It’s About Time: Investing in Transportation to Keep Texas Economically Competitive

Appendices
M A R C H 2 011

It’s About Time: Investing in Transportation to
Keep Texas Economically Competitive

It’s About Time: Investing in Transportation to Keep Texas Economically Competitive

APPENDICES
TABLE OF CONTENTS

Page
Appendix A – Pavement Quality .............................................................................................. A-1
Appendix B – Bridge Quality ................................................................................................... B-1
Appendix C – Urban Traffic Congestion .................................................................................. C-1
Appendix D – Rural Connectivity ............................................................................................. D-1
Appendix E – Additional Revenue Sources for Pavement and Bridge Maintenance ................ E-1
Appendix F – Funding Transportation Improvements .............................................................. F-1
Appendix G – Estimating Vehicle Operating Costs and Pavement Deterioration ..................... G-1

The 2030 Committee Report and Executive Summary can be found on the 2030 Committee’s
website at: texas2030committee.tamu.edu.

ii


APPENDIX A – PAVEMENT QUALITY
by
Zhanmin Zhang, Associate Professor
Michael R. Murphy, Research Fellow
Robert Harrison, Senior Research Scientist
Center for Transportation Research
The University of Texas at Austin

The pavement maintenance and rehabilitation (M&R) needs consist of two parts
that will be analyzed separately: 1) the needs to maintain the existing pavements of
TxDOT highway network; and 2) the needs to maintain newly added highway pavements
from the mobility analysis. Both parts of the needs were established based on the four
predefined analysis scenarios. The needs are expressed in term of 2010 costs.
1) Needs to Maintain the Existing Pavements of TxDOT Highway Network: The
needs analysis of existing pavements will be based on historical data from the TxDOT
Pavement Management Information System (PMIS). Using the PMIS data and calibrated
pavement deterioration models developed at UT, the average condition of the pavement
network for the base year (2010) was first calculated. The base-year average condition
was then compared with the scenario goals, to determine the difference between them for
each PMIS pavement section. This difference was used to determine the M&R projects
required for the base year. Finally, combining unit cost information with the required
M&R projects produced the base-year pavement needs in dollars. This process continued
as a loop for the whole analysis period from year 2011 to 2035, yielding the pavements
needs for each individual year and the total pavement needs for the analysis period. The
overall analysis procedure is illustrated in Exhibit A1.
2) Needs to Maintain Newly Added Highway Pavements from the Mobility
Analysis: The M&R needs for newly added pavements were based on the information
produced from the TTI mobility analysis. The information on newly added pavement
lane-miles is provided by the mobility research team. Once the lane-miles are determined
for each year of the analysis period, an average cost approach was employed to determine
the M&R needs.
Basic assumptions for the pavement need analysis include: 1) only state-
maintained highways are considered; 2) toll-roads, such as the Trans-Texas Corridor, are
self-sustainable; 3) costs include not only the pavement materials but also other costs that
are required to deliver the pavement as a completed project; 4) truck size and weight
remain unchanged over the analysis period.

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Pavement Network
Data Source: PMIS
Scope: On-system Highways
Analysis Block: by District
Average Deterioration Rate
Data filter: Sections w/o M&R
Time Horizon: 10 years Base Year Network Condition
Analysis Method: Statistical
Measurement: PMIS Condition Score
Calculation Method: Length-weighted
Average Condition Drop
from previous
Condition Prediction Models year to current
Model Format: Sigmoidal Curve
Method: Calibration Traffic
Stratification: by pavement type
Next Year Network Condition

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& traffic
Unit cost for
Measurement: PMIS Condition Score
M&R
Calculation Method: Length-weighted
Network Goal Average
Goal Defined for
Each Scenario
Assignment of M&R Prioritization of Sections
M&R Alternatives: Do Nothing, Ranking Index: Combined Index of
Candidate Project
Preventive Maint., Light Rehab., Condition Score, Condition Drop, and
Selection
Medium Rehab., Heavy Rehab. Traffic

Updated Network Condition
Estimated Needs Update Method: Gains from M&R Next Year
for Analysis Year Calculation Method: Length-weighted

Exhibit A1. Methodological Framework for the Analysis of Existing Pavements.


I. NEEDS ANALYSIS FOR EXISTING PAVEMENTS
The needs analysis of the existing pavements of TxDOT’s highway network has been
addressed with the development of a methodological framework by the Transportation
Infrastructure and Information Systems (TIIS) Lab of the Center of Transportation Research
(CTR). Major components of the methodological framework are shown schematically in
Exhibit A1 and discussed as follows.

Pavement Network

The pavement network of the analysis concerned the existing pavements under TxDOT’s
jurisdiction and in particular the highway network whose sections are part of the existing PMIS
database. The most current version of the PMIS database was used in the analysis, based on the
2010 data collection. The analysis blocks of the network were TxDOT’s 25 districts.

Base Year Network Condition

The base year of the analysis was 2010. The condition of the entire state’s pavement
network was initially determined based on the individual scores of the pavement sections in the
PMIS database. The Condition Score of these sections was used as the performance
measurement index, and the state’s network condition was determined by averaging the
individual Condition Scores of all the sections in all 25 districts, weighted by their respective
length and number of lanes (aggregated in one measure, i.e., section lane-miles).

Average Deterioration Modeling

Before planning for the Maintenance and Rehabilitation (M&R) actions for the road
network, the deterioration process of the pavements was studied in order to understand when
their condition would reach a critical level that would trigger intervention. The process that was
followed in order to calculate the average yearly deterioration rate consisted of a number of steps
as explained in the following.
Data filtering: A dataset was queried from the PMIS for a period of 10 years (1995 to
2005). The dataset contained the following information: section reference markers, pavement
type, Annual Average Daily Traffic (AADT), Condition Score, Distress Score and Ride Score.
The deterioration rate was defined as the difference in the pavement condition between
consecutive years. Since any M&R action would result in an improvement of the condition, the
dataset was filtered in order to exclude these effects. The filtering was carried out by removing
the data entries that showed condition improvement between two consecutive years.
Pavement stratification: It is well known that rigid pavements and flexible pavements
have different load distribution mechanisms. Moreover, for different Highway Functional
Classes, the pavement structures, which are usually designed as a function of the traffic, are also
different. In this study, a statistical analysis was carried out to analyze the deterioration rate
distribution for the different structure types and pavement functional classifications. As a result,
nine broad groups were defined:

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 Group 1: flexible interstate highways, flexible US highways.
 Group 2: flexible state highways.
 Group 3: flexible farm-to-market and flexible others.
 Group 4: CRCP-interstate highways, CRCP US highways.
 Group 5: CRCP state highways.
 Group 6: CRCP farm-to-market and CRCP others.
 Group 7: JCP interstate highways.
 Group 8: JCP US highways.
 Group 9: JCP farm-to-market.

