 O 13 155 Owensboro KY 1.010 13 27 335 Palm Coast -Daytona Bch -Port Orange FL 0.849 20 230 471 Panama City FL 3.395 21 77 485 PorkersburgVVtcOH 905 14 22 317 Pascagoula MS 778 14 18 323 Peoria IL 4.743 17 110 391 Petaluma CA 634 0 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 fere ces 1. Current Employment Statistics, U.S. Bureau of Labor Statistics, U.S. Department of Labor, Washington D.C., http://www.b1s.00vices/home.htm 2. National Average Speed Database, 2009 to 2014. INRIX. Kirkland, WA. www.inrix.com 3. Federal Highway Administration. "Highway Performance Monitoring System," 1982 to 2010 Data. November 2012. Available: http://www.fhwa.dot.,dov/policvinformation/hpms.cfm 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: http://www.tpics.us/tools/documents/SHRP-C11-Reliabilitv-Tech-Doc-and-User-Guide.pdf 5. Urban Mobility Scorecard Methodology. Texas A&M Transportation Institute, College Station, Texas. 2015. Available: http://mobility.tamu.edu/ums/methodolocy. 6. Development of Diurnal Traffic Distribution and Daily, Peak and Off -Peak Vehicle Speed Estimation Procedures for Air 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/summaryinfo.cfm. 2015 Urban Mobility Scorecard 39 Appendix A Methodology for the 2015 Urban Mobility Scorecard 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 a spreadsheet that can be downloaded at http://mobility.tamu.edu/ums/congestion-data/. This appendix documents the analysis conducted for the methodology utilized in preparing the 2015 U/bunMmb0ityScoreouni This methodology incorporates private »ectortmf0cspeed 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 (2). Adetai|ed description of that dataset can be found at: http://wwwjhwa.dot.gov/po|icv/ohpi/hpms/index.htm. Methodology Changes for the 2O15UMS There are several ch a nges to the UMS methodology forth e 2015 Urban Mobility Scorecard. The |a/gest 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 of hourly 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 dataset that used individual road links. The Planning Time Index (PT|) is based on the ideas that travelers want to be on -time for an important trip 19 out of2Utimes; so one would be late towork only one day per month (on-timefor 19 out of the 20 work days each month). For example, a PTI value of 1.80 indicates that a traveler should allow 35 minutes to make an important trip that takes 20 minutes in low traffic volumes. * Speeds supplied by INRIX are collected every 15-minute5 from a variety of sources every day of the year on most major /oads. Many of the slow speeds formerly considered "too slow to be a valid observation" are now being retained in the |NR|X dataset. Experience and increased travel speed sample sizes have increased the confidence in the data. A,| 20\5lJzbon�\ohi\hyScorecard ��ctbodology The Urban Mobility Scorecard (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 orgroups ofurban areas. Calculation procedures use a clataset of traffic speeds from INRIX, a private company that provides travel time information toavariety ofcustomers. |NR|X'sZUl4data 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 (Z4hours x7days x4periods per hour). INRIX's speed data improves the freeway and arterial street congestion measures in the following ways: m "Rea|"rush hour speeds used toestimate arange ofcongestion measures; speeds are measured not estimated. w Overnight speeds were used to identify the free -flow speeds that are used as a comparison standard; low -volume speeds oneach rood section were used os the comparison standard. • 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 dotob combined with the best volume information to produce high-quolity congestion measures. The Congestion Measure Calculation with Speed and Vo|urneQatasets The following steps were used to calculate the congestion performance measures for each urban roadway section. 1, Obtain HPIVIStraffic volume data by road section 2. 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 5. Calculate congestion performance measures 7. Additional steps when Volume data had no speed data match The mobility measures require four data inputs: • Actual travel speed • Free -flow travel speed w Vehicle volume ° Vehicle occupancy (persons per vehicle) to calculate person -hours of travel delay A-2 2015 Urban &6,b/li(yJoorrcuo/Methodology The 2014 INRIX traffic speed data provide abetter data source forthe firsttwo inputs, actual and free- flowtrave|time. The UMSanalysis requires vehicle and person -volume estimates for the delay calculations; these were obtained from FHVVA/s HPIVISdataset. 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 is described in more detail inStep 7. Step 1.