Moving people and goods by air, water, road and rail. The degree to - - PowerPoint PPT Presentation

Moving people and goods by air, water, road and rail. The degree to which transportation infrastructure systems serve the US economic and business community

• bjectives.



In 2000, The World Bank projected the world economy to grow 33% between years 2000 and 2010, increasing from $31.8 trillion to $40 trillion.



It reached $60.5 trillion in 2008 ($78.9 trillion in 2011 est).



By the year 2050, the world economy is projected to increase to between $135 trillion to $216 trillion. Are our infrastructure systems ready for the growth? Are the investments in US infrastructure adequate?

 Transparency  Accountability  Gaps

› Currently no “rigorous” index for measuring US

infrastructure, specifically in relation to economic growth

› Need a well-defined methodology for creating an

index

› Existing methods for creating indices should be

applied

 Develop methodology for constructing a US

Transportation Performance Index (TPI)

› Repeatable › Transparent › Use to evaluate trends in infrastructure

performance

 Main goal of index: measure the effect of

infrastructure performance on economic prosperity

 F

r agile F

• undations (1988)

“the a mo unt o f infra struc ture o r its c o nditio n did no t c a pture the a b ility o r c a pa b ility o f the infra struc ture to de live r the se rvic e e xpe c te d o r re q uire d”

 NRC study (1997)

“the de g re e to whic h the syste m se rve s multile ve l c o mmunity o b je c tive s. I de ntifying the se o b je c tive s a nd a sse ssing a nd impro ving infra struc ture pe rfo rma nc e o c c ur thro ug h a n e sse ntia lly po litic a l pro c e ss invo lving multiple sta ke ho lde rs”

 T

his study

“the de g re e to whic h the infra struc ture syste m se rve s U.S. e c o no mic a nd multi-le ve l b usine ss c o mmunity

• b je c tive s”

• 1. Definitions
• 2. Geographic Samples
• 3. Create Models of the Sectors and Criteria
• 4. Identify Indicators
• 5. Explore Data Sources & Assemble Data
• 6. Weight the Indicators
• 7. Compute the Index with Economic Correlation

Phases

Initiation Phase – Prototype transportation index National Complete Transportation Performance Index

(TPI) (1990-2008, 2015 projections)

State by State Transportation Performance Index (1995,

2000, 2007, 2015 projections)

Update TPI for 2009

 Based on MSAs (366 in 2007)

›

Organized based on sector

›

Stratified Random

›

Weighted based on economic contribution

 MSA Sample for Transportation = 36 total

›

Classifying MSA by Economic Sector

›

Classifying MSAs by Population

›

Combining Population and Economic Sector Classifications

›

Determining Sample Size by Economic Classification and Population Group

›

Selecting MSAs for the Sample

 Five-step process  Brainstorming (Literature review)  Exploring data (Initiation phase)  Expert meeting  Stakeholders workshops (Chicago, Atlanta,

Houston, San Jose)

 Revisions and data assembly

 Supply- availability and coverage  What geographical area is covered?  Quality of Service- inconvenience cost of

disruption, and reliability

 How well service is provided?  Efficiency- the cost of service  Does the service provide full value for cost?  Utilization- whether growth can be

accommodated

 How fully the existing facilities are used?

Supply

• Highway Density
• Transit Density
• Airport Access
• Airport Capacity
• Rail Density
• Waterway Density
• Port Access
• Intermodal –

Freight Access

Quality of Se r vic e

• Travel Time

Reliability

• Highway Safety
• Road Roughness
• Bridge Integrity
• Air Congestion
• Air Safety
• Rail Safety
• Waterway

Congestion

• Transit Safety

Utilization

• Highway Reserve

Capacity

• Air Reserve

Capacity

• Transit Reserve

Capacity

• Rail Reserve

Capacity

Supply

• Highway Density
• Transit Density
• Airport Access
• Airport Capacity
• Rail Density
• Waterway Density
• Port Access
• Intermodal –

Freight Access

Quality of Se r vic e

• Travel Time

Reliability

• Highway Safety
• Road Roughness
• Bridge Integrity
• Air Congestion
• Air Safety
• Rail Safety
• Waterway

Congestion

• Transit Safety

Utilization

• Highway Reserve

Capacity

• Air Reserve

Capacity

• Transit Reserve

Capacity

• Rail Reserve

Capacity

Safe ty Infr astr uc tur e Condition Conge stion Re duc tion Syste m Re liability F r e ight Move me nt and E c onomic Vitality

