NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Built Environment Analysis Tool

April 2013

C. Porter

Cambridge Systematics, Inc.

Cambridge, Massachusetts

NREL Technical Monitor: Laura Vimmerstedt 

Subcontract Report

NREL/SR-6A20-58552

May 2013
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

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Contract No. DE-AC36-08GO28308

Built Environment Analysis Tool

April 2013

C. Porter

Cambridge Systematics, Inc.

Cambridge, Massachusetts

NREL Technical Monitor: Laura Vimmerstedt

Prepared under Subcontract No. DGJ-1-11857-01

Subcontract Report

NREL/SR-6A20-58552

May 2013
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8560-001 

Table of Contents

1.0 Overview .............................................................................................................. 1-1

2.0 Tool Basis and Structure.................................................................................... 2-1

2.1 Models of Person-Miles of Travel............................................................. 2-2

2.2 Computing PMT and VMT by Mode....................................................... 2-4

2.3 Allocation of U.S. Population.................................................................... 2-5

2.4 Calculation of Energy and GHG Emissions............................................ 2-7

Energy and CO2 Content of Fuels............................................................. 2-7

Full Fuel Cycle Energy and GHG............................................................. 2-9

Automobile Energy Consumption Rates .............................................. 2-10

Transit Vehicle Energy Consumption Rates, Activity, and Load 

Factors ........................................................................................................ 2-10

GHG Emissions from Electricity Generation........................................ 2-12

3.0 Tool Validation ................................................................................................... 3-1

Total VMT and VMT per Capita............................................................... 3-1

Energy Use and CO2 Emissions by Personal Vehicles .......................... 3-2

Transit Ridership and Energy Use ........................................................... 3-3

4.0 Using the Tool ..................................................................................................... 4-1

4.1 Tool Worksheets ......................................................................................... 4-1

Scenarios ...................................................................................................... 4-1

FixedInputs.................................................................................................. 4-4

Energy .......................................................................................................... 4-6

Outputs ........................................................................................................ 4-7

‘MSA Top 50’ and ‘MSA Other’............................................................... 4-7

NHTSModel ................................................................................................ 4-9

Checks .......................................................................................................... 4-9

5.0 Limitations and Potential Enhancements ...................................................... 5-1
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List of Tables

Table 2.1 Mode Shares (Percent of PMT) from 2009 NHTS................................ 2-5

Table 2.2 Energy and CO2 Content of Fuels.......................................................... 2-8

Table 2.3 Fuel-Cycle Energy Factors .................................................................... 2-10

Table 2.4 Energy Consumption Rates for Automobiles (Btu/mi) ................... 2-10

Table 2.5 Energy Intensity of Transit Modes (Btu/vehicle-mile)..................... 2-11

Table 2.6 Default Transit Activity and Load Factors ......................................... 2-12

Table 2.7 GHG Emissions from Electricity Generation ..................................... 2-12

Table 4.1 Cell Formatting......................................................................................... 4-1

Table 4.2 Cells for Entering Built Environment Scenarios .................................. 4-1

Table 4.3 Description of Dimensions for Built Environment Scenarios ............ 4-2
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1.0 Overview

This documentation describes the development of a tool created to evaluate the 

effects of built environment scenarios on transportation energy and greenhouse 

gas (GHG) emissions, and provides guidance on how to apply the tool. The tool 

was developed in support of the U.S. Department of Energy (DOE)’s 

Transportation Energy Futures (TEF) Study, which is being led by the National 

Renewable Energy Laboratory (NREL) and Argonne National Laboratory. The 

tool is designed for scenario analysis at a national level. Key inputs include the 

amount of population by various land use characteristics that are related to 

travel behavior, including population density, employment density, urban vs. 

rural context, and pedestrian environment. Outputs include transportation 

energy use and GHG emissions. The tool includes a base year of 2010 and 

evaluation years of 2030 and 2050.

At the core of the tool is a set of linear regression models, developed from the 

2009 National Household Travel Survey (NHTS), to predict daily person-miles of 

travel (PMT) per capita as a function of various sociodemographic and land use 

variables. Mode shares from the NHTS were used to convert total PMT into 

PMT by mode. Average vehicle occupancies were then used to convert PMT by 

mode into vehicle-miles of travel (VMT) by mode. Finally, energy and emission 

factors were applied to estimate energy consumption and GHG emissions. The 

model estimates full fuel-cycle energy and emissions, accounting for energy and 

emissions associated with fuel production as well as direct vehicle operations.
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2.0 Tool Basis and Structure

The literature sources reviewed in a companion paper prepared for the TEF 

study, “Effects of Built Environment on Transportation Energy Use, Greenhouse 

Gas Emissions, and Other Factors,” identify a variety of built environment 

factors that affect travel patterns and resulting energy use. The paper also 

identifies existing analysis tools, thereby suggesting opportunities for further 

development to create tools with functionalities not already available.

Many of the existing empirical studies of land use and travel behavior are based 

on analysis of travel survey data from particular locations, combined with 

detailed land use data that is collected through a combination of existing local 

and national sources and field studies. The data from these studies, however, are 

not readily transferable into a national-level sketch analysis tool. Furthermore, it 

was determined that this research method would be too resource-intensive to 

apply for the purposes of the current effort, given the need for a nationallyapplicable model. 

Instead, the 2009 National Household Travel Survey (NHTS) was used to 

develop a new set of models to serve as the core of the built environment 

analysis tool. The 2009 NHTS, administered from April 2008 through April 2009, 

contains travel information from nearly 150,000 households nationwide, 

capturing both work and nonwork travel to provide a snapshot of travel 

behavior in the U.S. Previous versions of the NHTS have been widely used for 

travel behavior analysis. The 2009 dataset expands the opportunities for 

examining built environment relationships with the addition of a limited number 

of urban form-related variables, including:

• Census tract population density;

• Census tract job density;

• Housing unit type; and

• Perceptions of the built environment such as walking routes and safety.

Use of the NHTS dataset also permits inclusion of other variables which affect 

travel patterns, such as household size, composition, worker status, and income.

While the NHTS does not identify the specific location of individual respondents 

in the public dataset, it does identify the metropolitan statistical area (MSA) in 

which the respondent is located, if that MSA is one of the 50 largest MSAs in the 

U.S.. This allows regional-level variables to be matched with NHTS records for 

residents of large MSAs. In this effort, a number of variables related to regional 

infrastructure supply were tested.
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2.1 MODELS OF PERSON-MILES OF TRAVEL

At the core of the tool is a set of linear regression models, developed from the 

NHTS data, to predict daily PMT per person as a function of various 

sociodemographic and land use variables. Only trips less than 200 miles were 

included. Separate PMT models were developed for four travel segments:

• Work travel in the 50 largest MSAs;

• Non-work travel in the 50 largest MSAs;

• Work travel in other MSAs; and 

• Non-work travel in other MSAs.

The following variables were included in all models. Breakpoints were set based 

on analysis of the relationship between the independent variable and the 

dependent variable (PMT).

• Census tract population density (persons per square mile). Various 

segmentations of this variable were tested. A three-level segmentation with 

breakpoints at 750 and 7,000 persons per square mile was used in the final 

model, with these breakpoints showing good relationships to changes in 

PMT.

• Census tract employment density (jobs per square mile). Various 

segmentations of this variable were tested. A three-level segmentation with 

breakpoints at 100 and 1,000 jobs per square mile was used in the final model, 

with these breakpoints showing good relationships to changes in PMT. 