|dentifVTraffic Volume Data The HPMS dataset from FHWA provided the source for traffic volume data, although the geographic designations in the HPIVIS dataset are not identical to the INFIX speed 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,l. 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 Factors Day of Week Monday to Thursday Friday Saturday Sunday Adjustment Factor (to convert average annual volume into day ofweek volume) A,3 Z015[�bunA&/hbYryScorecard ���bodo|ngy 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 an estimate of traffic speed and traffic volume was available for each roadway segment in each urban area. The combination (also known as conflation) of the traffic volume and traffic speed networks was accomplished using Geographic Information Systems (G|5)tools. The |NR|Xspeed network was chosen as the base network; an ADT count from the HPMS 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 of the speed data. Typical time -of -day traffic distribution profiles are needed to estimate hourly traffic flows from average daily traffic volumes. Previous analytical effortz"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 traffic in the morning (AM), peak traffic in the evening (PM), 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 of37 states. I Roadway Usage Patterns: Urban Case Studies. Prepared for Volpe National Transportation Systerns Center and Federal Highway Administration, July Z2' 1994. 2 Development of Diurnal Traffic Distribution and Doily, Peak and Off-peak Vehicle Speed EshmotionProceduresƒbr Air Quality Planning. Final Report, Work Order B-94-06, Prepared for Federal Highway Administration, April 1996. 20/5Urban Mobility Scorecard Methodology A,4 Exhibit A-2. Weekday Traffic Distribution Profile for No to Low Congestion 12% 10°/0 8Q/0 1- E > 6Q/0 -1- 0:00 2:00 9:00 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 -Ft-PM Peak, Freeway Weekday 4-AM Peak, Non -Freeway Weekday -.4-PM Peak, Non -Freeway Weekday Exhibit A-3. Weekday Traffic Distribution Profile for Moderate Congestion 12°/0 10% 8% a) E 7:1 6/. CI 4% 0 0) 2% 0) 11. 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 -4AM Peak, Freeway Weekday -0-PM Peak, Freeway Weekday 2015 Urban Mobility Scorecard Methodology A-5 http://mobility.tamu.edu/ums/cong,estion-data/ Percent of Daily Volume Exhibit A-4. Weekday Traffic Distribution Profile for Severe Congestion 12% 10% 0% irr 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 44-PM Peak, Freeway Weekday AM Peak, Non -Freeway Weekday .4.4.-PM Peak, Non -Freeway Weekday 12% 0- 10% E 15 8% 0 4% 2% 4- Exhibit A-5. Weekend Traffic Distribution Profile 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-Freeway Weekend -0-Non-Freeway Weekend 2015 Urban Mobility Scorecard Methodology A-6 http://mobility.tamu.edu/ums/congestion-data/ Percent of Daily Volume 12% 10% mx *» 2% � Exhi6itA,6. Weekday Traffic Distribution Profile for Severe Congestion and Similar Speeds inEach Peak Period mm nm mm mm 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 *=,ofn"' -�—Freeway �O-mon-weeway The next step in the traffic flow assignment process is to determine which of the 16 traffic distribution profi|esshou|dbeasdgnedtoeachXDNetvvorkroadvvay|ink("XDNetwork"isthe^goography"osedby INRIX to define the roadways), such that the hourly traffic flows can be calculated from traffic count data supplied byHPMS. The assignment should beasfollows: w Functional class: assign based onHPK4Sfunctional road class n Freeway -access'contnoUedhighwoys o Non-freeway-aUcthermajorroadsandsteets w Day type: assign volume profile based ooeach day o Weekday (Monday through Friday) • 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 Z0/&� �{��oxo6/li(y3oorecuuMet hodology A,7 biip://roobi|ity.\umo.cdu/uonu/coo�ontioo-dotu/ path using speed data from 6a.m. tol0am.(morning peak period) and 3p.m.to7pm. (evening peak pehod). I) Calculate a free -flow speed during the light traffic hours (e.g, lO pm. to S a.m.) to be used as the baseline for congestion calculations. 3) Calculate the peak period speed reduction by dividing the average combined peak period speed bythe free -flow speed. Average Peak Speed = Period Speed Reduction Factor Pree'FlovvSpeed (10p.om.tn5a.ro.) For Freeways: o speed eduction factor ranging from 80%to I00% (no to |mm congestion) o speed reduction factor ranging from 75%to 90% (moderate congestion) o speed reduction factor less than 75Y6 (severe congestion) ForNon'Feevvayc o speed reduction factor ranging from 80Y6 to I00% (no to |ovv congestion) o speed reduction factor ranging from 65Y6to 80% (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 speed dataset. The peak period speed differential is calculated as follows: I) Calculate the overage morning peak period speed (6 a.m. to IO a.m.) and the average evening peak period speed (3 p.m. to7p.m.) 2) Assign the peak period volume curve based