Indicator Measure

Highway Density Transit Density Airport Access Airport Capacity Rail Density Waterway Density Port Access Freight Access Travel time reliability Safety Road Roughness Bridge Integrity Air Congestion Air Safety Rail Safety Waterway Congestion Transit Safety Highway Reserve Capacity Air Reserve Capacity Transit Reserve Capacity Rail Reserve Capacity Route miles per 10,000 population Miles of transit per 10,000 population % of population within 50 miles of major airport AAR/ADR per hour Route miles per 10,000 population Miles of inland waterways per sq mi Distance to closest international port

Numb e r o f fa c ilitie s pe r 10,000 po pula tio n

Travel time index Fatalities per 100 million VMT % of road with IRI > 170 in./mi. % of bridges structurally deficient or obsolete % on time performance for departures Runway incursions per million operations # incidents per million operations Average lock delay per tow # incident per million PMT % of lane miles at level of service ‘C’ or better % capacity used between 7am to 9pm PMT per capacity Ton-miles per track mile

 Population over 1 million (all MSAs have

airports) – 23 MSAs; 21 indicators

 Population under 1 million with a primary

airport – 7 MSAs; 18 indicators

 Population under 1 million without a

primary airport – 6 MSAs; 15 indicators.

 Bureau of Transportation Statistics (BTS)  National Transportation Atlas Data (NTAD)  Highway Performance Monitoring Systems (HPMS)  National Bridge Inventory (NBI)  National Transit Database (NTD)  Aviation System Performance Metrics (ASPM)  FAA’s Runway Safety Database  Terminal Area Forecast (TAF)  Fatal Accident Reporting System (FARS)  Federal Railroad Administration (FRA)  U.S. Army Corps of Engineers  U.S. Bureau of Census

• 1990 to 2008
• 10,440 pie c e s o f da ta
• >10GB

Indicator #9 Highway Congestion Definition: The travel time reliability is measured by the Travel Time Index (TTI) which is the ratio of peak period travel time to free flow travel time. Why it’s important: The TTI expresses the average amount of extra time it takes to travel during peak hours relative to free‐flow travel. A TTI of 1.3, for example, indicates a 20‐minute free‐flow trip will take 26 minutes during the peak travel times, a 6‐minute (30 percent) travel time penalty. Criteria metric: Quality of Service Historical Values: Observations:  Congestion problems tended to be more severe from 1990 to 2007 in large urban

• areas. The average increase in the travel time was about 10% during this period.

 As economy goes down, travel time indices slightly decrease in 2006 and 2007, probably due to less traffic on the highways. Contribution to Index: MSA type 00 (population under 1 million without primary airport) – 0.000 MSA type 01 (population under 1 million with primary airport(s)) – 0.000 MSA type 11 (population over 1 million with primary airport(s)) – 0.113 The weight factors are determined and calculated from Analytical Hierarchical Process based on a survey of U.S. Chamber members. Primary data sources: Texas Transportation Institute, The Annual Urban Mobility Report, available at http://mobility.tamu.edu, currently available from 1982 to 2007. Data issues &

• pportunities

Detailed data are available only for most urbanized areas over 1 million population based on the availability of data provided.

1.00 1.20 1.40

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Year Over 1 million with one or more airports (MSA Type 11)

 Re vie w o f the type o f da ta a nd the

ra ng e o f the da ta

 Gra phs o f indic a to rs b y MSA a nd o ve r

time to c he c k fo r c o nsiste nc y.

 Sc a le a nd L

e ve l o f Ag g re g a tio n

 Missing a nd E

rro ne o us Da ta

› Da ta no t re po rte d o r c o lle c te d › Cha ng e s in fo rma t o r inc o nsiste nt re po rting › E rro rs in so urc e s da ta

 F

• re c a sting a nd Pre dic tio n

 I

nstitutio na l Co nstra ints

I nte rmo da l c o nne c tivity (ra mps/ 10,000 po pula tio n)

0.000 0.050 0.100 0.150 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year

Under 1 million with no airports (MSA Type 00)

0.00 0.20 0.40 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year

Under 1 million with one or more airports (MSA Type 01)

0.08 0.09 0.10 0.11 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year

Over 1 million with one or more airports (MSA Type 11)

 Ca pturing

› I nte ra c tio ns a mo ng mo de s › Diffe ring sc a le s › Diffe ring g e o g ra phy