Employment density is used in this model as a proxy for a mixed-use 

environment, with 100 jobs per square mile roughly corresponding to a 

modest threshold of mixed uses (e.g., neighborhood retail) and 1,000 jobs per 

square mile corresponding to larger employment and/or retail centers.1

• Housing unit type. A two-level segmentation was used: single-family vs. 

multifamily. This variable was insignificant in three of the four final models 

of PMT but was retained due to policy interest.

• Gender (male or female).

 

1 In the literature, indicators of mix of uses, or diversity, have been developed based on 

relative proportion of population and employment, or of different employment 

categories. For example, a ratio of 1:1 population to employment would be considered 

well-mixed. The advantage of using employment density over a diversity index is that 

it keeps low-density tracts that have relatively equal levels of population and 

employment from being considered mixed-use, where the employment may be too 

spread out to support walk, bike, or transit trips even if total population and 

employment is relatively balanced. 
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• Household income. A three-level segmentation was used with breakpoints 

at $30,000 and $60,000.

• Age of worker (less than 36, 36 to 50, over 50) – for work-trip models only.

• Number of work trips or non-work trips – During estimation, this variable 

was used to normalize the number of work and non-work trips made by 

different individuals. The coefficient was applied to the average number of 

trips per worker per day (for work trips), or the number of nonwork trips per 

person per day, as calculated from the NHTS data.

• Region of the country (Northeast, Midwest, South, West, based on Census 

definitions). 

• Urban vs. rural location – Households may be located in a “rural” area, even 

if they are located within a metropolitan statistical area.2 

• MSA size. Within the top 50 MSAs, MSAs over 3 million population were 

separated from MSAs of less than this size. Within “other MSAs,” a 

breakpoint was set at a population of 200,000.3 The top 50 MSAs correspond 

roughly to those with a population of over 1 million.

The following regional-scale variables were included only in the models for the 

top 50 MSAs, since these were the only areas for which individual households 

could be associated with regions. 

• Travel Time Index – The ratio of the travel time during the peak period to 

the time required to make the same trip at free-flow speeds, as defined and 

measured by the Texas Transportation Institute (TTI).4

 

2 MSAs are based on county boundaries, whereas urban areas are based on census 

tract/block geography. According to the U.S. Census Bureau, an “urban area” will 

comprise a densely settled core of census tracts and/or census blocks that meet 

minimum population density requirements, along with adjacent territory containing 

non-residential urban land uses as well as territory with low population density 

included to link outlying densely settled territory with the densely settled core. For the 

2010 census, urban areas are based on an aggregation of census tracts with densities of 

at least 1,000 persons per square mile, along with adjacent tracts with densities of at 

least 500 persons per square mile. For a complete definition, see: “2010 Census Urban 

and Rural Classification and Urban Area Criteria,” http://www.census.gov/

geo/www/ua/2010urbanruralclass.html.

3 The final model also included a breakpoint at 500,000 population, but the population 

data did not include this breakpoint and the difference in the coefficients for 200,000 – 

500,000 and >500,000 was small, so the same coefficient was used for all “other MSAs”

larger than 200,000 population.

4 Texas Transportation Institute. “Urban Mobility Information.” 

http://mobility.tamu.edu/ums/media-information/faq/
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• Freeway dominance – Ratio of freeway miles to total freeway + arterial 

miles, again based on data from TTI.

The following variables were tested as potentially being of policy interest in this 

tool, but discarded due to lack of significance or unexpected signs:

• Existence of rail service in the region (yes/no) – The coefficient on this 

variable was positive, possibly because of correlation between existence of 

rail service and size of the region, which was also associated with longer trip 

lengths.

• Various metrics of transit supply and access, including vehicle revenuemiles per capita (bus, rail, and total), and track-miles for areas with rail were 

tested. A consistently significant relationship between transit supply and 

PMT per capita could not be identified. It is hypothesized that transit 

primarily affects VMT through mode shares, rather than total distance 

traveled.

It was desired as much as possible to include variables with policy relevance and 

for which future changes in variables could be intuitively understood so that 

logical scenarios could be created. Population and employment density, and 

urban vs. rural setting, are the most directly relevant variables included for this 

purpose. The transportation variables (congestion index and freeway 

dominance) may be varied to assist in understanding how congestion reduction 

strategies, or transportation investment policy, might affect travel. The 

distribution of population by region of the country and MSA size could also be 

varied for sensitivity analysis, although these are not likely to be targets of policy 

initiatives. Other variables, including income, age, gender, would not normally 

be varied for the purposes of built environment scenario analysis, but are 

included as important controlling variables.

2.2 COMPUTING PMT AND VMT BY MODE

To develop energy and GHG estimates, total PMT by trip type needs to be 

broken out into PMT and VMT by mode. Three modes are included: 

automobile, transit, and non-motorized transit (NMT). To do so requires data on 

mode shares (to calculate PMT by mode) and average vehicle occupancy (to 

calculate VMT by mode). 

Mode shares were computed from the 2009 NHTS sample data and are expressed 

on the basis of percent of PMT (rather than percent of trips, which mode shares 

commonly refer to). Mode shares were differentiated by population density 

(three levels) and by trip type (work vs. other). Consideration was given to 

applying mode shares for additional dimensions (including employment density 

and MSA size). However, increasing the number of dimensions led to 

inconsistent mode share relationships in some cases (e.g., a higher transit mode 

share in low-density than medium-density locations for non-work trips in 

“other” MSAs), due to small sample sizes in some cells. The mode share 
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relationships across two dimensions (Table 2.1) appear consistent and of 

reasonable magnitude.

Table 2.1 Mode Shares (Percent of PMT) from 2009 NHTS

Home-Based Work Trips Other Trips

Population Density Auto+Other Transit NMT Auto+Other Transit NMT 

High 78.6% 19.3% 2.1% 82.4% 13.8% 3.9%

Medium 96.1% 3.4% 0.6% 95.7% 3.0% 1.3%

Low 99.0% 0.7% 0.3% 96.7% 2.7% 0.6%

Total PMT is first calculated for 1) home-based work (HBW) trips and 2) other 

trips, using the PMT models. Total PMT by mode is then calculated by trip type 

as follows:

• Work-trip PMT = total HBW PMT * labor force participation rate * HBW auto 

mode share. The result is work-trip PMT for the average person (including 

non-workers). For automobiles, work-trip and other-trip PMT were retained 

as separate results so that different occupancy factors could be applied;

• Non-work trip PMT = total “other trip” PMT * “other trip” mode share.

The following calculations are then applied to compute VMT by mode:

• Automobile VMT = work-trip PMT / work-trip vehicle occupancy + other 

trip PMT / other trip vehicle occupancy;

• Transit VMT = total transit PMT / average transit load factor;

• NMT VMT = zero.

Automobile occupancies of 1.19 for work trips and 2.10 for nonwork trips were 

computed from the 2009 NHTS.5 An average transit load factor was computed 

based on average load factors (passenger-miles per vehicle-mile) by transit 

submode from the 2010 National Transit Database, multiplied by total vehiclemiles by transit submode from the same source. (Details of transit data 

assumptions are provided on the “Energy” worksheet in the model.)

2.3 ALLOCATION OF U.S. POPULATION

The calculations of PMT and VMT by mode, as described above, are performed 

for current and forecast U.S. population by population “cell.” These cells have 

five dimensions, with the number of levels of each noted, corresponding to the 

previously-described model variables: 

 

5 These occupancies are based on the number of person-trips divided by the number of 

vehicle-trips, so they are not weighted by trip distance.
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– Urban or rural (2);

– Region of the country (4);

– MSA population (2 each within “top 50” and “other” categories); 

– Census tract population density (3); and

– Census tract employment density (3).