onthe speed differential. The lowest speed determines the peak direction. Any section where the difference in the morning and evening peak period speeds is6mph orless will beassigned the even volume distribution. A,8 J0l5Urban JQob��vScorecard ��oUzodo|ngy Truck -Only Volume Profiles New to the 2015 Urban MobilityScorecard is the use of truck -only volume curves. Themixed+ehide 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 in the mixed'vehidevo|ume process. The eiOhttruck'on|y profiles used to create the lS' minotetruckvo|umes are shown in Exhibits A-7through A'9. Thetruck'on|y profiles are identical for all congestion levels. Exhibit A-7.Veekday Freeway Truck -Traffic Distribution Profiles Hommmay ---&+—AM poau --*—PM Peak ~�ampxx pe»x Exhi6itA'O. Weekday Non -Freeway Truck -Traffic Distribution Profiles 0x00 zoo ^mo 6�00 uxm 10�00 12:00 14:00 z000 18:00 20�00 22:00 Hour of Day 20/5Urbun Scorecard Methodology A,9 Percent of Daily Vol Exhibit A-9. Weekend Truck -Traffic Distribution Profiles Hour of Day Step4. 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 toget aquantity ofvehide'houo; these were summed for all Z4hours across the entire urban area. Step 5. Establish Free -Flow Travel Speed and Time 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 UIVIS methodology, 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 of65 mph was placed on the freeway free -flow speed to maintain a reasonable estimate ofdelay; nolimit was placed onthe arterial street free -flow speeds. Step 6. Calculate Congestion Performance Measures The mobility performance measures were calculated using the equations shown in the next section of thismethodo|ogyoncethel5'minutedatasetofacLua|speeds,fee-f|ovvtmve|speedsandtraffic volumes was prepared. 20/5L�-bonMobility Jcoracuo/Methodology A,lO Step 7. Estimate Speed Data Where Volume Data Had No Matched Speed Data 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 one million in population. 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 http://mobility.tamu.edu/ums/congestion-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 on the area. The core counties ofthese urban areas (these include the counties with atleast 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. in the suburban counties (non -core), where less than 15 or 20 percent of the area's VIVIT was in a 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 lthrough 6were repeated for the non -core counties of these urban areas. |neach ofthe core counties, all ofthe unmatched HPIVIS sections were gathered and ranked inorder 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 25 percent of the lane -miles, 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 of unmatched speed data were ordered from most congested toleast congested based ontheir Travel Time Index value. Since the lane -miles ofroadway for these sections were not available with the INRIX speed data, the listing was divided into the same splits as the traffic volume data (ZS/I5/5Openent). (The Travel Time Index was used instead ofspeed because the TT| 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. Z0/5Urban Mobility Scorecard Methodology A-12 Calculation of the Congestion Measures This section summarizes the methodology utilized to calculate many of the statistics shown in the Urban Mobility Scorecord and is divided into three main sections containing information on the constant values, variables and calculation steps ofthe main performance measures ofthe mobility database. Not all of the measures are reported in the 2015 Urban Mobility Scorecord. In some cases, the measures below were last reported in the 2012 Urban Mobility Report (UMR); this is noted in the pages that 1. National Constants 3. Urban Area Constants and Inventory Values 3. Variable and Performance Measure Calculation Descriptions l\ Travel Delay Z) Annual Person Delay 3\ Annual Delay per Auto Commuter 4\ Total Peak Period Travel Time (last reported inJOIJ UMR) 5\ Travel Time Index G) 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 lO\ Truck Commodity Value (last reported in20I2UMR) ll) Number ofRush Hours lZ\ 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. Z0/5Urban Mobility Jc�o/-ecnrJMethodology &,13 b8p://nzohiiiiy.tunn/.edu/umo/cougcatioo'do n/ National Constants The congestion calculations utilize the values in ExhihitA,lU as national constants —values used in all urban areas toestimate the effect ofcongestion. ExhibitA,10. National Congestion Constants for 2025Urban Mobility Scorecard Constant Value Vehicle Occupancy Average Cost ofTime($I0l4)(2) Commercial Vehicle Operating Cost ($]0l4)(3) Total Travel Days (7x5Z) ' Adjusted annually using the Consumer Price Index. Vehicle Occupancy 1.2Spersons per vehicle $1J.67per person hour' $94.O4per vehicle hour' 364 days The