These nine groups were found to have distinctive deterioration rates; and therefore a different set
of models were developed for each group.
Climatic regions: It is also known that the daily temperature range and the precipitation
play an important role in the pavement deterioration process. As a result, instead of developing
pavement condition models for every district in Texas, these models were developed instead for
the four climatic zones of Texas, as shown in Exhibit A2. For each zone, separate pavement
condition models pertaining to the Distress Score and the Ride Score were developed.

Exhibit A2. Climatic Regions in the State of Texas.

A-4


Next Year Network Condition

The condition of the network for each subsequent year was based on the condition of the
previous year with the addition of the effect of the natural deterioration, as predicted by the
developed condition prediction models. The models were used in order to predict the
deterioration of each individual section in terms of the Ride Score and their Distress Score. Once
these new values were determined then they were combined together to calculate the new
Condition Score of each section. The new Condition Scores of each sections were then averaged
together weighted by their respective lane-miles to get the new state-wide Condition Score.

Network Goal

The needs analysis was conducted according to the condition goals defined for each of
the following analysis scenarios:

Grade F: Unacceptable Conditions
Grade D: Worst Acceptable Conditions
Grade C: Minimum Competitive Conditions
Grade B: Continue 2010 Conditions

The score in compliance with each of the goals was calculated for each year of the
analysis period by summing together all the lane-miles of the individual sections with a
Condition Score greater than or equal to 70 and dividing them with the overall number of lane-
miles in the state, according to the following equation:

∑ (section lane-miles for sections with CS <70)
% of fair, poor, and very poor =
Σ (section lane-miles)

Candidate Project Selection

The selection of candidate projects was based on the assignment of Maintenance and
Rehabilitation actions to the various individual pavement sections, as well as on their subsequent
prioritization.
Assignment of M&R actions: The assignment of M&R actions to the various individual
pavement sections was performed by considering two criteria: 1) the section’s current Ride Score;
and 2) the drop of the Ride Score between the current year and the previous year. Based on these
defined categories of Ride Score and Ride Score drop, the M&R actions were assigned to form a
decision matrix. Using the decision matrix, the current Ride Score as well as the drop of the Ride
Score between the current and the previous year were simultaneously considered for every

A-5


section in order for a specific treatment to be assigned. Furthermore, a few restrictions were
placed in the number of M&R actions of each type that any individual section could receive
during the planning horizon. This was determined based on the minimum cycle length of each
action/treatment type, which was set according to past experience and current practice at TxDOT.
Each M&R action was assumed to have a specific effect on the section it was applied to in terms
of the section’s Ride Score and Distress Score. The correspondence between the various M&R
actions and their respective effect on the pavement sections are set also based on past experience
and current practice at TxDOT. Finally, the implementation of each action corresponded to a
specific cost for the agency, based on the unit cost of the action by lane-mile treated and the
lane-miles of the treated section(s). The unit costs of each action were set to values that reflect
the total delivery cost of a project.
Prioritization of Sections: Once the various M&R actions had been assigned the sections
planned to receive them were prioritized in order to be selected for implementation based on
three criteria:

 The section’s Ride Score.
 The section’s Distress Score.
 The section’s traffic.

The final outcome of the prioritization algorithm was a ranking number ranging from 0 to
5 with the value of 5 denoting a very high priority for M&R actions and 0 denoting no need for
any action.

Updated Network Condition

After the various projects were selected so that the Texas Transportation Commission
goal was accomplished for the current analysis year, the analysis for the following year would
begin. The individual sections that had received a treatment would get their Condition Scores
updated based on the improvement of the Ride and Distress Scores and the overall Condition
Score of the entire network would be calculated. This would lead again to the prediction of the
deterioration based on the prediction models and the whole process would again be repeated until
all years in the planning horizon have been analyzed.

Estimated Needs for Analysis Year

Based on the number of sections treated during the analysis year in order to reach the
defined scenario goal the overall state-wide needs were determined. There results were reported
for each year of the analysis period.

A-6


II. NEEDS ANALYSIS FOR ADDED CAPACITY MOBILITY LANE-MILES
The added capacity (urban mobility and rural connectivity) lane-miles were provided to
the Pavement Needs Analysis Team based on the TTI Mobility Team’s analysis. The added
capacity lane-miles used by the Pavement Needs Team included only on-system added lane-
miles. Four added capacity lane-mile scenarios were analyzed by the Mobility Team including:

Scenario Added Capacity Lane-Miles (On-System)
Grade F: Unacceptable Conditions 18,500 (12,400)
Grade D: Worst Acceptable Conditions 26,300 (17,600)
Grade C: Minimum Competitive Conditions 35,500 (23,800)
Grade B: Continue 2010 Conditions 46,500 (31,100)

This analysis only considered the Maintenance & Rehabilitation costs for added capacity
lane-miles. The capital cost for constructing the pavement was captured in the Urban Mobility
and Rural Corridor Appendices. The cost of treating the added capacity lane-miles is a small
fraction of the cost to treat the existing 192,150 on-system lane miles. This is because the added
capacity lane-miles are being added over a 25-year period, rather than all at once, and are new
lane-miles that do not require as much heavy treatment as does the older and much larger
existing system.

III. PROJECT DELIVERY TREATMENT COSTS
Treatment costs used in the Pavement Needs analysis were based on total project delivery
costs rather than just the cost to provide the paving materials in place. Total project delivery
costs include additional costs such as contractor mobilization, traffic control, storm water
pollution prevention procedures, and other costs that are related to constructing a pavement
Preventative Maintenance or Rehabilitation project.
These costs were determined through interviews with TxDOT Construction and
Maintenance Division personnel, the Associated General Contractors, a pavement engineer
expert task group that was convened and information provided through the TxDOT online
average bid price system. These costs were then converted to 2010 dollars, using the Highway
Cost Index (HCI) provided by TxDOT.

A-7


APPENDIX B – BRIDGE QUALITY
by
Jose Weissmann, Professor of Civil and Environmental Engineering
Angela J. Weissmann, Research Scientist
The University of Texas at San Antonio

INTRODUCTION
This appendix summarizes the methodology employed to generate the forecasts for bridge
costs for the several bridge-deficiency scenarios included in the main part of this report. These
costs are subdivided in two major categories: 1) costs for the rehabilitation and replacement of
deficient bridges, and 2) costs borne by the users of the system due to deficient bridges such as
additional Vehicle Operating Costs (VOC) due to ride quality on deficient bridge decks and risks
of detour due to load posted bridges. In addition, annual costs for regular maintenance and
inspection of the bridge network are included and are based on estimates from the first 2030
Committee report. Culvert replacement costs are also discussed.
The bridge analysis was driven by a 2030 Committee consensus: the current priorities for
bridge preservation under a restricted funding scenario over the planning horizon must target
Structurally Deficient and Substandard for Load Only bridges. This is a departure from
previously established forecasts that addressed substandard bridges overall, including
Functionally Obsolete bridges.
Structurally Deficient bridges present significant deterioration of one or several bridge
elements, such as the deck or supporting beams, as measured during routine inspections.
Functionally Obsolete bridges are unable to accommodate existing traffic due to geometric
characteristics that may include roadway alignment, clearances, and traffic capacity.
A bridge is considered Substandard for Load Only if it is classified neither as Structurally
Deficient nor as Functionally Obsolete, but its original as-built capacity was not designed to carry
current legal loads. A Substandard for Load Only structure is load-posted or recommended for
load posting.
The calculations reported in the body of this report summarize the impacts of different
funding scenarios, using as performance variable the percentage of surface bridge deck area
classified as deficient. For the purpose of this report, deficient surface bridge deck area
encompasses both Structurally Deficient and Substandard for Load Only bridges. It does not
include Functionally Obsolete bridges.