The base year (2010) distribution of population in these categories was estimated 

using Census 2010 population data combined with employment data from the 

Census’ Longitudinal Employer-Household Dynamics (LEHD) dataset.6 Tractlevel population data from the Census was matched with tract-level job data 

compiled from LEHD so that joint distributions of population by both population 

and employment density could be computed. At the time of model 

development, LEHD did not include data for the District of Columbia, 

Massachusetts, or Puerto Rico, so the combined data for which both population 

and employment densities were available is not a complete representation of the 

U.S. population.7 Therefore, the percentage of population in each cell for which 

both data items were available was applied to a control total population for all 

MSAs as determined from the 2010 Census.

The tool contains a total of 106 populated cells for the “top 50” MSAs and 98 

populated cells for the “other” MSAs. The reason that there are not 144 cells in 

each category (2 x 4 x 2 x 3 x 3) is that some cells have no population. For 

example, there are no populated areas in the rural – high population and high 

employment density category. 

Total daily automobile VMT is calculated for each cell by multiplying PMT by 

population, and was then summed across cells to obtain total daily VMT for the 

entire population of the MSA group. Total transit and NMT PMT are calculated 

by summing the product of population and modal PMT by cell. Daily PMT and 

VMT are annualized using a factor of 365 since the NHTS survey data covered all 

days of the week.

For comparison purposes, daily VMT per capita is also computed for all cells. 

For the top 50 MSAs, the minimum under baseline conditions is 57 percent of the 

average, and the maximum cell value was 130 percent of the average for this 

group. This range (a factor of about 2.3) is roughly comparable to the range of 

VMT per capita across different population density levels (five density ranges) 

from analysis of the 2001 NHTS data (see built environment issue paper, 

 

6 Source: http://lehd.did.census.gov/led/onthemap/. LODES 6.0 Workplace Area 

Characteristic data, jobs are totaled by work Census Block aggregated to the Census 

Tract level.

7 A total of 70,750 tracts were identified with non-zero employment density compared to 

a total of 74,002 total tracts with population data. 
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Table 3.1). The range for “other MSAs” is 64 to 113 percent of the average value 

for this group.

2.4 CALCULATION OF ENERGY AND GHG EMISSIONS

Energy consumption and GHG emissions are calculated for both automobiles 

and transit vehicles. Both direct operating and life-cycle (full fuel cycle) energy 

and emissions are reported in the tool. Options are provided for a variety of fuel 

types in use today, including:

• Conventional and reformulated gasoline;

• Gasoline with 10 percent ethanol blend (E10);

• Conventional diesel;

• Biodiesel (20 percent blend, B20);

• Natural gas; 

• A custom “renewable fuel” for which the user specifies carbon dioxide 

equivalent (CO2e) emissions relative to conventional gasoline; and 

• Electricity.

The tool includes the following parameters:

• Energy and carbon dioxide (CO2) content of different fuels;

• Scale factors by fuel type, to convert direct energy and CO2 emissions into 

both direct and fuel cycle energy and CO2 equivalent emissions;

• Current and scenario year (2010, 2030, 2050) emission rates for electricity 

generation;

• Current and scenario year light-duty vehicle and transit vehicle energy 

consumption per vehicle-mile; and

• For transit, fraction of VMT, average load factors, and fuel type fractions by 

mode.

The assumptions are discussed in more detail below. Some of the data, 

especially for transit vehicles, were developed for a concurrent research project 

for the Federal Transit Administration to identify environmental impact factors 

for transit projects. The 2012 Annual Energy Outlook (AEO) Reference Scenario 

serves as the primary data source for current and forecast year assumptions.

Energy and CO2 Content of Fuels

A key issue in deriving energy consumption rates is the appropriate factor for 

converting gallons of fuel consumed into British thermal units (Btu) of energy. 

Conversion factors exist for both low heating value (LHV) and high heating 

value (HHV) and much confusion and disagreement exists regarding which one 

is most appropriate for application to transportation fuels. GHG reporting 
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protocols, including The Climate Registry – General Reporting Protocol8 and the 

Energy Information Administration (EIA) Voluntary Reporting of GHGs 

Program,9 use HHVs. However, LHVs are used in the GREET (Greenhouse 

Gases, Regulated Emissions, and Energy Use in Transportation Model) life-cycle 

emissions model developed by Argonne National Laboratory. We believe that 

the LHV values are more appropriate for use in internal combustion engines and 

propose to use these values.10 Reported values of LHV and HHV also vary 

slightly by source; we proposed to use the values included in the GREET model 

(v. GREET1_2011) as shown in Table 2.2. 

Table 2.2 Energy and CO2 Content of Fuels

Fuel Energy – 

Btu/gal (LHV)

CO2 – kg/gal CO2 – 

kg/mmBtu

Conventional gasoline 116,090 8.91 76.75

Reformulated or low-sulfur 

gasoline

113,930 8.73 76.63

Ethanol = 10% of gasoline 112,110 8.02 71.54

Conventional diesel 128,450 10.15 79.02

Biodiesel (B20) 126,670 8.12 64.10

Natural gas (Btu/scf) 929 58.78

Electricity (Btu/kWh) 3,412

Source: Btu content – All except natural gas from GREET, The Greenhouse Gases, Regulated Emissions, 

and Energy Use In Transportation Model, GREET 1.8d.1, developed by Argonne National 

Laboratory, Argonne, IL, released August 26, 2010. Ethanol and biodiesel blends calculated based 

on weighting of conventional gasoline and diesel with 100% ethanol and biodiesel. The natural gas 

HHV average for the U.S. is taken from the EIA Voluntary Reporting of GHGs Program 

(http://www.eia.doe.gov/oiaf/1605/emission_factors.html). This value was factored by the ratio of 

LHV to HHV in GREET (983/1,089), which provides values for natural gas at 32 degrees F instead 

of 60 degrees (the measurement point for a standard cubic foot).

CO2 content – Conventional gasoline, diesel, and biofuel factors from EIA Voluntary Reporting of 

Greenhouse Gases Program. Reformulated gasoline calculated at 2 percent oxygenate blend. 

Ethanol and biodiesel blends calculated based on weighting of conventional gasoline and diesel 

with 100% ethanol and biodiesel. Natural gas is value of 53.06 kg/mmBtu from EIA (provided for 

HHV), multiplied by ratio of HHV to LHV energy content.

CO2/mmBtu – calculated from Btu/gal and CO2/gal.

 

8 The Climate Registry (2008). General Reporting Protocol, Version 1.1.

9 http://www.eia.gov/oiaf/1605/. 

10 The HHVs assume that the energy used to vaporize water is recaptured when the 

water condenses. The LHVs assume that this energy is lost. Since most water vapor 

probably recondenses after leaving the car engine, we believe that the LHV is more 

appropriate. See http://en.wikipedia.org/wiki/Heat_of_combustion for a discussion.
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In addition to conventional gasoline and diesel, values are shown for 

reformulated gasoline, ethanol, and biodiesel. Different blends of gasoline are 

sold in different parts of the country including gasoline-ethanol blends as well as 

the addition of other oxygenates to gasoline to improve air quality. For 

automobiles the following default assumptions are included in the model:

• 2010 – Average ethanol blend across the country of 6.1 percent by energy 

content, modeled as 61 percent E10/38.5 percent conventional gasoline. The 

remaining 0.5 percent of energy content is made up of diesel and natural 

gas.11

• 2030 and 2050 – diesel, natural gas, and electricity fractions are taken from 

the 2012 AEO Reference Scenario. The remainder (95.7 percent) is assumed 

to be E10.