average number ofpersons ineach vehicle during peak period travel isl25. Working Days and Weeks With the addition of the INRIX speed data in the 2011 UMR, the calculations are based on a full year of data that includes all days ofthe week rather than just the working days. The delay from each day of the week ismultiplied by52work weeks toannualize the delay. Total delay for the year isbased on364 total travel days inthe year. Average Cost ofTime The 2O14value ofperson time used inthe report is $17.G7per hour based onthe value oftime, rather than the average o/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,|4 Z0/5/�bun�&/b/�8/Scorecard �1c\hodo|ogy Urban Area Variables 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 (DVMT) is the average daily traffic (ADT) of a section of roadway multiplied bythe length (in miles) ofthat section ofroadway. This allows the daily volume ufall urban facilities tobepresented interms that can beutilized incost calculations. DVMTwas estimated for the freeways and principal arterial streets located in each urbanized study area. These estimates originate from the HPIVIS database 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 (HPyWS) (2,4). Estimates ofpeak 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 6a.m.and 1Oam.nr3 p.m.and 7p.m. isapeak-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 onanaverage day. The same NHT3data were also used toestimate 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 System dataset(l). The values are used tnestimate congestion costs and are not used toadjust the roadway capacity. ZD1JUrban Mobility Scorecard k4cthodo|ogy Variable and Performance Measure Calculation Descriptions 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) tothe calculation. DaUrVebicle'Boms of Delay ~~ Annual Person Delay 'Miles of Travel _ of Travel Speed ) ( Free -Flow Speed 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 by5Zweeks per year (Equation A,3). Annual Daily ~ 1.26P000mux Peromoo'Bnuro~~ ofDelay oo x 52Weeks 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 Zo/5Urban 8&ohid/yScorecard Methodology A,10 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 ofthe delay that occurs during the peak hours nfthe day (6:0O am.tol0:OOam.and 3:OOp.m.to7:0Opmjisassigned tothe pool ofcommuters. |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. Delay per AutoCocnznutec Peak Period Delay Remaining Delay) }f\ Population ) � Total Peak Period Travel Time (Lost reported /nthe 2D1ZUMR) Total travel time isthe sum oftravel delay and free -flow travel time. |nthe 2O22Urban Mobility 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, sowill 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 ofextra 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 aperson hours. Peak Period Daily Delay of Travel ��m�B�� \\\i Speed�— Daily Vehicle-NInes of Travel Free- Flow Speed Percent of Vehicle �25 Persons x MiiexufT�r� � per Vehicle During the Peak 8,|7 2U/5L��un&6»b/�ty 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 da\/speak hours (EquationA-6). Equation A,6converts vehicle hours toperson hours. Peak Free -Flow 1 Daily Percent ofVehicle �25p�rcoo� Travel Time � xVe�d���sx M��of Travel x Free-Flow�orYebd� (Person-Hours)ofTrav� Duc���tbe9eab — Trav�5peed 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. Total Daily Peak Period Travel Time (Minutes per Commuter) Travel Time Index Primar-�� Primary Road �� U - f������ ���� �i f��a� � � po��omh� �ea�oeb�^ �`�~^`^~ ^~'^ Free -Flo � TravelTime Travelrboe � [ � Auto Commuters ^ The Travel Time Index (I 11) compares peak period travel time to free -flow travel time. The Travel Time Index includes both recurring and incident conditions and is, therefore, anestimate ufthe conditions faced byurban travelers. Equation AO5illustrates the ratio used tocalculate the TOL The ratio has units oftime divided bytime and the Index, therefore, has nounits. This "unit|ess"feature allows the Index tobeused tocompare trips ofdifferent lengths toestimate the travel time inexcess ofthat experienced in free -flow conditions. The free -flow travel time for each functional class is subtracted from the average travel time toestimate delay. The Travel Time Index is calculated by comparing total travel time to the free -flow travel time (EqoationsA'8 and A,9). Travel Time Index = Travel Time Index = Peak Travel Time Free -Flow Travel Time DelayTime + Free -Flow Travel Time Free -Flow Travel Time 20/5Urbon Sco/ecuu/Metbodo|ogy The change in Travel Time Index values is computed by subtracting 1.0from all the TTI values snthat 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 ufS0percent compared toZ5percent). Comm Liter Stress Index The Commuter Stress Index ([S|) is the same asthe TD 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 2025Urban Mobility Scorecard. The PT|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'timefo/ l9out ofI0work days each month). For example, a PT|value of1.8Oindicates that a traveler should aUuvv 36 minutes to make an important trip that takes 20 minutes in low traffic volumes. The PT|va|ues in Table 3 are for freeways only. The Rlisthe 9S*percentile travel time relative to the free -flow travel time as shown in Equation A-10. TheZOl5UrbunMob0ityScorecordestimatesthePT|fo/thpsusingaverage|ink(XDNetvvork|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). 