DATA AVAILABILITY AND STATISTICS
Bridge inspection data at TxDOT are stored separately for the On- and Off-systems. The
On-system encompasses the bridges managed by the state of Texas. Examples of roadways
comprising the On-system are Interstates, US Highways, FM and RM roads. Off-system bridges
are managed by cities, counties, and other entities. Examples of roadways comprising the Off-
system are county roads and city streets.

B-1


The main data source for the analysis included in this report was the bridge inspection
information available from TxDOT’s Bridge Inventory, Inspection and Appraisal Program
(BRINSAP). BRINSAP is a dynamic database with new inspection results being included in a
daily basis. For this analysis, end of fiscal year (August 31) snapshots of the data were retrieved
for years 1995 through 2010. On-system record count for the 2010 BRINSAP data is 34,208.
From these records, 20,390 belong to bridges, with the remaining records belonging to culverts.
Off-system counts for 2010 BRINSAP data are 18,225 with 12,384 belonging to bridges and with
the remaining records belonging to culverts. In this study, bridges and culverts were analyzed
separately due to their different performance characteristics.
Exhibits B1 and B2 present the distribution of bridge deck area by age as of 2010 for the
On- and Off-systems, respectively. According to the 2010 BRINSAP data, the total deck area for
On- and Off-system bridges was 364 million and 65 million square feet, respectively.
Considering an average bridge design life of 50 years, Exhibits B1 and B2 indicate significant
funding requirements from now to the year 2035 for the upkeep of Texas bridges; a significant
amount of deck area will be reaching the end of their design lives for both systems.

16

14

12
Deck Area (million sqft)

10

8

6

4

2

0
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89

Bridge Age (years)

Exhibit B1. Deck Area Age Distribution for the On-System Bridges.

B-2


4

3.5

3
Deck Area (million sqft)

2.5

2

1.5

1

0.5

0
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89

Bridge Age (years)

Exhibit B2. Deck Area Age Distribution for the Off-System Bridges.

NEEDS ANALYSIS APPROACH FOR DEFICIENT BRIDGES
The analysis approach was implemented separately for the On- and Off-systems. To
implement necessary calculations, several supporting SAS programs were developed and
extensive analysis of the historical BRINSAP data for both On- and Off-Systems from 1995 to
2010 was performed. The analysis approach encompassed the six steps listed below. Steps three,
four, and six are explained in more detail subsequently.

Steps:

1. Read the 2010 BRINSAP data. This is the base-year data.
2. Extract the records for bridges (excluding culverts).

The next steps were repeated for each year in the planning horizon (2010 to 2035):

3. Add the amount of deck area that becomes deficient for that year.
4. Apply annual budget. Sort the deficient bridges by age and traffic and program bridge
rehabilitation or replacement for the deficient bridges until annual scenario budget is
exhausted. Substandard for Load Only bridges are programmed first.

B-3


5. Calculate percentage of deficient deck area for that year after budget is exhausted and
record the number.
6. Calculate potential Vehicle Operating Costs (VOCs) for that year due to bridge
deficiencies (ride quality and detours), separating the results for passenger cars and
trucks.

Step 3 Methodology– Deck Area that Becomes Deficient on a Yearly Basis

Several statistical analyses were performed to estimate the area of bridge deck that
becomes deficient on a yearly basis. These calculations were based on the historical BRINSAP
data spanning 1995 through 2010. Exhibit B3 presents the distribution of deck area that became
Structurally Deficient for the On-system, based on the historical BRINSAP database. On average,
on a yearly basis, 1.1 million square feet of bridge deck surface area deteriorate to a Structurally
Deficient condition. Similarly, about 0.25 million square feet on an annual basis become
Substandard for Load Only. On average, the total bridge deficient deck area to be added on a
yearly basis is 1.35 million square feet. A similar analysis of the Off-system historical data leads
to 0.5 million square feet of deficient bridge deck area being added on a yearly basis to the needs.

3,000,000

2,500,000

2,000,000
Deck Area (sqft)

1,500,000

1,000,000

500,000

-
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Exhibit B3. Distribution of Deck Area that Became Structurally Deficient for the On-
System.

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Step 4 Methodology – Apply Annual Budget

The 2030 Committee established goals for bridge condition for the different scenarios,
which are detailed in the main body of this report. These goals are defined by percentages of
deficient deck area that are acceptable for each scenario. These percentages are associated with
separate annual budget levels for the On- and Off-systems and are reported in Step 5 of the
analysis.
The procedures in Step 4 include an average expansion factor for the deficient bridges
that are prioritized for intervention of 50 percent. This expansion factor recognizes what is
common practice for bridge managers when programming work for deficient bridges and is
driven by coordination with other factors such as required traffic capacity.
Deficient bridges that are unable to undergo improvements due to annual budget
restrictions become part of the backlog of deficient bridges that is processed in the next year of
the planning horizon. Unit costs of bridge interventions were discussed in the 2030 Committee
report published in 2009 and were reevaluated to be consistent with 2010 unit costs.

Step 6 Methodology –Calculate Potential Vehicle Operating Costs due to Bridge
Deficiencies

Impacts on users of trucks and passenger cars are calculated separately and are based on
costs per mile discussed in the Vehicle Operating Costs Appendix (Appendix G) of this report.
Two types of costs are estimated: (1) increased VOC due to vehicles operating on rough,
Structurally Deficient decks, which are calculated using traffic data recorded in BRINSAP and
bridge length, and (2) potential detours caused by Substandard for Load Only bridges included in
the backlog for each year of the planning horizon. Costs due to ride quality and detours are
calculated for all the deficient bridges in the backlog for a given year in the planning horizon.