For buses we use a mix of 10 percent biodiesel (B20) and 90 percent conventional 

diesel for all years. As of 2010, about 8 percent of diesel used by transit 

properties is biodiesel.12 

Heating values for compressed natural gas (expressed per standard cubic foot or 

scf) and electricity (Btu per kilowatt-hour) are also shown in Table 3.1. The 

electricity conversion factor of 3,412 Btu per kilowatt-hour (kWh) is a universal 

unit that reflects point-of-use propulsion energy (“at the plug”); it does not 

account for upstream losses in electricity generation and transmission. 

Full Fuel Cycle Energy and GHG

Full fuel-cycle energy consumption and GHG emissions includes energy used 

and GHG emitted in extracting, refining, and delivering fuel (for liquid and 

gaseous fuels) as well as energy losses in electricity generation and transmission.

The GREET model (v. GREET1_2011) was used to develop ratios of fuel-cycle 

energy consumption to energy consumed directly by the operation of the vehicle, 

as well as ratios of fuel-cycle CO2e to direct CO2 (these factors reflect scaling both 

for CO2 to total CO2e, and direct to fuel cycle emissions). These ratios were 

developed for 2020 for the fuels of interest as shown in Table 2.3. (The GREET 

model only allows analysis through 2020; factors are similar for 2013.) While the 

GREET model is intended for passenger vehicle assessment, the ratios should be 

similar for heavy-duty vehicles and electric transit since the fuel sources and 

production pathways are the same. For electricity, the scaling factors for fuel 

 

11 Source: Calculated from data in AEO 2012 Early Release tables “energy consumption 

by sector and source” (motor gasoline) and “renewable energy consumption by sector 

and source” (ethanol used in gasoline blending). Quadrillion Btu converted to gallons 

using LHV values. An ethanol content of 6.1 percent by energy content corresponds to 

8.6 percent by volume.

12 National Transit Database. 
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cycle to fuel CO2 and CO2e account for the emissions associated with fuel 

production and extraction, in addition to those produced by fuel combustion at 

the electricity generation plant.

Table 2.3 Fuel-Cycle Energy Factors 

Fuel

FC to 

Operating 

Energy

FC to Fuel

CO2

FC to Fuel

CO2e

FC CO2e to 

Operating 

CO2

Conventional gasoline 1.27 1.27

RFG or low-sulfur gasoline 1.27 1.27

Ethanol = 10% of gasoline 1.29 1.27

Conventional diesel 1.22 1.28

Biodiesel (B20) 1.20 1.09

Natural gas 1.17 1.47

Electricity 2.43 1.03 1.09

Source: GREET1_2011, run for passenger car (PC) with default assumptions, outputs for year 2020. 

Automobile Energy Consumption Rates

Energy consumption rates for automobiles (cars and personal light trucks) are 

derived from the AEO 2012 Early Release, Reference Scenario. This source 

provides total energy consumption and total miles driven for light-duty vehicles 

for future years through 2035. The projections reflect the impacts of adopted 

Federal fuel economy standards for Model Years 2012-2016, but not the impacts 

of additional standards proposed for Model Years 2017-2025. The assumed rates 

are shown in Table 2.4. Rates projected for 2035 were used to represent 2050 

conditions.

Table 2.4 Energy Consumption Rates for Automobiles (Btu/mi)

2010 2030 2050

Direct 6,025 4,521 4,365

Fuel-cycle 7,652 5,742 5,544

Source: Direct: AEO – Energy Information Administration, Annual Energy Outlook 2012 Early Release, 

Reference Scenario; calculated based on total energy consumption (quadrillion Btu) and total miles 

driven (billion vehicle-miles) for light-duty vehicles. Fuel-cycle = direct * scale factor from 

Table 2.3.

Transit Vehicle Energy Consumption Rates, Activity, and Load 

Factors

Energy consumption rates for a variety of transit modes are derived from 2010 

National Transit Database (NTD) data reporting of fuel consumption and 
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vehicle-miles traveled. While buses will be subject to Federal heavy-duty fuel 

economy requirements in the future, there is no similar controlling standard for 

rail energy use, and opportunities for improvements – especially for electric rail 

efficiency – are likely to be lower than for bus technology. Since there is no 

source of agreed-upon projections for future bus and rail efficiency, energy 

consumption rates in 2030 and 2050 were scaled from 2010 rates based on the 

following relative energy consumption rates reported in the 2012 AEO Reference 

Scenario:

• Buses – based on heavy-duty vehicles (trucks); and

• Rail – based on freight rail efficiency (per ton-mile). 

Table 2.5 shows the calculated average energy intensity of the various modes 

reported in the NTD. The “efficiency factor” is the relative efficiency assumed in 

the forecast year vs. 2010.

Table 2.5 Energy Intensity of Transit Modes (Btu/vehicle-mile)

Mode 2010 2030 2050

Bus efficiency factor: 1.00 0.83 0.81

Rail efficiency factor: 1.00 0.97 0.97

Bus 33,964 28,190 27,511

Light rail 28,635 27,776 27,776

Heavy rail 19,239 18,662 18,662

Commuter rail – diesel 81,239 78,802 78,802

Commuter rail – electric 34,884 33,837 33,837

Source: 2010 – National Transit Database, for all systems reporting in U.S. Vehicle-miles are either the sum 

of passenger-car miles for rail modes, or the sum of vehicle-miles for non-rail modes. Reported 

gallons of diesel, gasoline, CNG, and biodiesel and kWh of energy consumption are converted to 

Btu using conversion factors shown previously. For CNG, the diesel conversion factor is used 

since transit agencies are asked to report CNG consumption in terms of gallons of either diesel or 

gasoline equivalent. 2030 and 2050 – adjusted from 2010 based on efficiency improvements for 

trucks (applied to buses) and freight rail (applied to rail) as derived from 2012 AEO Reference 

Scenario.

Transit vehicle activity (percent of vehicle-miles by mode) and average load 

factors (passenger-miles per vehicle-mile) were developed from the 2010 

National Transit Database and represent U.S. averages. Assumptions are shown 

in Table 2.6. Default assumptions are held constant in 2030 and 2050, but the 

user is provided with the option of changing these assumptions for the purposes 

of scenario analysis. Demand-responsive transit, vanpools, and modes 

representing a very small fraction of service (e.g., monorail, ferry) are not 

included in this inventory and therefore their energy consumption is not 

reflected in the outputs of the built environment analysis tool.
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Table 2.6 Default Transit Activity and Load Factors

Mode 

% VehicleMiles by Mode Average 

Load Factor

Bus 66.9% 9.2

Light Rail 3.0% 23.3

Heavy Rail 19.9% 24.6

Commuter Rail – Diesel 4.6% 31.5

Commuter Rail – Electric 5.6% 31.5

Source: 2010 – National Transit Database, for all systems reporting in U.S. Vehicle-miles are either the sum 

of passenger-car miles for rail modes, or the sum of vehicle-miles for non-rail modes. 

GHG Emissions from Electricity Generation

GHG emissions from electricity generation are derived from 2012 AEO Reference 

Scenario forecasts through 2035 (2035 values are used to represent 2050). 

Intensity as expressed in terms of kg GHG emissions per kWh and per mmBtu is 

shown in Table 2.7. Emissions based on “point-of-use” energy are derived 

directly from AEO forecasts of total net generation (in kWh) and GHG emissions

(in tons), and include a 7 percent adjustment for generating and transmission 

losses. GHG emissions per kWh are converted to GHG emissions per Btu using a 

factor of 3,412 Btu/kWh. These factors are applied to reported electric transit 

vehicle energy use (in Btu/mile) as calculated from the National Transit 

Database. Emissions are also calculated on a full fuel-cycle basis (i.e., net GHG 

emissions per kWh or Btu of fuel-cycle energy). This is so that emission factors 

for electric trolley buses can be developed assuming that an electric bus has the 

same life-cycle energy consumption as a diesel bus. (The NTD shows slightly 

higher energy consumption for trolley buses but this is based on reporting for 

only five systems which may have unique characteristics.)