 Re fe re nc ing syste ms  Pre dic ting future va lue s  Ac c e ss to pe rfo rma nc e da ta  Pro a c tive c o nve rsa tio ns o n the ne xt

g e ne ra tio n pe rfo rma nc e me a sure s

 Use Analytic Hierarchy Process for weighting

• f indicators

 Pairwise comparisons completed by

stakeholders

 Comparion and Expert Choice Software  Result - final combined weight for each

indicator

• Sample pairwise comparison survey

question in Comparion

25

 Import pairwise comparison values

Must b e le ss tha n 0.1 fo r c o nsiste nc y

0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 Inland Waterway Density Airport Access Waterway Congestion Air Congestion Air Utilization Road Roughness Airport Capacity Port Access Rail Safety Rail Utilization Transit Utilization Intermodal Freight Access Transit Density Rail Density Air Safety Transit Safety Highway Safety Bridge Integrity Highway Density Travel Time Reliability Highway Utilization

 Step 1 – Normalize the data

› 1 is desirable and 0 is undesirable › 2000 as the base year

 Step 2 – Correlate indicator to type of MSA

› Adjust indicator weights to reflect the fact that not

all data is collected for all MSAs in the sample

 Step 3- Compute index

› For each MSA type

 For each MSA

 For each indicator

 (Indicator Weight x normalized indicator measure x contribution to the economy)

42.00 44.00 46.00 48.00 50.00 52.00 54.00 56.00 58.00 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

T r anspor tation Pe r for manc e Inde x

T ra nspo rta tio n I nde x Pre vio us Ob se rva tio ns

 L

a rg e r is b e tte r, sma lle r is wo rse

 T

he re is no sc a le (just like the Co nsume r Pric e I nde x o r the Do w Jo ne s I ndustria l Ave ra g e )

 95% c o nfide nc e inte rva l - +/ - 2.5  A c ha ng e in o ne indic a to r in o ne MSA

ha s little impa c t o n the T PI



Se c urity.



Susta ina b le infra struc ture



Burde ns o f re g ula tio n.



Burde nso me pro je c t de live ry pro c e ss



Sig nific a nt c ha ng e s in funding (Hig hwa y T rust F und, Avia tio n T rust F und a nd I nla nd Wa te rwa y T rust F

• und. )



I na b ility o f lo c a l, sta te a nd re g io na l g o ve rnme nts’ to ma tc h fe de ra l funds



Citize ns’ unwilling ne ss to suppo rt infra struc ture impro ve me nts



De la ys in pa ssing a utho rizing le g isla tio n. (e .g . SAF E T E A-L U e xpire d Se pte mb e r 2009)



Sig nific a nt inc re a se s in the c o st o f c o nstruc tio n, re pa ir, a nd ma inte na nc e in re a l do lla rs.



I nc re a sing a wa re ne ss o f infra struc ture issue s.



Sta te spe c ific initia tive s



I mpro ve d o pe ra tio ns (mo re thro ug hput), multi-mo da l a ppro a c he s, re g io na l a nd c o rrido r issue s, impa c t o f b o ttle ne c ks, a nd syne rg ie s b e twe e n mo de s.

40.00 45.00 50.00 55.00 60.00 65.00

T r anspor tation Inde x

E q ua l b y We ig ht E q ua l b y T ype We ig hte d b y Pa sse ng e r Mile s We ig hte d b y T

• n Mile s

E q ua l b y Crite ria AHP We ig hts

90.00 100.00 110.00 120.00 130.00 140.00 150.00

% of 1990 Value

T ra nspo rta tio n I nde x T ra nspo rta tio n I nde x - Mo ving Ave ra g e Po pula tio n Pa sse ng e r Mile s T

• n Mile s

10000 20000 30000 40000 50000 60000 70000 42.00 44.00 46.00 48.00 50.00 52.00 54.00 56.00 58.00

T r anspor tation E xpe nditur e 2000 $m T r anspor tation Pe r for manc e Inde x

T ra nspo rta tio n I nde x F e de ra l Spe nding (2000$m)

0.00 0.50 1.00 1.50 2.00 2.50 42.00 44.00 46.00 48.00 50.00 52.00 54.00 56.00 58.00 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Re por t Car d GPA T r anspor tation Pe r for manc e Inde x

T ra nspo rta tio n I nde x ASCE Re po rt Ca rd

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

T r anspor tation Pe r for manc e Inde x

E xtr apolate d T r anspor tation Pe r for manc e Inde x (T PI)

T ra nspo rta tio n I nde x Sig nific a nt inve stme nt No -ne w inve stme nt Sta te o f Go o d Re pa ir

 Pa st re se a rc h ha s a tte mpte d to

c o rre la te infra struc ture spe nding a nd e c o no mic g ro wth

 F

• llo wing Sa la -i-Ma rtin (1994) a nd

Sa nc he z-Ro b le s (1998), g ro wth mo de l fo rm is: ln GDP pe r c a pita

= f (I

nde x, GDP (le ve l), Go ve rnme nt po lic y, Po pula tio n he a lth)

GDP per Capita Coefficients* Transportation Index ** 0.0037 Real GDP 0.6120 Federal debt ‐0.0025 R‐squared 0.9953

*All coefficients significant at 0.99. **Three year lag

Also positive correlation between the index and foreign direct investment.