B-5


ANNUAL COSTS FOR REGULAR BRIDGE MAINTENANCE,
INSPECTIONS, AND CULVERTS
These annual costs are considered as fixed throughout the planning horizon and were
discussed in detail in the 2009 2030 Committee Report (with the exception of culvert costs). The
2009 report values were updated to reflect 2010 dollars. These calculations estimate that costs for
inspection of the On- and Off-systems amount to $44 million on an annual basis. Regular bridge
maintenance requires $53 million on an annual basis for On-system bridges.
Analysis of the historical BRINSAP database from 1995 to 2010 shows that, on the
average, 19 culverts transition to a deficient status on a yearly basis. Exhibit B4 shows this
distribution for the On-system. The average for the Off-system is also 19 culverts per year.
Further analysis involving average culvert replacement costs results in an annual cost to replace
On- and Off-system deficient culverts of $15 million. Annual costs for bridge maintenance,
inspections, and culverts are included in each one of the scenario costs reported in the 2011 2030
Committee Report.
60

50
Number of Culverts

40

30

20

10

0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Exhibit B4. Yearly Number of Culverts Transitioning to a Deficient State.

B-6


APPENDIX C – URBAN TRAFFIC CONGESTION

by
Tim Lomax, Regents Fellow and Research Engineer
David Schrank, Associate Research Scientist
Texas Transportation Institute
The Texas A&M University System

INTRODUCTION

For more than three decades, our state’s largest cities have experienced increasing
congestion. Texas Transportation Institute’s (TTI’s) 2010 Urban Mobility Report (1) found that
the cost of annual travel delay and extra fuel consumed in stop-and-go traffic by Texans was
$9.8 billion. Congestion is worse in large cities, but it is getting worse in medium and small
cities as well. The cost, difficulty, frustration, and inability to plan a trip affects everyone
whether they are traveling to work, school, doctor’s appointments, or leisure activities. With the
Texas population expected to grow from 25 million in 2010 to more than 40 million by 2035,
congestion will affect even more trips, cities, regions, and times of day.
Mobility challenges affect everyone—people who live and work in big cities, small
towns, and rural areas between them. Our state’s favorable
Q. What cities make up
business, economic, and social climate will bring significant “urban” Texas?
growth in Texas. The question is how will Texans address the Abilene
transportation challenges presented by this growth? Will we Amarillo
develop a set of policies, programs, projects, plans, and Austin
partnerships in a conscious, planned, cooperative decision-making Beaumont-Port Arthur-
Orange
process? Or will we pay for our lack of attention to the growth Brownsville
issues with more time and wasted fuel but less time with our College Station-Bryan
families, at our jobs, with social and civic groups, and at parks and Corpus Christi
schools? Will the challenges overwhelm our ability to craft a Dallas-Fort Worth
meaningful plan to deal with travel mobility? What actions will be El Paso
Harlingen-San Benito
taken by transportation agencies, private businesses, the public, Hidalgo County
and decision-makers? This chapter describes the mobility choices Houston-Galveston
facing Texans and offers a basis to craft solutions that will meet Killeen-Temple
the travel challenges we face. Laredo
Longview
ORGANIZATION OF THIS APPENDIX Lubbock
Midland-Odessa
San Angelo
This appendix explains the state’s current travel mobility in San Antonio
urban areas and looks at possible ways that policy-makers, Sherman-Denison
decision-makers, and the public can view the future of mobility to Texarkana
prevent or respond to the challenges Texas faces. The 2030 Tyler
Victoria
Committee established several potential scenarios for handling
Waco
growing mobility issues and identifying ways to specify desired Wichita Falls
mobility outcomes. This chapter explains those scenarios and
presents possible outcomes and recommendations. Topics include:

C-1


 Current and future mobility conditions in Texas.
 The costs of investments and benefits from investments in mobility.
 Cost saving effect of mobility improvement options.

CURRENT CONDITIONS
The congestion levels for the Texas cities included in the Urban Mobility Report are
compared to regions of similar size from around the US in Exhibit C1. The extra travel time
spent by Texas auto commuters is displayed with the averages for other urban areas in the United
States within four population groups. Dallas-Fort Worth, San Antonio, El Paso, and Beaumont
have congestion levels near the midpoint of all US regions their size, with the average declining
as population decreases. Houston and Austin, however, have congestion levels ranked in the top
five in their size group. The average urban Texas auto commuter spends an extra 43 hours in
traffic each year with a value of wasted time and fuel of $970 per year, 60 percent more than a
decade ago.
Mobility challenges are manifest in two ways: 1) increasing congestion and 2)
inadequacy of travel options. Both of these problems result in additional hours spent traveling,
more fuel purchased, interference with work, loss of leisure time with family and friends, and
increased cost of goods. Mobility is reduced when travel demand is greater than the available
capacity of the transportation system or when crashes, vehicle breakdowns, weather, or other
events conspire to increase congestion.

Exhibit C1. 2009 Urban Congestion Levels, Texas and US.
Hours of Delay
Urban Area and Population Range
Per Auto Commuter1
Houston   58
US Very Large Area Average (over 3 million) 50
DFW‐Arlington   48
Austin   39
US Large Area Average (1 to 3 million) 31
San Antonio   30
US Medium Area Average (500,000 to 1 million) 22
El Paso   21
McAllen   7
Beaumont   21
US Small Area Average (less than 500,000) 18
Brownsville   14
Laredo 12
Corpus Christi   10

1
Delay per Auto Commuter: Expresses the extra travel time during the year divided by the number of
people who commute in private vehicles in the urban areas. This measure estimates the amount of time,
on average, that each traveler would spend in congested traffic each year.
Source: (1)

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THE MOBILITY SCENARIOS
The 2030 Committee developed a range of scenarios to achieve goals that reflect both the
aspirations of Texans and prudent long-term investment strategies. Those scenarios represent
trade-offs between investment levels, economic benefits, and personal user costs. They provide a
range of mobility levels using a variety of cost estimates. The goals that most improve mobility
will put Texas in a more competitive position compared to peer regions and cities around the
nation.
The development of regional mobility estimates were facilitated by the ongoing planning
activity of the state’s 25 metropolitan planning organizations (MPOs); all of the Committee’s
recommendations draw heavily on the local knowledge captured in those MPO plans. Mobility
needs have been the subject of substantial analysis by TxDOT (2), the MPOs (3), the 2030
Committee (4) and the Governor’s Business Council (5).
The computerized planning models combine population, job, and land development
forecasts with estimates of the transportation network to describe travel and congestion for future
years. The area covered is typically larger than the urban area used in the Urban Mobility
Report; there are differences in the data and estimates between the two sources, but the
information and conclusions are similar. Using the regional models ensured that the different
characteristics of each region were included in the results while using a common analytical
approach to congestion forecasts. Each model generated trips for work, school, shopping,
medical, and other purposes and applied them to roadway sections; these traffic volumes were
combined with the capacity of each road to estimate traffic speed and then congestion levels.

SCENARIO DESCRIPTIONS

Four mobility scenarios were examined by the 2030 Committee, with the conditions that
will result from current funding trends providing the baseline for comparison to three
improvement options. The comprehensive studies of urban mobility funding and long-range
projects and programs in Texas prepared by each of the Texas metropolitan planning
organizations were used as the analytical basis for the scenarios.
The Committee used letter grades ranging from F to B to describe the scenarios. The
strategies range from doing nothing new to implementing enough programs and projects to
maintain conditions as they are now. The Committee did not assign a letter grade of A to any
scenario due to the significant funding required to achieve this level of quality for the
transportation system. The scenarios incorporated goals for pavement quality, bridge quality,
urban mobility, and rural connectivity; the full 2030 Committee report describes the
development of each scenario. The urban mobility scenarios described below use the regional
transportation model data and forecasts as the base information; additional computations were
performed by the Texas Transportation Institute.