Table 2.7 GHG Emissions from Electricity Generation

 2010 2030 2050

Point-of-Use Energy

kg GHG/kWh 0.612 0.550 0.547

kg GHG/mmBtu 179.3 161.2 160.2

Fuel-Cycle Energy

kg GHG/kWh 0.231 0.208 0.206

kg GHG/mmBtu 67.7 60.8 60.5

GHG intensity vs. 2010 90% 89%

Source: Cambridge Systematics, Inc. calculations based on 2012 AEO Reference Scenario, “total net 

generation” and “GHG emissions,” and 7 percent generating/transmission loss; 2035 values 

shown for 2050.
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It should be noted that 2012 AEO Reference Scenario shows only a very modest 

reduction in the GHG intensity of future electricity emissions. This could change 

substantially if more aggressive GHG reduction policies or other alternative 

energy programs are implemented in the future. 
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3.0 Tool Validation

A number of validation checks were applied to compare the output of the tool to 

other benchmarks of personal travel and energy use.

Total VMT and VMT per Capita

Total annual U.S. VMT is reported in the Federal Highway Administration 

(FHWA) Highway Statistics publication by vehicle type, including for all lightduty vehicles (LDV) and for short-wheelbase LDVs, which is probably the 

category most closely corresponding to personal travel. The VMT data are 

estimated by each state based on a network of traffic monitoring stations which 

is part of the Highway Performance Monitoring System (HPMS). Counts are 

multiplied by segment lengths for each monitored site and expanded to 

represent the entire roadway network. If the total of 2,026,396 million shortwheelbase LDV VMT reported in 2010 is divided by the U.S. population of 308.8 

million, the average per capita VMT is 6,560. This compares with a modeled 

average of 7,592, about 16 percent higher.

There are a number of possible reasons for discrepancies between modeled VMT 

per capita and estimates from measured sources, including: 

• The NHTS reflects 2008/2009 conditions, not 2010. (Comparing 2008 

Highway Statistics VMT and the Census’ estimated 2008 population reduces 

the difference by about 2 percent.)

• The model does not including travel by non-MSA residents. This group 

would be expected to have per-capita travel at least as high as MSA residents, 

so this factor should not account for the overestimation. 

• The model does not account for trips over 200 miles (this should also lead to 

a small underestimation of VMT, although many of the longer trips will be 

taken by air, rail, or intercity bus). 

• There may be errors introduced in Highway Statistics by state-level VMT 

measurement and estimation methods in the HPMS on which the VMT 

estimates are based. 

• Short-wheelbase light-duty VMT does not completely correspond to travel in 

personal vehicles. Some such vehicles may be used for commercial purposes, 

while some larger vehicles may be used for personal travel. Even if the 

HPMS data were accurate, the fraction of VMT attributable to personal travel 

is not known with certainty.

• There may be errors in reported or observed distances traveled in the NHTS.

Trip distances in NHTS are estimated, based on the reported origin and 

destination, rather than modeled network distances. The behavior of the 

NHTS survey sample may not be exactly representative of the U.S. 
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population as a whole. Differences may also be introduced by mode shares 

and vehicle occupancies calculated from the NHTS data and applied to the 

modeled PMT to obtain modeled VMT.

• Errors will be introduced by modeling (the regression models have relatively 

low goodness of fit to begin with) and will be combined with discrepancies 

introduced by extrapolating results modeled from NHTS sample to entire 

U.S. population subgroups. 

Person-miles of travel by automobile were estimated from Highway Statistics 

VMT multiplied by average vehicle occupancy as observed in the 2009 NHTS. 

The observed value of 3.97 trillion compares with a modeled value of 3.29 

trillion, about 17 percent lower. This is consistent with expected differences, 

given that PMT by the 15 percent of the population not in an MSA is not 

included. However it is subject to errors of the same nature as described above.

The fraction of VMT made of up work travel vs. all travel was modeled to be 36.3 

percent in the top 50 MSAs, and 28.5 percent in other MSAs. This is reasonably 

close to data reported from the 2001 NHTS that 33 percent of all VMT is travel to 

work.13 A better comparison could be made by examining the same statistic 

from the 2009 NHTS.

Energy Use and CO2 Emissions by Personal Vehicles

The 2011 Annual Energy Outlook provides historical estimates of energy use by 

light-duty vehicles. This was reported as 16.2 quadrillion Btu in 2010. To adjust 

for LDV travel by commercial vehicles, this figure was multiplied by the ratio of 

short-wheelbase to total LDV VMT of 76.5 percent, as reported in Highway 

Statistics for year 2010, providing an estimate of 11.4 quadrillion Btu by shortwheelbase LDVs. Our modeled estimate of 12.0 quadrillion Btu from personal 

automobile travel is about 5 percent higher.

Some of the same factors identified for the VMT per capita comparison may also 

be leading to differences in the energy use estimates. The basis for the AEO 

estimates of LDV energy use is not known but is probably state-reported fuel 

sales data apportioned to vehicle type by type of fuel. If based on fuel sales, this 

is a reason for relative discrepancies between the energy estimates and VMT 

estimates. If based on VMT, a comparison would be required of VMT estimates 

and fuel economy assumptions. Note that Highway Statistics reports an average 

fuel economy for all light-duty vehicles of 21.6 mpg, while AEO reports an 

average of 20.8 mpg for the light-duty stock.

In a similar manner, CO2 emissions from AEO were compared with our modeled 

CO2 emissions (actually CO2e with a minor scaling adjustment for CO2 /CO2e). 

 

13Hu, P.S. and T.R. Reuscher (2004). “Summary of Travel Trends: 2001 National 

Household Travel Survey.” U.S. Department of Transportation.
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AEO reported total U.S. emissions in 2010 of 1,071 mmt for LDVs, which is about 

755 mmt when adjusted for short-wheelbase LDVs as described above. Our 

modeled estimate of 919 mmt is about 22 percent higher.

The relative differences in modeled vs. observed (AEO) estimates for energy (5 

percent higher) vs. CO2 emissions (22 percent higher) can be explained easily by 

the difference in the heating value of fuels used. As noted in Section 2.4, we use 

the LHV and AEO appears to use the HHV. If AEO’s reported LDV CO2

emissions are divided by energy use, a carbon intensity of 66.2 kg CO2 per 

mmBtu is obtained. Our model’s carbon intensity is 73.5 kg/mmBtu. If we 

substitute HHV for LHV in our model for conventional gasoline and E10, we 

obtain a carbon intensity of 68.6 kg/mmBtu which is much closer to the AEO 

value. 

Transit Ridership and Energy Use

Our model’s estimated transit energy use of 0.72 quadrillion Btu compares 

against AEO’s estimate of 0.27 quads used by buses and 0.05 quads used by 

passenger rail (0.32 quads total, or less than half). The AEO data may not 

include urban electric rail systems which could largely account for our model’s 

overestimate compared to AEO. More investigation of the basis for AEO’s 

transit estimates would be required to further examine this factor.

The National Transit Database reports a total of 50.8 million person-miles of 

travel on U.S. transit systems in 2010. This compares with our modeled estimate 

of 189 million PMT, which is over two and one-half times greater. Part of this 

discrepancy should be due to the inclusion of walk, drive, and other non-transit 

access and egress distance considered as part of transit trips in our model, 

whereas the NTD only reports passenger-miles actually traveled on the system. 