95thPercentile Travel Time Index (PTI) Free -Flow Travel Time (E�A-10) (E q�8,11) Where: PDmp =PT|for atrip (reported for freeways inTable 3ufthe 2DI5UMS);and PT|o"^ = Average of PTIs for all the XD Network links weighted by VIVIT in the urban area. Z0/5Urban Mobility Scorecard Methodology A,19 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. |talso quantifies and illustrates the re|ationshipbotwpenthefnee-f|nwtrave|time,ave/agutmve|time,8»mpeoenti|et/ave|dme,and95m percentile travel time. Carbon Dioxide /[0z Production and Wasted Fuel /[Ozwas last reported /n20l2UMR/ This methodology uses data from the United States Environmental Protection Agency's (EPA) MOtor Vehicle Emission Simulator (K8UVES)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 FHWA's HPMS, 2) INRIX traffic speed data, and 3)EPA'sMOVES 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 [Ozproduction. 2. Obtain O}z Emission Rates for Urban Area Group —emission rates (in grams per mile) were created for each ofthe 14groups from Step #1. l Fit Curves toO}zEmission 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 ofgas (or diesel for trucks) produced for the [O2emissions produced. 5. 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- flowspeedswhenfewcaoareonthenoad. Free -flow results are subtracted from congested - conditions /esukstoobta\n[Ozemissionsandfue|wastedduetucongestion. A,20 20/5{�bun�/o6�dMet hodology # of Days in the Year 100 a • 05- p.) 0 80 60 40 Exhibit A-11. Example of Morning Commute Travel Time Distribution Is Your Morning Commute Time the Same Each Day. o, It Varies! 20 20 minutes is your free -flow travel time (commute time when few other cars are on the road) Federal Holiday — you speed to work in 22 minutes because traffic is lightl Last year, before the construction - life was better then 30 30 minutes is your averageaveraqe travelI 2 0 morning workday trips in the year) 38 minutes is the worst day of the week_ Allow this much time to only be late for work one day a week (sometimes called the 80th percentile travel time) You have to leave home by 7a.m_ to be sure that you are at your job by 8 am (sometimes called the 95th percentile travel time, allowing this much time ensures you plan for the worst day of the month). Note that this is 3 times your free -flow travel time. Several of your trips from April to August were delayed by construction. 2 lanes were closed by a multi -vehicle crash on December 8th 40 Your Commute Time to Work (minutes) 50 Unexpected downpour on July 121'4 60 Who can forget that Jan 17e blizzard! z 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 O3zemissions, 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 "on." It was not feasible to use emission rates for every county in the United States, so 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, avehicle traveling lOOmiles with anA[FofIlpercent would travel 11ofthose 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 UMR from 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 A[Fs based on hourly temperature and relative humidity data from MOVES. They used this hourly data to calculate hourly ACFs,which they then weighted by hourly traffic volume data from MOVES and averaged for each month. To produce the weighted seasonal ACFs, researchers averaged these weighted month|yA[Fs over three-month periods for the seasons defined by MOVES. To group the counties (or urban areas) based on similar seasonal climates, researchers used temperature and relative humidity scatter plots tovisually 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 J0/JOihunMobility Scorecard 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 nfgroups 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'lIshows the groupings ofurban areas. EzhibdA-l2' 'The Continental United States with Each County Shaded by Grup ���' � _�� M, W W /,,001 independent Groups - Anchorage, ^x Honolulu, HI San Francisco, cx sonman' pn Seattle, WA Step 2. Obtain CO�2 Emission Rates for Urban Area Group TT| 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 [O/emissions asdescribed inStep 4. Researchers produced emission rates for every ACFvalue assigned tothe groups inStep l. For each A[Fvalue, researchers produced emission rates for each vehicle type, fuel type, and road type used in the UMR. 20/5Urbun Mobility Scmrrcom/McUhndo|ogy A-23 MOVES has many different vehicle classifications, but TTI's UMR has just three broad categories: light- dutyvebides, medium'dutytrucks, 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" hnm MOVES meet the description of each vehicle type used in TT|'s UMR (light - duty vehicles, medium'dutytrucks, and heavy-duty trucks). For example, both the combination short - haul and combination long -haul trucks qualify as heavy-duty trucks. 