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 GRADE F: Unacceptable Conditions – The current policies, planning processes, and
funding schemes would continue under this scenario. The expected growth in jobs and
people will not be addressed by new transportation projects:
o Urban congestion is projected to rise from 37 extra hours of travel today to 44
hours in 2015 and 50 hours in 2019. This represents the equivalent of 4½ days of
vacation today and more than 6 days of vacation by 2019.
o The projections are worse from 2020 to 2035. Congestion will grow to an average
of 130 hours of extra travel time in 2035; transportation investments will not keep
pace with the growth in jobs and people over this period.
o Many of the benefits from one-time funding sources will slow congestion growth
through 2019.
o More travel time means less productive time at work, less time with family and
friends, and larger delivery and service fleets to handle the same number of
customers.

 GRADE D: Worst Acceptable Conditions – Investments would be made to
maintenance programs to reduce the amount of roads and bridges that will require
expensive rebuilding.
o Urban congestion will grow at a rapid rate. Congestion will be better than the
current Unacceptable Conditions scenario, but will more than double to an
average of 84 hours of extra travel time per urban commuter by 2035.

 GRADE C: Minimum Competitive Conditions – Texas’ infrastructure and congestion
levels would remain in a condition equal to or better than its peer states or metropolitan
regions.
o Urban regions would have congestion levels better than at least half of the US
regions with similar populations.
o The average urban area delay will be 57 hours in 2035.

 GRADE B: Continue 2010 Conditions – Under this scenario, the transportation system
conditions experienced in 2010 would be maintained throughout the period from 2011 to
2035.
o The urban road networks would have the same congestion levels as in 2010.

Q: How are scenario costs defined?

A: Cost estimates are defined by the amount of investment required between 2011 and 2035 for each
scenario. This estimate includes many projects for which funding has already been identified.

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HOW WILL SOLUTIONS BE IMPLEMENTED OVER THE NEXT 25
YEARS?
Whatever scenario is pursued, the long-range transportation plans are evolutionary
processes—changes are made to elements every few years
when the plans are updated. The analysis in the 2030 Q: What is the “funding gap”?
Committee Report should be a part of the process of
A: The term “funding gap” defines
identifying the need for improvements and the general
the difference between the funded
costs and benefits from any large-scale transportation projects and needed investment.
investment program. Community leaders and the public
will be responsible for developing specific plans, projects,
and programs; the important element at this time is to define the size of the problem and the
goals, and mobilize the resources needed to address the long-term solutions. The 2030
Committee Report can be used by decision-makers and the public to assess progress toward
long-range goals.

WHAT WILL THE IMPROVEMENTS COST?
The leaders of the state’s 25 metropolitan planning organizations (MPOs) adopted an
approach to consistently estimate the cost of mobility solutions in their Texas Mobility Plans (3).
These organizations consider all transportation modes when developing solutions—a multi-
modal approach. Not every region will adopt the same mix of strategies, so the cost estimating
approach had to use available data and consistent analytical techniques as well as reflect an
average cost of all solutions.
Like the analysis conducted by the MPOs, the cost estimating approach for the 2030
Committee analysis began by identifying problems in the transportation network. Additional
spending to address congestion would be targeted at those locations. Recognizing that each
region would develop a different mix of strategies targeted at corridors and sections, the rich
historical database of roadway costs and the long-range transportation planning model were
used. Project or program cost estimates from each MPO were used whenever possible (and
updated to 2010). Where more capacity was needed, the scenario cost was estimated as the
funding required to add roadway lane-miles. The specific projects and programs to be deployed
will be drawn from a broad array of modes that are used to
improve urban mobility—such as walking, cycling, bus Q: How were the problem
rapid transit, light rail and commuter rail transit, high- locations determined?
technology improvements to highway operations, and even A: The planning organizations
using telecommuting to accomplish a trip without physical from Texas’ larger regions
travel. (above 50,000) developed an
The 2030 Committee encourages the reader to approach using long-range
recognize the importance of viewing the urban mobility planning models. If a road link
was projected to have more
investment recommendation as a broad expression of the traffic volume than the scenario
dollars needed, not simply an estimate of future highway goal (for example, “reduce
infrastructure. Future mobility solutions will require a congestion”), enough road lanes
broad mix of transportation strategies, so the investment were added to reduce
congestion to acceptable levels.

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needed for each mobility scenario is expressed in both “lane-miles” and “person-miles of
capacity.” The person-mile expression reflects the Committee’s strong intent to focus on
investing in moving people, rather than concentrating on any one travel mode. A mix of modes,
programs, projects, policies, and partnerships, such as those described by the North Central
Texas Council of Governments, will make sense in Texas communities, especially as the cost of
traditional highway construction increases with rising urban land values and changing urban land
use patterns. Cost estimates also include allocations for freeway-to-freeway interchanges and
right-of-way.

POTENTIAL REDUCTIONS IN BOTH TOTAL IMPLEMENTATION COSTS AND THE
STATE’S SHARE OF THOSE COSTS

The cost estimates used in this report are a representation of the total cost of addressing
mobility needs through a variety of projects, programs, policies, and plans that will be developed
and implemented by multiple agencies or partners over the next 20 years. The 2030 Committee
did not presume to identify the appropriate mix of strategies or methods that regions will choose
to solve their mobility challenges, but the cost estimates used in the report assume a more
aggressive deployment of non-road widening solutions than the current situation. This section
describes the process used to estimate the scenario costs in the 2030 Committee report.
The 2030 Committee recognizes the importance of using every improvement technique to
enhance the transportation system and infrastructure conditions. The needs are large, but they
can be reduced by doing things smarter, more efficiently, with advanced technology and with
greater participation by employers, commuters, and businesses. There will be a different mix of
strategies in every region based on the size, scale, and scope of the problems and the interests of
the public in matching their goals for the region to the investments and strategies they support. In
all cases, the solutions must work together to provide an interconnected set of transportation
infrastructure and services.

The Transportation Action Program

Three general methods can be used to reduce the state share of future transportation
funding requirements. All of these strategies will play an important role in Texas’ future, but the
size of the problem in the largest regions is more significant than these actions will be able to
address alone.