There may be errors introduced in reporting and measurement of trip distances 

for both the NTD and NHTS, although it is not likely that NTD systematically 

underreports PMT by a substantial amount.

Transit PMT in our model is a function simply of total PMT and transit mode 

shares by trip type. Total PMT has been investigated above and not found to 

differ significantly from national estimates based on observed VMT and 

occupancy data. We compared the work-trip mode shares that we use (from the 

2009 NHTS) with mode shares from the 2009 American Community Survey 

(ACS). The mode shares we use average 5.9 percent when weighted across the 

three population density classes by the population in each class (calculations 

shown on the “FixedInputs” worksheet). This compares with 5.4 percent from 

the ACS (trip mode shares weighted by average trip length, and excluding workat-home trips), so our calculation of transit PMT for work trips appears 

reasonable.

Unfortunately a national-level independent source similar to the ACS is not 

available for comparing non-work transit trips. Analysis of the 2001 NHTS did 

show that over half of transit PMT is for work trips, which is considerably 
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greater than the 18 to 24 percent of PMT for work trips found in our model 

(values noted for top 50 and other MSAs, respectively).14 Our model uses a 

weighted non-work transit mode share of 5.0 percent (observed from NHTS 

data), which seems high if work trips still make up a majority of all transit travel. 

If our model overestimates non-work transit PMT, that could also account for 

some of the difference between our estimate and the NTD estimate. To fully 

explain the difference between modeled transit PMT and PMT reported in the 

NTD would require further investigation.

 

14 Hu and Reuscher, op cit.
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4.0 Using the Tool 

The primary purpose of the tool is to test the effects of built environment 

strategies on outputs including PMT, VMT, energy use, and GHG emissions. 

Built environment strategies are changed on the “Scenarios” worksheet. Various 

other parameters on the “FixedInputs” and “Energy” worksheets can be changed 

as well. The mechanics of each worksheet are described below.

Some cells have a colored background or text. Their meaning is identified in 

Table 4.1. 

Table 4.1 Cell Formatting

Cell Formatting Significance 

Pale yellow background User may change input

Blue background Key intermediate output (useful for internal checking/QC)

Blue font Data input from an external source

Yellow background with 

bold red font Invalid data (conditional formatting)

Grey shaded Data not used in final version of model

4.1 TOOL WORKSHEETS

Scenarios

The user should enter a scenario name, description, and date of analysis in cells 

E2:E4. These will carry through to other worksheets. The data describing the 

built environment scenarios are entered in the cells shown in Table 4.2, as 

described below. 

Table 4.2 Cells for Entering Built Environment Scenarios

MSA Group/Scenario Year Change in percent of 

population by population & 

employment density class

Change in pedestrian 

design index by population 

density class

Top 50 MSAs in 2030 J16:L18 P16:P19

Top 50 MSAs in 2050 J47:L49 P47:P49

Other MSAs in 2030 J80:L82 P80:P82

Other MSAs in 2050 J111:L113 P111:P113
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Entering the “Change % of Population” by Population and Employment 

Density Class

The scenario tables for user input are dimensioned two ways: by population 

density class (three classes) and by employment density class (three classes). The 

thresholds for each class and a brief description of each are provided in Table 4.3. 

These dimensions represent the national distribution of population and 

employment density at a census tract level. Census tracts, which are set for a 

consistent population averaging around 4,000 residents, can vary in physical size 

from an urban neighborhood to a rural village or township. “Low” population 

densities generally reflect a rural environment, with “medium” densities 

reflecting a suburban environment (a wide range of characteristics is possible) 

and “high” densities a strongly urban environment (or suburban with extensive 

multi-story developments). “Medium” and “high” employment densities are 

intended to correspond to a mixed-use environment that supports shorter 

driving trips and possibly walk and transit trips, with high densities reflecting 

larger employment centers. The extent to which walk and transit trips are 

supported will depend not only upon densities, but also upon design 

characteristics and how well the commercial uses are integrated with the 

residential uses. 

Table 4.3 Description of Dimensions for Built Environment Scenarios

Employment Densitya

Population 

Density

Low (<100 jpsm) Medium (100 – 1,000 

jpsm)

High (>1,000 jpsm)

Low (<750 

ppsm)

Low-density suburban or 

rural residential 

neighborhoods. Nearly 

all travel by auto.

Low-density suburban or 

rural residential 

neighborhoods with some 

commercial activity. 

Nearly all travel by auto.

CBD or other major 

employment center; 

possible focal point of 

transit trips.

Medium (750 – 

7,000 ppsm)

Low- to medium- density 

suburban residential 

neighborhoods. Most 

travel by auto.

Urban or suburban areas 

with moderate-density 

residential neighborhoods 

and commercial activity. 

Some transit and walk 

trips may be possible with 

good design and wellmixed uses.

CBD or other major 

employment center, with 

some residential areas 

providing potential for 

local transit and walk 

travel.

High (>7,000 

ppsm)

High-density residential, 

but not mixed-use. 

Transit use primarily for 

work trips.

High-density residential 

with neighborhood/small 

scale commercial. Good 

potential for transit use, 

some walk trip 

opportunities.

High-density employment 

centers and residential 

mixed or in close 

proximity. Many 

opportunities for walking 

and transit use.

a These descriptions are based on professional judgment and have not been validated against real-world 

data. However, as previously noted, the density thresholds were selected to correspond to observed 

differences in travel behavior.
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The values entered into the table represent the percent of total population within 

the MSA group that is being “shifted” into or out of each density group. The 

user-entered values should be realistic, based on the baseline forecast percent of 

population in each class, as shown in columns D:G. The “scenario % of 

population” is shown in columns S:V and is computed as the “baseline % of 

population” minus the “change % of population” entered by the user. In no case 

should the “scenario % of population” be less than zero. Values less than zero in 

these tables will be highlighted in bold red font with yellow background.

Note that only eight cells are user-entered for each MSA group and scenario 

year. The ninth cell (not highlighted) is calculated from the remaining eight cells 

so that the total change in population is 0.0%.

Directly under the user-input table is shown the amount of population being 

“shifted” as a percentage of the total forecast population growth between 2010 

and 2030 (or 2010 and 2050). While it is possible for this value to exceed 100 

percent, it would mean that not only is all new population growth shifted, but 

some existing population is shifted (e.g., through redevelopment). A value in the 

range of 50 to 75 percent or less for this parameter is probably more realistic.

Below the total population for each MSA group/year are shown tables for 

population in urban areas vs. rural areas. The total user-entered change in each 

cell is allocated to urban vs. rural in proportion to the baseline distribution of 

urban/rural population for that cell.

The user should enter different population change scenarios for the top 50 MSAs 

vs. other MSAs because the baseline population distributions are quite different. 

(Other MSAs have less population in high-density areas and more population in 

low-density areas.) However, as a shortcut, the user may choose to scale the 2050 

changes from the 2030 changes. For example, doubling the percent change in 

each cell between 2030 and 2050 would represent a continuation of present-2030 

trends through 2050. Care must be taken to ensure that the “scenario % of 

population” in 2050 is not less than zero for any cell.

Note that after the population distributions are changed, the orange “update 

data” button should be pressed so that outputs are correctly recalculated. This 

command causes the pivot tables on the worksheet to be updated.

Entering the “Change in Pedestrian Design Index” by Population Density 

Class

The pedestrian design index is used to represent factors influencing walkability 

that are not captured in the population and employment density factors above. 

A positive change in the pedestrian design index indicates a more favorable 

environment for walking and transit access (e.g., more complete sidewalk 

network, smaller block sizes, a well-connected pedestrian network).