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 an exception, however). Todetermine which "SourueType"would supply the emission rates for a vehidetype,npsearcheochosethe"3ourceType"withthehighestpercentageofvehide-mi|esoft/ave| (VMT)within each UMRvehicle type. TO researchers used a different method for |ight+dutyvehides because not all "Suu/oeTypes 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 emissionratesforthisvehidetype(|ight'dutyvehic|es),researcherscombinedandvveightedthe emission rates of two different "SourceTypes" — passenger cars (59%) and passenger trucks (41%). Researcheousedon|ythepassengert/uck"SourceType,tosupp|ytheemioionratesforbcth passenger trucks and light commercial trucks because they have similar emission rates, and because passenger trucks account for more VK4T. Emission rates also differ for specific fuel types, and TTIresearchers selected afuel type for each vehicle type based on fuel usage data in MOVES. Given that light commercial trucks account for a small portion ofthe light -duty vehicle population, researchers used the gasoline emission rates torepresent all fuel usage for |ight'dutyvehides when calculating emissions. Researchers used the diesel emission rates to represent all fuel usage for medium'dutytrucks and heavy-duty trucks. TT| researchers ran MOVES for the appropriate vehicle types, fuel types, and road types to obtain emission rates ingrams per mile. 20/5Crhoo Mobility 3corc000/Mcthodcdngy /\-24 bUp:/dnobUity.tunzu.edu/umu/coogcsdoo-du1o/ Step 3. Fit Curves toCO2 Emission Rates TT|researchers developed curves tocalculate emission rates for agiven speed. Researchers later used the equations for each curve tocalculate emissions. MOVES produces emission rates for speeds of 2.5 to 75 mph in increments of five (except for 2.5 mph). Using[NicrosoftExoe|m,researoheoinitia|lynonstrucLedspeed'dependentemissionfactorcumesby fitting one to three polynomial curves (sp|ine)tothe emission rate data from MOVES (see ExhibitA'13 example). Researchers compared emission rates generated with the polynomial spline tothe underlying MOVES -generated emission rates. � 1000 � mm ~ � LU *uu um o n Exhibit A,13. Example Light -duty Vehicle Emission Rate Curve -set Showing Three Emission Rate Curves P 10 zo 30 wo so 60 m 80 Speed (mph) The polynomial spline that was deemed sufficiently accurate by researchers was a two -segment spline usingnne6*'on1erpolynomia|fortheU-3Umphsegmentandanother6�'onderpo|ynomia|fo/the3O —GOmphsegment Speeds over 6Oused the emission rates ofthe 3U-6Omph polynomial at6Omph. Note that these speeds are averages, and variability with speed (slope) isneg|igab|efor speeds greater than 6Omph. Lowe/avemgespeedshavehigherspeedf|uctations(ormorestop'and1go),vvhichcauses 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 system operation, it is desirable for urban areas to operate during the relatively free -flow conditions as much as possible. Thus, the authors capped emissions generation atapproximately 6Omph. A�25 20/5[��on�/ob/�/yScorecard K3ctbodology Step4` Calculate [O2Emissions and Fuel [onsunmotonDuring Congested Conditions Tocalculate emissions, researchers combined the emission rates with hourly speed data supplied by INRIX and hourly volume data supplied by Highway Performance Monitoring System (HPIVIS). Researchers used SAS' to automate the process of calculating emissions. This process involves 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 the/evviUbeaseparatespeedandvo|umeva|ueforUght'dutyvehides,medium'dutytrucks,andheavy+ dutyt/ocksforeachl5'minutesofeachdayofthevveek.Toaccountfortheseasona|dimatechanges, 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 of each season, vehicle type, and day ofthe week toproduce the annual emission estimates. Researchers produced the annual emission estimates for congested conditions, which includes free - flow. 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, Step 5. Estimate the CO Estimate Wasted Fuel and COhDue toCongestion 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 [O2emissions from congestion, researchers subtracted the free -flow condition emissions estimates from the congested -conditions emissions estimate from Step #4. This is shown in Equation A,1I. 