 Commute options – Businesses are finding that they can save office costs and improve
productivity by offering employees a variety of ways to accomplish their jobs without
traveling to work in the rush hours. Electronic communications can be used in place of
physical travel to an office. Support can be provided to workers who wish to carpool or
use public transportation. Flexible work hours can be offered to encourage workers to
commute to work during off-peak hours. More aggressive actions might include
monetary incentives to encourage travel outside the peak hours or to use electronic
communication methods. These have been successful in improving employee
productivity and satisfaction, as well as allowing flexibility to meet the needs of both

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family and job. The 2030 Committee analysis assumed these programs would cost
10 percent of the program benefits.
 Operating improvements – Several methods have been deployed on streets and freeways
to get as much service as possible from the existing roads. Many of these are relatively
low-cost projects and programs; they have broad public support and can be rapidly
implemented. These ideas require innovation, 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, improving road
and intersection designs, or adding a short section of roadway are relatively simple
actions with big payoffs. The 2030 Committee analysis assumed these programs would
cost 15 percent of the operational project benefits.
 Revenue from local sources, toll road projects, and transit projects – The traditional mix
of funding could be altered to rely less on state and federal funding sources and more on
a variety of other agencies, projects, and programs. The effect of revenue enhancement
scenarios can be estimated but the specific elements of any scenario were not identified.
The 2030 Committee analysis assumed these programs would have no cost to obtain the
benefits.

Action Program Scenarios

Three levels of improvement were studied as part of the 2011 2030 Committee report and
two time horizons were evaluated, 2020 and 2035, to examine the near- and long-term needs.
The possible outcomes and resulting decreases in funding required to achieve the goals were
identified in the scenario cost analysis. Other combinations are possible, but the scenarios listed
below are a reasonable demonstration of a system of balanced improvements.

 Enhanced – Strategies and levels of effort that are beyond those currently deployed, but
appear to have broad public support and are within current regulatory frameworks were
used to construct this scenario. A 10 percent increase in local, public transportation, or
tolling projects was also assumed.
 Aggressive – In addition to the Enhanced level, actions that have been tested in North
America but are not deployed in Texas would be used to expand commute options and
increase system efficiencies. Local regions would have flexibility in choosing the actions
that best meet their needs. In some cases, these would require changes in regulations,
methods of enforcement, and policies. A 15 percent increase in local, public
transportation, or tolling projects was also assumed.
 Very Aggressive – Most of the possible commute options and system efficiency increases
would have to be widely deployed and operated to achieve the very aggressive scenario.
Some of these will require legislative action to change enforcement regulations and
Texans would have many incentives to make different travel choices, and may be
rewarded for choosing home and job locations that can be reached by travel modes other

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than private vehicles. A 20 percent increase in local, public transportation, or tolling
projects was also assumed.

Results

The net revenue enhancement from the Action Program Strategies shown in Exhibit C2 is
based on a level of needs keyed to the Continue 2010 Congestion scenario; it was assumed that
the actions would be independent of the chosen 2030 report scenario. The net revenue displayed
in Exhibit C2 ranges between $4 and $10 billion from 2011 to 2020 and between $10 and
$29 billion from 2011 to 2035. These values represent substantial contributions to closing the
funding gap. If the least aggressive set of enhancement options are chosen, the Worst Acceptable
Scenario in 2020 appears to be within reach. Better goals have remaining state funding levels
that appear to require additional actions. The scenario analysis suggests additional funding or
actions will be needed to achieve any of the 2035 scenarios, even if the most aggressive set of
options are pursued.
Exhibit C2 identifies the importance of addressing congestion levels with every possible
strategy. The projections also suggest that more funding will be one of those strategies.
Additional information is included in Exhibits C7 to C11 at the conclusion of Appendix C.

Exhibit C2. Possible Contributions to Funding Needs from Commuting Options, Operating
Strategies, and Funding Sources.
2011 to 2020 2011 to 2035
Amounts in   B – Continue B – Continue
2010 $Million D – Worst C – Minimum 2010 D – Worst C – Minimum 2010
Share Acceptable Competitive Conditions Acceptable Competitive Conditions

State Funding Forecast $     8,822 $     8,822 $     8,822 $   13,137 $   13,137 $   13,137
Other Revenue Sources $   26,444 $   26,444 $   26,444 $   54,754 $  54,754 $   54,754
Current Funding Trend $   35,266 $   35,266 $   35,266 $   67,891 $  67,891 $   67,891

Total Funding Needed $   39,362 $   58,010 $   68,703 $ 105,990 $ 145,158 $  182,509
The Funding Gap $     4,095 $   22,744 $   33,437 $   38,099 $   77,267 $  114,618

Summary of “Buying Down” the

State Share
Total Net Revenue Enhancement
Enhanced $     3,948   $     3,948   $     3,948   $     9,945   $      9,945   $      9,945
Aggressive $     7,160   $     7,160   $     7,160   $   19,159   $    19,159   $    19,159
Very Aggressive $   10,371   $   10,371   $   10,371   $   28,373   $    28,373   $    28,373

Remaining State Share
Enhanced $       147   $    18,795   $   29,488   $   28,154   $    67,321   $ 104,673
Aggressive $  (3,064) $    15,584   $   26,277   $   18,940   $    58,108   $   95,459
Very Aggressive $  (6,276) $    12,373   $   23,065   $     9,727   $    48,894   $    86,245

Exhibit C3 presents the size of the existing and possible future Texas urban networks
along with investment required for each mobility scenario. The investment levels described in
Exhibit C3 represent the additional amount necessary to meet the scenarios by 2035 in 2010
dollars. Costs for achieving the scenarios range from $68 billion (the best estimate of the amount

C-8


that will be spent if policies and funding scenarios do not
Q: What is a lane-mile?
change) to $183 billion. The large amount of additional
roadway might be surprising, but many road sections have A: A measure of roadway
heavy traffic volumes now, and the growth in population, space. A 10-mile-long, 4-lane
employment, and trade will place great strain on the network. road has 40 lane-miles.
The measure of equivalent lane-miles used throughout this
Appendix is simply a consistent way of estimating the cost of the full range of strategies that will
be deployed to improve mobility over the next 25 years, regardless of transportation mode. The
added lane-miles are also included in the pavement maintenance cost requirements to ensure
funding will be available if the road miles are built. The cost of urban projects reflects the higher
cost of construction in large, congested metropolitan regions.

Exhibit C3. Investment Required for Each Mobility Scenario.
Estimated Equivalent Lane‐ Investment Required
Mobility Scenario
Miles Needed (Billions of 2010 $)
Urban Network Size
Completed by 2010   82,100   NA
Urban Scenarios
F – Unacceptable Conditions 18,400   $68
D – Worst Acceptable 26,000   $96
C – Minimum Competitive 36,500 $135
B – Continue 2010 Congestion 46,600 $173
Note: Costs are the median value of a range of cost estimates.
2010 dollars used in the calculations.