The impact of a change in pedestrian design is evaluated using elasticities 

entered on the “FixedInputs” worksheet (see below). The elasticity, combined 
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with the percent change in the design index entered by the user on this (Scenario) 

worksheet, is used to calculate the percent change in person-travel for walk and 

transit modes. The specific index that is represented (e.g., sidewalk coverage, 

pedestrian environment factor) is entered by the user on the “FixedInputs”

worksheet and carried through to cells P11 and P12. It can be different for walk 

vs. transit. However, only one percent change in design index can be applied by 

the user.

As with scenario population by cell, changes are expressed relative to baseline 

conditions, i.e., the change in both 2030 and 2050 is expressed relative to 

conditions extrapolated based on 2010 data. Therefore, scenarios would logically 

include a larger change in 2050 than in 2030.

FixedInputs 

This worksheet contains various global inputs that should not normally be 

changed by the user. However, some inputs (pale yellow cells) may be changed

to test the sensitivity of the model to different assumptions. Columns B:I store 

data that is referenced by other worksheets, while Columns K and beyond store 

original data (for values that may be changed by the user) and provide sources.

Note that if any values are changed in this worksheet, the orange “update data”

button should be pressed so that outputs are correctly recalculated. This 

command causes the pivot tables on the “Scenario” worksheet to be updated. 

Population and Workforce Control Data

This section includes population and labor force data for years 2010, 2030, and 

2050 as derived from Census Bureau forecasts and other sources. The following 

variables are set to their current values as defaults, but may be changed by the 

user:

• Labor force participation rate – The default values for 2030 and 2050 are 

scaled from the current (2010) rate, as reported by the Bureau of Labor 

Statistics, based on the Census-projected percent of population in the 18-64 

age group.

• Change from Census growth – This variable can be used to adjust 2030 and 

2050 population relative to the Census forecast. A value of 0% means that the 

Census forecast is used. A value of -100% means no growth, and a value of 

+100% means a doubling of growth.

• % of U.S. in MSA – This variable represents the percent of U.S. population 

that lives in a Metropolitan Statistical Area (MSA). The value was 84.9 

percent in 2010. The Census Bureau does not forecast population by MSA so 

the default is retained at the same value in 2030 and 2050.

• % of MSA pop/workers in Top 50 MSAs – This variable represents the 

percent of MSA population and workforce that lives or work in the 50 largest 
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MSAs. The value for population was 64.2 percent in 2010 and workers are 

assumed to have the same distribution as population.

The following other key assumptions are noted:

• The population by region of the country (four regions) is extrapolated from 

2010 population by region, based on growth rates in regional forecasts 

produced by the Census Bureau in 2005 (the latest available). Population by 

region in 2050 is extrapolated. See cell comments and formulas for details of 

the extrapolation.

Coefficients for Fixed Variables in Model

The models of PMT (described on the “NHTSModel” worksheet) includes a 

number of demographic and regional infrastructure variables. Providing joint 

distributions of population by these variables, along with the built environment 

variables, would have required the creation of intractably complex scenarios. 

Instead, default distributions are provided for these variables (based on 2010 

data), and the coefficient values are averaged based on these distributions and 

added to the fixed coefficient in the model. (The coefficients on these variables 

are imported from the “NHTSModel” worksheet and shown to the right of the 

distributions). However, the default distributions may be adjusted to test global 

sensitivity to changes in demographics and infrastructure supply. The specific 

variables are:

• Demographics – Household income (three categories), gender, and worker 

age (three categories). The primary change of interest here would be 

increasing or decreasing incomes. (Note that incomes are in constant 

dollars.)

• Level of congestion – This is the ratio of congested to normal travel time at 

the metropolitan area level, as described in Section 2.1. A higher value 

indicates higher levels of congestion. This variable could be changed to 

evaluate the global travel, energy, and GHG impacts of actions (such as 

infrastructure investment) that increase or decrease congestion. However, 

only impacts on PMT and VMT are captured, not impacts on mode share, or 

reduced vehicle efficiency under congested conditions.

• Freeway vs. arterial dominance – This is the ratio of freeway lane-miles to

total freeway and arterial lane-miles in the metro area. A lower ratio (less 

than 25 percent) means relatively fewer freeways. This variable could be 

changed to test the global impacts of relative investment in freeways vs. 

arterials.

For both level of congestion and freeway vs. arterial dominance, default shares of 

one-third each are provided. The breakpoints were set to divide metro area 

population into roughly three equal groups according to these variables.
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Other Inputs

This subsection includes the average number of trips per person or worker per 

day, vehicle occupancy, and mode shares. All of the values are taken from 

analysis of the 2009 NHTS and are not intended to be changed.

Pedestrian Design Elasticities

The impact of a change in pedestrian design is evaluated using elasticities of 

transit and walk mode share with respect to a pedestrian design factor. Different 

elasticities can be entered for low, medium, and high population density areas. 

(Impacts of a percent change in design might be expected to be lower in areas of 

low density where most destinations are not within walking distance.) Default 

values are provided based on an assessment of values found in the literature, as 

provided below the user-entry highlighted cells. If the user changes the elasticity 

he or she should also identify what aspect of pedestrian design the elasticity is 

applied with respect to.

Energy

This worksheet contains a variety of inputs required to calculate energy 

consumption and GHG emissions. Data taken from AEO, GREET, or another 

source as referenced are shown in blue font. AEO values for 2035, the last year in 

the 2012 forecast, are used to represent 2050. Other data are calculated.

Inputs that are allowed to be changed by the user include:

• GHG emissions (CO2e) from electricity generation – GHG intensity vs. 

2010: This factor represents the relative intensity of GHG emissions (GHG 

per unit of electricity generated) in 2030 and 2050, relative to 2010.

• Light-duty vehicle energy & GHG – energy intensity vs. 2010: This factor 

represents the relative energy intensity (Btu/mile) of light-duty vehicles in 

2030 and 2050, relative to 2010. The default values from 2012 AEO Reference 

Scenario account for Model Year 2012-2016 Federal fuel economy standards, 

but not more stringent standards for years beyond 2016. 

• LDV % energy by fuel type: This is the percentage of each fuel used in light 

duty vehicles in 2030 and 2050 (expressed as a percent of total energy 

content – not volume). The user can enter fractions for diesel, biodiesel (B20), 

compressed natural gas, renewable fuel (user-specified custom blend – see 

below), and electricity. The remaining fraction (not user-entered) is assumed 

to be a standard 90% gasoline/10% ethanol blend.

• CO2e ratio for renewable fuel vs. gasoline: This allows the user to specify a 

custom-blend renewable fuel with lower emissions than conventional 

gasoline. The user can specify direct and life-cycle emissions relative to 

conventional gasoline (expressed as a percentage).
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• Transit improvement (relative energy consumption vs. 2010): These factors 

are the relative energy consumption of bus and rail transit vehicles in 2030 

and 2050, relative to 2010. The defaults shown for buses are from freight 

trucks, and for rail are from freight rail, from the 2012 AEO.

• % of transit VMT by mode: This is the fraction of national vehicle-miles 

traveled (passenger-car miles for rail) traveled by each mode as a percent of 

all the transit modes included. Default values are set at the 2010 values as 

determined from the National Transit Database.

• Load factor vs. 2010: This is the relative load factor for all transit vehicles in 

2030 and 2050, expressed as a ratio to the 2010 load factor. It can be used to 

test policies that would increase the utilization of transit vehicles, such as 

transit-oriented development that balances jobs and housing along a transit 

corridor to increase loads on trips in the non-peak direction of travel. The 

load factor is defined as the ratio of passenger-miles to vehicle-miles, i.e., the 

average number of passengers per vehicle across all systems.