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 8dditinoal00c Annual COz Annual CO2 Because of Emissions Produced — Emissions Produced Cunuestion ioCoocestion inFree-Flow Conditions (E�A-12) }\'26 20/5Urban ��/b/�(yJcorMethodology Annual Fuel fn������ Annual Fuel ~~ Coosuroedio — ��m�dbaCoosuo`nd VYaxt*dtoCnn�ss�oo Cmm�ao�nu inYrrr'BmvCondi�oos AWord about Assumptions /nthe [02and 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 ofStep S. 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) ofemissions produced during hee4low. 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[Ozproduction. 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 "addhiona|"COzproduced 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 COzproduced due tocongestion. Similarly, ifthere 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 atfree4lovv. While these are notable considerations and may betrue for specific corridors, the UMSanalysis isat the areawidelevel for all principal arterials and freeways and the assumption is that overestimating and underestimating will approximately balance out 20/5Urban /hobdY/vScorecard Methodology /\'27 over the urban area. Therefore, the methodology provides acredible method for consistent and rep|icab|e 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 tothe travel speed calculations. The following sections and Equations A,14thnough A, 16show how to calculate the cost ofdelay and fuel effects of congestion. Passenger Vehicle Delay Cost. The delay cost isanestimate ofthe value oflost time inpassenger vehicles in congestion. Equation A'14 shows how to calculate the passenger vehicle delay costs that result from lost time. Dai��s��e��� Value Vehicle �oou�Ps��e� -Annual~ Delay Cost ~� Hours ofDelay x Person Time x Occupancy xCmnveruzun (Eq. A4) ($ /bnur) (perm/vehicle) Factor Passenger Vehicle Fuel Cost. Fuel cost due tocongestion iscalculated for passenger vehicles in EquationA,l5. This is done by associating the wasted fuel, the percentage of the vehicle mix that is passenger, and the fuel costs. Daily Percent �nno�� Da ` Gasoline Annual ~~ Wasted x Passenger x » Fuel Cost Cost Conversion Factor (Eq. A-13) Vehicles Truck orCommercial Vehicle Delay Cost. The delay cost isanestimate ofthe value oflost 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. D��Coor��eb��e Value Annual �uom�Cooznu��e� ~~ �~ Delay Cost BomrsmfDelu�� a� x CConversione��x Cocmec�u (Eq. i\-4) /$/hour) Factor 20/5Urban Mobility 3cnrucun/Methodology A,28 Truck orCommercial Vehicle Fuel Cost. Fuel cost due tocongestion is calculated for commercial vehicles inEquation A'l6. This is done by associating the wasted fuel, the percentage ofthe vehicle mix that iscommercial, and the fuel costs. Da�v�mm Percent Annual Daily Diesel Annual ~~ Wasted x Commercial x X Fuel Cost Coot Conversion Factor (8g. A-13) Vehicles Total Congestion Cost. Equation A,18combines the cost due totravel delay and wasted fuel to determine the annual cost due tocongestion resulting from incident and recurring delay. Annual Cost Due to Annual Passenger Annual Passenger Vehicle Delay Truck Commodity Vulue(Lost reported in2OlZUMR Annual Comm Annual Comm Veh Delay Cost f Veh Fuel Cost (Eq. A -lb) (Eq A'17) (E q.8,16) The data for this performance measure came from the Freight Analysis Framework (FAF) and the Highway Performance Monitoring System (HPK85) 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. I. Calculate the national commodity value for all truck movements 2. Calculate the HPMS truck VMT percentages for states, urban areas and rural roadways 3. Estimate the state and urban commodity values using the HPMS truck VMTpercentages 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 in13lregions ofthe U.I The database contains aI3lbyl3lmatrix oftruck goods movements (tons and dollars) between these regions. Using just the value ofthe 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'counted). The FAFdatabase has commodity value estimates for different years. The base year for FAF-3 is 2007 with estimates of commodity values in 2010 through ZO4Oin5-yearincrements. 