20 30 40 50 60 70 80 90

Ala b a ma Ala ska Arizo na Arka nsa s Ca lifo rnia Co lo ra do Co nne c tic ut De la wa re Distric t o f Co lumb ia F lo rida Ge o rg ia Ha wa ii I da ho I llino is I ndia na I

• wa

K a nsa s K e ntuc ky L

• uisia na

Ma ine Ma ryla nd Ma ssa c huse tts Mic hig a n Minne so ta Mississippi Misso uri Mo nta na Ne b ra ska Ne va da Ne w Ha mpshire Ne w Je rse y Ne w Me xic o Ne w Yo rk No rth Ca ro lina No rth Da ko ta Ohio Okla ho ma Ore g o n Pe nnsylva nia Rho de I sla nd So uth Ca ro lina So uth Da ko ta T e nne sse e T e xa s Uta h Ve rmo nt Virg inia Wa shing to n We st Virg inia Wisc o nsin Wyo ming T r anspor tation Inde x

1995 2000 2007

30 40 50 60 70 80 90

Nor th Dakota South Dakota Ne br aska Montana Iowa Kansas Ve rmont Maine Wyoming Minne sota Or e gon Vir ginia Utah Idaho Alaska Oklahoma Washington Mississippi Color ado Indiana Arizona Mic higan Alabama T e nne sse e South Car

• lina

Ge or gia Ohio Missouri Ke ntuc ky Ne w Hampshire T e xas Mar yland Illinois We st Virginia De lawar e Rhode Island Wisc onsin L

• uisiana

Pe nnsylvania Arkansas F lor ida Ne w Yor k Conne c tic ut Nor th Car

• lina

Ne w Me xic o Massac huse tts Califor nia Ne vada Hawaii Ne w Je r se y Distr ic t of Columbia

State - by- State T r anspor tation Inde x

2007

 Ob je c tive : to examine the effect

environmental influences has on the relationship between GDP per capita and TPI at the state level.

 Used Data Envelopment Analysis to compute

an efficiency score for each state.

 Environmental adjustment (population, density,

growth, usage)

 Outputs – ln GDP per capita  Inputs – debt, life expectancy

 Delaware’s

comprehensive efficiency stayed constant, being a benchmark for all 3 data years or having an efficiency value of 1.00. The TPI for Delaware for the data years are:

• 1995: 54.70, 34th Rank
• 2000: 57.11, 28th Rank
• 2007: 57.43, 35th Rank

• Relate indices to investments and policies
• Develop a strategy for annual updating including

refining the indices

 I

mpo rta nt to c a pture te mpo ra l a nd spa tia l va ria b ility (use thre sho ld)

 De c isio n ma ke rs a re g o o d a t ma king do

(Ya nke e ing e nuity)

 L

• ts o f da ta , q ua lity is q ue stio na b le

 Ha ving a visio n is pro b a b ly the mo st

e ffe c tive to o l



Po st Do c to ra l Re se a rc he r – Qia ng L

i,



Gra dua te Re se a rc h Assista nts - Mic he lle Oswa ld a nd Mo si L

• ndo n



Unde rg ra dua te Re se a rc he rs - Jo na tha n Ca lho un, T

a g g a rt K . F

• ulke



T e a m Me mb e rs - Mic ha e l Ga llis, E rik K re h, T

• m Ska nc ke , Susa nne

T rimb a th



US Cha mb e r o f Co mme rc e

› Ja ne t K a vino ky › Murphie Ba rre tt 

Wo rksho p pa rtic ipa nts



T ra nspo rta tio n E xpe rts

› Ja me s Co rb e tt › Ma rk Ha nso n › Ashish Se n 

Suppo rt – US Cha mb e r o f Co mme rc e , US De pa rtme nt o f E duc a tio n Gra dua te Assista ntship in Are a s o f Na tio na l Ne e d (GAANN), US De pa rtme nt o f T ra nspo rta tio n (UT C pro g ra m), a nd De pa rtme nt o f Civil E ng ine e ring a t UD.

F

• r mo re info rma tio n se e : http:/ / www.usc ha mb e r.c o m/ lra / tra nspo rta tio n-inde x