• Transit fuel use: These factors represent the fraction of each fuel (on an 

energy content basis) used in buses and in diesel commuter rail. Default 

values for 2030 and 2050 are set at the 2010 values.

Outputs

This worksheet summarizes the key outputs from the tool. Outputs are shown 

for 2010, 2030 baseline and scenario, and 2050 baseline and scenario. The outputs 

reflect only impacts associated with population living within metropolitan 

statistical areas (MSAs), since the tool does not provide for analysis of built 

environment scenarios in non-MSA areas where options for reducing travel are 

much more limited. As of 2010, about 85 percent of the U.S. population lived in 

an MSA.

Travel impacts are shown including VMT as well as PMT by mode. Per capita 

values of both are also provided as a benchmark, along with the percent of PMT 

by mode. Note that this percentage is not the same as “mode shares” that are 

typically reported, which represent the percentage of trips by mode.

Energy and GHG outputs include energy consumption in quadrillion Btu and 

GHG emissions in million metric tons of carbon-dioxide equivalents (mmt CO2e). 

Outputs are shown for both direct and full fuel-cycle energy and GHG emissions.

‘MSA Top 50’ and ‘MSA Other’

These worksheets are where the calculations are performed for travel impacts by 

population cell. Rows 1:13 import the relevant model coefficients from the 

“NHTSModel” worksheet. These coefficients are referenced from VLOOKUP 

functions and must be kept in alphabetical order. None of the data on these 

worksheets should be changed by the user.
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Rows 18:123 (MSATop50) and 18:117 (MSAOther) contain the calculations for the 

population cells. Columns B:F are the relevant cell class descriptions by 

urban/rural, region of the country, MSA size (MSApop), population density, and 

employment density. Column J contains the percent of U.S. metropolitan area 

population in each cell as of 2010. For the purposes of this model, workers (i.e., 

residents who travel to work – not the same as jobs) are assumed to be 

distributed in the same way as population. These percentages are used to 

calculate the baseline population by cell (columns G and h) using the worker and 

population control totals in the “FixedInputs” worksheet.

Columns K:P contain population and worker totals for the 2030 baseline and 

scenario conditions. The baseline population is scaled off the 2010 population, 

inflated by the growth rate for the respective region of the country from 

“FixedInputs.” Workers are scaled from population based on the labor force 

participation rate from “FixedInputs.” The scenario population is obtained from 

lookup functions referencing the relative change in population by density and 

urban/rural as calculated from the “Scenarios” worksheet. Columns Q:V repeat 

these calculations for 2050.

Columns X:Y are the daily modeled PMT by trip type: PMT per worker for work 

trips, and PMT per person for other trips. 

In columns Z:AS, the PMT values are split out by mode and also determined for 

the future year baselines and scenarios. In these columns all PMT values are 

expressed per person. Notes on these calculations are as follows: 

• The PMT values for transit and NMT in this block are the sum of work and 

other trip PMT. The baseline values for all modes reflect total PMT 

apportioned by mode shares by trip type from the “FixedInputs” worksheet.

• The scenario values for transit and NMT reflect adjustments for the “change 

in ped design index” factor from the “Scenarios” worksheet. The increase in 

transit and NMT PMT is then subtracted from the auto PMT so that total 

PMT is the same (for each population cell) for the baseline and scenario 

condition.

• With the default inputs, average PMT for work trips is lower in 2030 and 

2050 than in 2010, because of the lower forecast labor force participation rate 

in these years. Average PMT for other trips is assumed to be the same as in 

2010.

Columns AU:AY compute the total daily auto VMT for each population cell and 

scenario. This is done by multiplying auto PMT per capita by the population of 

each cell and dividing by average vehicle occupancy. Different occupancy 

factors are used for work vs. other trips. Note that the largest difference in outputs 

between the baseline and scenario condition in each year will generally be caused by a 

redistribution of population across cells – not by VMT changes within each cell, 

which are only affected by the pedestrian design factor.
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Columns AZ:BE show the average VMT per capita in each cell, and compare the 

2010 average as a percent of the average across the entire population. This 

calculation is performed to examine how the range of predicted VMT per capita 

varies across population cells. This block of output is provided for comparison 

purposes only and is not used in further calculations.

Column BG contains text strings used in the lookup functions.

Summary statistics across all population cells are computed in rows 125:154 

(MSATop50) and in rows 119:148 (MSAOther). Blue highlighted cells are 

summary outputs that are of particular interest. These outputs are summed 

across the MSATop50 and MSAOther worksheets to provide the summary 

output on the “Output” worksheet. However, it can be useful for diagnostic 

purposes, or for research interest, to examine how these outputs differ for the 50 

largest MSAs vs. other MSAs.

One important note is that the total 2030 and 2050 population and workers, as 

computed across the summary cells in these worksheets, may not equal the control total 

population and workers as determined from the Census data. This is a result of the 

algorithm used to forecast population in each cell based on growth rates by 

region of the country. While it would be possible to apply a “rebalancing” to 

match the control total population, as long as the scenario and baseline 

populations in each year are equal, the key model results (including differences 

between the scenario and baseline, and per-capita indicators) should not be 

significantly affected.

NHTSModel

This worksheet contains the coefficients for the daily PMT models developed 

from the 2009 NHTS data, as well as descriptive statistics for the models. The 

models are:

1. Home-based work PMT in the 50 largest MSAs;

2. All other PMT in the 50 largest MSAs;

3. Home-based work PMT in other MSAs; and

4. All other PMT in other MSAs.

None of the data on these worksheets should be changed by the user.

Checks

This worksheet contains various checks on the model’s intermediate and final 

output as described in Section 3.0.
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5.0 Limitations and Potential 

Enhancements

While an advancement over previous methods used for built environment 

analysis at a national scale, the tool still contains a number of limitations, and a 

variety of enhancements would be possible to further mine the NHTS data or 

take advantage of additional data from other sources.

One limitation of the models is that variables describing overall metropolitan 

area urban form/structure, such as average density or population-weighted 

density, were not included. Such variables could have been developed and 

appended to records from the 50 largest MSAs. However, overall average 

density has been found to be of questionable value in explaining travel behavior. 

Potentially more useful measures, such as population-weighted density, were 

not developed due to resource limitations. It was also not possible to include 

household-level variables such as regional accessibility (i.e., relative access to 

jobs and other destinations) or distance from the region’s core, since the location 

of individual households was not known. Both of these variables have been 

found to be significant in predicting per-capita travel. To the extent that either is 

strongly correlated with variables included in the model (such as tract-level 

density), the model may therefore be picking up some of these confounding 

effects and overstating the contributions of the variables that were included. 

Mode shares for auto, transit and NMT are applied based on population density, 

but are not modeled. Models could be tested to predict mode shares as a 

function of the joint population and employment density variables used for 

predicting PMT. This could further increase the sensitivity of the model to built 

environment scenarios.

The use of population and employment densities as the primary built 

environment descriptors is also fairly crude. Such measures do not capture more 

fine-grained relationships such as the extent of land use mixing, network 

connectivity, etc. within a census tract. The matching of local/neighborhoodlevel metrics with NHTS data would require geocoded data. Such data is 

potentially available from over 20 states that funded “add-on” samples for the 

2009 NHTS. Additional place-specific metrics could be identified from a newlydeveloped U.S. Environmental Protection Agency (EPA) dataset, the Smart 

Location Database, that includes a variety of built environment measures for the 

entire nation. However some effort would be required to obtain geocoded NHTS 

data from a sample of states sufficient to represent the U.S., match these data 

with EPA’s indicators, and re-estimate models.