USER COSTS RESULTING FROM MOBILITY CONDITIONS
Two types of user costs were estimated based on the improved transportation service in
the scenarios. Identifying the appropriate target scenario involves considering both elements—
the taxes and fees paid to construct the improvement projects, programs, policies, and plans; and
the congestion effects that result from the scenario. The scenarios studied provide a range of
congestion reduction in exchange for additional investment in transportation facilities and
services.
The 2030 Committee estimated the cost of congestion for the urban mobility investment
and used the value of travel delay and additional fuel consumption by persons and commercial
vehicles as a conservative estimate of the user costs. The cost of providing the system is
generically referred to as “taxes and fees” recognizing that no matter how the projects are
deployed, there will be some cost to implementing the strategy.
Other effects were not included in the 2011 Committee report, although they are also
important considerations. Effects on Texas businesses will be apparent with higher congestion
levels, and companies will not be able to serve the same number of customers with the same
equipment and personnel as companies in regions with less congestion. Local government tax

C-9


revenue from the transportation expenditures and the jobs and payroll from construction
programs are also not included in the effects on communities.

CONGESTION COSTS TO TEXANS

Congestion costs were estimated for personal vehicles and commercial trucks based on
the results from the computerized transportation planning models. The extra travel time above
that which could be achieved at free-flow conditions was the baseline for the calculation of
congestion. Commercial vehicle costs were calculated for each region using the percentage of
total travel by trucks.

 Time Costs – The speed of travel in the peak period is determined for arterial streets and
freeways. The value of delay for personal vehicles and for commercial vehicles is
estimated using a unit value of $16 per hour for person travel and $105 per hour for truck
travel. A value of 1.25 persons per vehicle was used for personal vehicles.
 Fuel Costs – The speed of travel and amount of stop-and-go traffic results in an estimate
of the fuel consumed in congested travel; this value is compared to fuel consumed in
free-flow travel. The less efficient fuel burn means higher costs for both personal and
commercial vehicle travel. Fuel costs are included in the truck operating costs. The 20-
year historic average for fuel costs as a proportion of travel delay costs is 8.4 percent; this
value was used in the analysis.

CALCULATING HOUSEHOLD TRAVEL COSTS

A key element of the 2030 Committee report is the calculation of the effects of mobility
problems on the average Texas household. To accomplish this, the congestion costs developed
for each region were separated into personal and commercial vehicle travel. While the
commercial vehicle costs are ultimately paid for by individuals in the costs that they pay for
goods and services, the conservative approach used in this analysis only used personal vehicle
travel to illustrate the household cost effects.
The commercial vehicle congestion costs are 30 percent of the state total congestion costs
in urban regions Exhibit C4. This varies from below 30 percent for most of the larger urban
regions to above 60 percent in smaller regions. Trucks comprise approximately 6.1 percent of
urban travel statewide. The value of commercial delay was subtracted from the total congestion
costs when presenting household costs.

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Exhibit C4. Truck Cost Component of Urban Congestion Cost.

Truck Cost as a Percent of
Urban Area Total Urban Congestion Costs
Abilene 60%
Amarillo 57%
Austin 32%
Beaumont 39%
Brownsville 30%
Bryan‐College Station 46%
Corpus Christi 48%
Dallas‐Fort Worth 26%
El Paso 22%
Harlingen 29%
Hidalgo 30%
Houston 27%
Killen‐Temple 37%
Laredo 52%
Longview 44%
Lubbock 32%
Midland‐Odessa 49%
San Angelo 47%
San Antonio 27%
Sherman‐Denison 51%
Texarkana 64%
Tyler 31%
Victoria 61%
Waco 41%
Wichita Falls 41%
Average 30%

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Mobility Results from Investment Scenarios
Texans will realize many benefits from any mobility improvements pursued. Current
trends, however, result in high congestion levels. Average trip times, as estimated by long-range
planning models, will increase substantially from today’s conditions in the absence of additional
funding sources and new policies. The cost of
congestion will rise from $820 per urban Texas Q: How are the needs identified in the 2030
household commuter today to $2,800 per average Report different from a “wish list”?
household in 2035 (expressed in 2010 dollars) A: Through computer models, traffic volume
(Exhibit C5). indicators identify the pieces of the
Mobility improvements described in the transportation network that will be more
scenarios produce significant time, fuel, and congested than the scenario goal. Scenario
financial savings. Exhibit C5 summarizes the key costs are related to the amount of lanes
needed to treat only the problem locations.
mobility outcomes of each scenario. In addition to
the scenario costs from 2011 to 2035 (see Exhibit
C3), three measures of congestion are also displayed. Congestion cost is the combination of
wasted fuel and time for trucks and personal vehicle travel for 2035. The annual hours of delay
per commuter is an estimate of the time spent in congestion by the average person who travels in
the peak period; larger regions typically have more delay per commuter (see Exhibit C9 for
regional delay per commuter values).

Exhibit C5. Summary of Urban Mobility Scenario Outcomes.
Current Congestion Congestion Cost per Household Annual Delay per Commuter*
Level $820 37 hours
2035 Mobility Scenarios
F – D –   C –   B –
2035 Mobility
Unacceptable Worst Minimum Continue 2010
Outcomes
Conditions Acceptable Competitive Congestion
2011 to 2035
  $68 $96 $135 $173
Scenario Cost ($ Billion)
2035 Congestion Cost
$61 $39 $26 $18
($ Billion)
2035 Delay per
  130 84   57   39
Commuter (hours)
2035 Congestion Cost
$2,710 $1,730 $1,170 $810
per Household
*Hours of extra travel time per urban area traveler during the peak period
Note: See Exhibits C7 to C11 for regional values and more information on congestion in 2015, 2020, 2025, and 2035.

Unacceptable Conditions – By definition, the baseline mobility scenario has no
associated congestion benefits. However, congestion would be much worse if no improvements
were made between 2011 and 2035. The Unacceptable Conditions scenario includes investments
between now and 2035 that will provide a congestion reduction effect. But the mobility picture
is not good. Many of the Texas regions will have congestion levels above the median value of
their population group in the country. The average urban commuter will spend the equivalent of

C-12


more than three extra work weeks of time in congestion (130 hours) and pay a “household tax”
of $2,710 in time and fuel each year. In 2035 alone, congestion costs will exceed $63 billion.
Worst Acceptable Conditions – This 2030 Committee scenario focuses most of its
investment on maintaining reasonable pavement and bridge quality. As a result, congestion will
increase dramatically, although less rapidly than under the Unacceptable Conditions scenario.
Congestion will cause the average Texas commuter to spend an extra 84 hours per year and cost
the average household an additional $1,730 in 2035. Larger regions will have even greater time
penalties
Minimum Competitive Conditions – Congestion levels will improve from the Worst
Acceptable Conditions if each region achieves a mobility level equal to or better than urban areas
of similar size. All of the metropolitan regions would be expected to have congestion levels at
least on par with peer US regions. Extra travel time will only consume the equivalent of 7 work
days (57 hours) and cost almost $1,200 per household each year.
Continue 2010 Congestion – Using current congestion levels as a target for 2035
mobility, while not desirable, would put Texas cities in a favorable competitive position with
regions of similar size. Even the relatively congested Texas regions would be better than US
regions of similar size. The average commuter delay will be about 39 hours in 2035. The
congestion cost would be $810 per household in 2035. The average statewide delay per
commuter increases slightly from the 37 hours in 2010 due to larger, more congested regions
comprising a higher percentage of urban travel in 2035 than in 2010.