20/5Urban Mobility Scorecard Methodology /\-29 Step 3—Truck VK8TPercentages. The HPyWSstate tmckVK4Tpercentages are calculated inEquation A, 19usingeachstate'sestimatedtruckV&1Tandthenationa|tmckVK8T. This percentage will beused to approximate total commodity value at the state level. State Truck State Truck- VMTPeoceutage \U. S.Truck VM7/ xI0096 The urban percentages within each state are calculated similarly, but with respect to the state VMT. The equation used fortheurban percentage isgiven inEquation A,20. The rural t/uckVIVITpercentage for each state isshown inEquation A,21. State Urban Truck 0MTPercentage State Truck State rb Truck VNIT State Rural Truck ~~1OO"/o— 3LateUrban Truck VNIT Percentage VMT Percentage The urban area tmckVMTpercentage isused inthefina|calculation. ThetmckVK1Tineach urban area in a given state is divided by all of the urban truckVMTforthe state (Equation A-20). Urban Area,, Urban Area Truck Truck VMT VM79ercentage State Urban ( Truck VMT, Step 3—Estimate State and Urban Area VK8Tfrom Truck VIVITpercentages. The national estimate of truck commodity value from Step 1 is used with the percentages calculated in Step Z to assign a VPWT' basedcommodityva|uetotheurbanandrum|roadvvayswithineachstateandtoeachurbanaea. State Urban Truck D�3LTruck �ateUrban VMT'8zsed � � Coo���odU�Value Truck Percentage Commodity Value ~ State Rural Truck D�S�Truck State Rural VK1T'8ased ~~ � Commodity Value TruckPerceutage Commodity Value (Eq. A-Z3) 2015 Urban Mobility SrvrecondMc1bodm1ogy /\'30 Urban Area Trock State Urban Urban Area Vy&7'Buned TruckVM7-Basedx Truck VMT Percentage Commodity Value Commodity Va ue Step 4—Calculate Ohgin/Destinaton-BbsedCommodity Value. The results inStep 3show the commodity values for the U.S.distributed based onthe truck VIVITflowing through states inboth rural portions and urban areas. The Step results place equal weighting on atruck mile in a rural area and a truck mile in an urban 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.I The percentage ofthe total Ulorigin/ destination'basedcommndityva|uescorrespondingtoeachoftheFAFregions,shmwninEquationsA,Z6 andA'Z7,wasca|cu|atedandthesepercentageswereusedtoredist/ibutethenadona|freight commodity value estimated in Step I 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 origin/destination'basedcommodity data. Urban areas not included inaFAFregion were assigned a commodity value based on their truck VIVIT relative to all the truck VMT which remained unassigned to a FAFregion (Equation A'Z9). FAF Region O/D -Based. Commodity Value % FAFR��u D'BauedComomdity Value D.S.O/D-Baxed Commodity Value EAF Region O/D -Based FAF ReginoO{D'Based x D.S.0/D'8aoed Commodity Value Commodity Value % Commodity Value O/D -Based FAPRegion I FAFRegion 2 Commodity Value for State 1 Value fi-om State 1 Value from State 1 Non-EAygegino Remaining Uaassi�,rned Urban Area D/D-8as:d State IF8FO/D'Based x Commodity Value from State I CommodiqValue Z0/5Urban Mobility Scorecard Methodology Nuo-F8EUrban &reaTruck \ VMTyerceotagm Roouaioio8Unassigned State 1 Truck VNIT9erceotage / (Eq.A-27) (E�A-39) Step 5—Final Commodity Value for Each Urban Area. The VIVIT-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 in the Urban Mobility Report. Final Commodity Value for Urban Area Urban Area Urban Area \ VMT'8aoed + O/D -Based Commodity, Value Commodity Value) Number of "Rush Hours" (Congested Hours), Congested Lone -Miles, and Congested VMT The number of "rush hours" (congested hours) is computed with a new method in the 2015 Urban Mobility Scorecard. For each XD 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, thesectinnofroadismarkedas"oongested^forthat1S'minuteperiod(9).|f3Opo/centoftheurban 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 of peak period vehicle -miles of travel and lane -miles are compared with the peak -period totals to determine the percent that iscongested. A,32 2015[�bun&8,6V>A/Scorecard ��ctbodo|ngy References 1 Federal Highway Administration, "Highway Performance Monitoring 3ystem,"1982to20lOData. November2OlI. Available: http://www.fhwa.dct.gov/po|icynformadon/hpms.cfn. Z McFar|and,VKF. M.[hui"The Value ofTravel Time: New Estimates Developed Using aSpeed [hoicePWodei^ Transportation Research Record N.11l6,Transportation Research Board, Washington, DI, 1987. 3 Ellis, David, "Cost Per Hour and Value of Time Calculations for Passenger Vehicles and Commercial Trucks for Use inthe Urban Mobility Report." Texas Transportation Institute, ZO09. 4 Populations Estimates. U.lCensus Bureau. Available: wwwzensus.gov 5 2O09National Household Travel Survey, Summary ofTravel Tends. Available: http://nhtsomigov/index.shtm| 6 American Automobile Association, Fuel Gauge Report. 2811.Avai|a6|e: wwvv.fue|gaugereport.com 7 Means ofTransportation toWork. American Community Survey 2OO9. Available: www.censucgov/acs/www 8 Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies. Strategic Highway Research Program, 2 (SHKP2) Report S2'LO3'RR'I. National Research Council, Transportation Research Board, Washington, DI,2Ul3.Available: http://on|inepubs.t/b.org/onUneVubs/shrp2/SHRP2 S2'L03-RR'1.pdf 9 Turner, S, R. WlarOiotta,and T. Lomax. Lessons Learned: Monitoring Highway Congestion and Reliability Using Archived Traffic Detector Data. FHVVA'HOP'05'003. Federal Highway Administration, Washington, D.[,October ZUO4. 10 Estimates ofRelative Mobility in Major Texas Cities, Texas Transportation Institute, Research Report 313'lF,1982. 20/jUrban Mo8//ivScorecard Methodology A'33