Q: What’s the connection between mobility and the economy?

A: A qualified workforce, reasonable tax and regulatory environment, and access to markets are key
elements in business location and expansion decisions. Access to markets is provided by a reliable and
well-maintained transportation network. Without an adequate network, Texas businesses are at a
competitive disadvantage—costing Texas jobs and economic opportunity.

Comparing the Total Costs for the Mobility Scenarios

All of the investments provide returns that are far greater than the additional costs. The
Unacceptable Conditions Scenario, the most likely estimate of what will occur is much better
than if no expansions were accomplished, but the $68 billion cost will result in more than
$1.3 trillion in congestion costs (Exhibit C6). The total of the two cost elements that the public
will pay is more than $1.4 trillion in 2010 dollars. The other three scenarios substantially reduce
total costs for each successively larger scenario cost. The improvement gained by additional
investment (as shown in the congestion costs savings) is between 7.5 and 13.5 times the
additional scenario cost. Said another way, for each additional dollar invested in the next
scenario, there are between $7 and $14 returned to taxpayers and businesses. This suggests an
economic case could be made to adopt any of the scenarios other than the Current Trend scenario
because at each level of investment, there are substantially more benefits than the program costs
required to fund that scenario.

C-13


Exhibit C6. Investment and Return for Urban Mobility Scenarios.
F – D –   C –   B –
Scenario and
Unacceptable Worst Minimum Continue 2010
Congestion Costs
Conditions Acceptable Competitive Congestion
2011 to 2035
Scenario Cost   $68   $96 $135 $173
($ Billion)
2011 to 2035
Congestion Cost $1,338 $961 $704 $555
($ Billion)
2011 to 2035
Congestion Cost Savings N A $377 $634 $783
($ Billion)
2011 to 2035
Total of Congestion & $1,406 $1,057 $839 $728
Scenario Cost ($ Billion)
Note: Values shown are the median of a range.

REFERENCES

1. 2010 Urban Mobility Report. Prepared by Texas Transportation Institute for University
Transportation Center for Mobility, College Station Texas. 2010.
http://mobility.tamu.edu/ums/.
2. Texas Statewide Long-Range Transportation Plan 2035. Texas Department of
Transportation, 2010.
http://www.txdot.gov/public_involvement/transportation_plan/report.htm.
3. Texas Metropolitan Mobility Plan: Breaking the Gridlock. Presented to the Texas
Transportation Commission, 2004.
4. Texas Transportation Needs Report. Texas 2030 Committee. 2009.
http://texas2030committee.tamu.edu/.
5. Shaping the Competitive Advantage of Texas Metropolitan Regions: The Role of
Transportation, Housing and Aesthetics. Governor’s Business Council, 2006.
http://texasgbc.org/Trans%20Report%20Docs/Shaping%20the%20Competitive%20Advantage.pdf

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Additional Appendix C Exhibits
Urban Mobility Summary Statistics
The additional Appendix tables provide a summary of the Urban Mobility scenario
findings for each urban region.

Type of Measure Exhibit No. Mobility Measure
Regional congestion Exhibit C7 Total Daily Delay (Person-Hours)
Regional congestion Exhibit C8 Annual Congestion Cost (2010$ Millions)
Individual person Exhibit C9 Delay Per Commuter (Hours)
Regional system needs Exhibit C10 Implementation Cost For Mobility Scenarios -
2011 Through 2035 (2010$ Millions)
Regional congestion Exhibit C11 Congestion Cost - 2011 Through 2035
(2010$ Million)

Total Daily Delay – The daily delay is expressed in person-hours. Delay is the difference
in travel time between peak period conditions and free-flow (or light volume) periods.
Annual Congestion Cost – Congestion cost is comprised of the value for travel delay
and extra fuel consumed. Unit values are $16 per person hour and $105 per truck hour. Fuel is
estimated as 8.4 percent of the delay value (average of last 20 years).
Delay per Commuter – This statistic is the amount of extra travel time for a year for the
average peak period traveler. Delay per peak period traveler (termed commuter) works well at a
regional or statewide level. Between 50 percent and 60 percent of a region’s population travels in
the peak; commuter in this case does not just refer to those traveling for a work purpose.
Implementation Cost – Cost for equivalent lane-miles, interchanges, and rights-of-way
estimated to be required to achieve each mobility scenario without the benefits of operational
improvements, commute options and other funding sources (expressed in 2010 dollars).
Congestion Cost – Value of delay and fuel costs for personal and commercial vehicles in
the 25-year period from 2011 to 2035.

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Exhibit C7. Total Daily Delay (Person-Hours).
Unacceptable Congestion Scenario

Metro Areas   2010 2015 2020 2025 2035

Austin
115,133           119,742           125,102           175,095           428,138
Corpus Christi              16,703             22,298             27,530             40,006             67,162
Dallas‐Ft. Worth
741,093           984,621       1,275,814       2,119,596       4,403,811
El Paso              19,235             24,129             42,599             49,138             65,911
Hidalgo              13,538             18,639             23,569             39,222             84,334
Houston
632,475           919,377       1,191,885       1,883,480       4,022,859
Lubbock                7,912
9,631             11,359             12,818             15,928
San Antonio
124,000           171,931           223,090           285,667           426,667
   METRO TOTAL        1,670,090       2,270,369       2,920,946       4,605,023       9,514,810

Urban Areas 2010 2015 2020 2025 2035
Abilene                    437                   514                   591                   660                   838
Amarillo                1,727
1,784
1,932
3,137
5,871
Beaumont              11,662             13,001             14,351             18,568             24,849
Brownsville                2,659
3,699
5,097
7,317             13,274
Bryan‐College Station                3,234
4,693
6,347
8,948             14,043
Harlingen                2,778
4,110
5,538
8,054             14,835
Killeen‐Temple                4,352
6,306
9,007             17,591             38,348
Laredo                5,549
8,836             12,627             18,177             34,722
Longview                5,754
6,973
9,110             11,514             16,876
Midland‐Odessa                3,376
3,934
3,618
4,570
6,299
San Angelo                    352                   317                   297                   336                   387
Sherman‐Denison                    473                   619                   752                   868
1,405
Texarkana                1,956
1,450
1,399
1,844
2,917
Tyler                6,571 4,479

5,964
8,290             11,625
Victoria                1,741
1,815
1,912
2,520
3,755
Waco                1,881
1,651
1,533
2,544
4,272
Wichita Falls                    865
1,065
1,269
1,548
1,879
   URBAN TOTAL              55,368             65,246             81,345           116,487           196,195
GRAND TOTAL        1,725,457       2,335,615       3,002,291       4,721,509       9,711,005

C-16

It’s About Time: Investing in Transportation to Keep Texas Economically Competitive

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