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APPENDIX D

Recommended Changes to URBEMIS for Proposed

Rule 9510 (Indirect Source Review) and

Rule 3180 (Administrative Fees for Indirect Source Review)

Area and On-road: Jones & Stokes Memo

Operational: Nelson/Nygaard Memo

December 15, 2005
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Revised Memorandum

Date: October 20, 2004 

To: Jennifer Barba, San Joaquin Valley Unified Air Pollution Control District

From: Tim Rimpo 

Subject: Recommendations for URBEMIS2002 On-Road and Area Source Mitigation 

Measures

Introduction

This memorandum describes Jones & Stokes’ proposed approach for updating 

URBEMIS2002’s on-road and area source emission calculations and mitigation measures. 

The information included in the memo combines and modifies information in two previous 

memos based on comments received from the District and the URBEMIS Working Group 

during several conference calls held during the summer of 2004. 

ON-ROAD MITIGATION MEASURES

The current version of URBEMIS2002 (version 7.5.0) includes operational mitigation 

measures that focus on travel behavior. They include measures designed to encourage 

individuals to walk, bike, or use transit in lieu of driving. A separate memo prepared by 

Nelson\Nygaard Consulting Associates (September 9, 2004) describes proposed changes 

to URBEMIS2002’s operational mitigation measures. 

In its current format, URBEMIS does not allow the user to enter mitigation measures to 

reduce on-road emissions associated with medium- and heavy-duty vehicles. This 

memorandum describes Jones & Stokes’ proposed approach for incorporating operational 

on-road mitigation measures into URBEMIS2002. 

The proposed addition of on-road mitigation to URBEMIS would focus on those measures 

that can be used to reduce emissions from diesel powered medium and heavy-duty engines 

(over 14,000 lbs). Although these on-road mitigation measures could be used to reduce 

emissions for any land use designation, they are most useful for land uses with a high 

percentage of on-road truck use, such as warehouses, manufacturing firms, and industrial 

uses. Other land uses that have a large number of on-road truck trips, such as large retail 

stores, may also reduce emissions using this new mitigation option.
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Jones & Stokes’ original proposal was to group on-road mitigation measures into the 

following four categories:

• Repower existing engines,

• Retrofit existing engines, 

• Purchase new engines, and/or 

• Use alternative fuels. 

However, after discussing this topic with the San Joaquin Valley Unified Air Pollution 

Control District (SJVUAPCD) during the May 13th, 2004 conference call, it became clear 

that this approach had too many uncertainties and could not be accurately estimated by 

URBEMIS. 

Instead, for project applicants wishing to provide on-road mitigation for medium- or 

heavy-duty trucks, the applicant would be required to work with the SJVUAPCD to 

estimate the pounds per day and tons per year emission reductions associated with the 

project. URBEMIS would be modified so that the user has the option of entering the 

pounds per day and tons per year emission reductions as calculated by the SJVUAPCD. 

This approach would work much the same way in other air districts. The project applicant 

would need to work with the air district to determine the emission reduction associated 

with a project. That emission reduction would then be entered directly into URBEMIS.

AREA SOURCE MITIGATION MEASURES

URBEMIS2002 currently can be used to estimate emissions from five categories:

• Natural gas fuel combustion,

• Woodstove fuel combustion,

• Fireplace wood combustion,

• Landscape fuel combustion, and

• Consumer products.

We propose adding one additional area source category, architectural coatings, to the area 

source emissions module. Each of these six area source emission categories is discussed 

separately below. 
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Natural Gas Combustion

Emission Factors

URBEMIS2002 can be used to estimate natural gas fuel combustion emissions from 

cooking and water and space heating using an approach first described in Tables A9-12, 

A9-12-A, and A9-12-B in the South Coast Air Quality Management District CEQA 

handbook (South Coast Air Quality Management District 1993). The equation used to 

estimate CO, ROG, NOx, and PM10 emissions from natural gas combustion is described in 

the URBEMIS2002 User’s Manual. No changes are proposed to those emission factors. 

Mitigation Measures – Natural Gas Combustion

URBEMIS currently includes three categories of area source mitigation measures: 

residential, commercial, and industrial. The residential and commercial categories include 

several mitigation measure check boxes that apply to space and water heating. They 

include:

• Solar water heaters;

• Central water heaters;

• Orient buildings north/south; and

• Increase insulation beyond Title 24. 

One additional mitigation measure - all electric landscape maintenance equipment – is 

included in URBEMIS for residential and commercial land uses. That mitigation measure 

is described separately below under the landscape fuel combustion heading. Orienting 

buildings north/south is currently the only industrial mitigation measure included within 

URBEMIS. 

Jones & Stokes proposes modifying the natural gas combustion mitigation measures so that 

only three measures are included, one each for residential, commercial, and industrial land 

uses. Each measure would be based on building energy efficiency relative to Title 24, 

California’s energy efficiency regulation for residential and nonresidential buildings. The 

three URBEMIS measures are as follows:

q Residential Land Uses: Increase energy efficiency ___% beyond Title 24.

q Commercial Land Uses: Increase energy efficiency ____% beyond Title 24.

q Industrial Land Uses: Increase energy efficiency ____% beyond Title 24.

The user would be required to turn on the appropriate measure and enter the percentage 

increase in energy efficiency. Emission reductions would be proportional to the increase in 

building energy efficiency beyond Title 24. For example, the developer of a commercial 

land use that proposes an increase in energy efficiency 10% beyond the Title 24 
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requirements would receive a 10% reduction in natural gas combustion emissions 

compared to uncontrolled emissions. 

Title 24 requires that compliance (with Title 24) be demonstrated before a building permit 

can be issued. This requirement applies to any heated building in California. 

Consequently, the percentage increase in energy efficiency beyond Title 24 would be based 

on the required compliance documentation.

Residential natural gas combustion primarily includes space heating, water heating, and 

cooking. One comment raised during the May 13th conference call was whether natural gas 

used for cooking is properly accounted for in the above mitigation. Data published by the 

California Air Resources Board (Emission Inventory Source Category 7.2 Residential 

Natural Gas Combustion, Revised November 1998) lists the percentage of residential 

natural gas use by category. That evaluation shows that approximately six percent of 

natural gas used in California residences is for cooking. Title 24 includes specific 

measures for natural gas cooking equipment. Since Title 24 covers natural gas cooking and 

because the cooking category is relatively small compared to total gas use, the mitigation 

measures listed above for total natural gas are sufficient to account for natural gas used for 

cooking.

Wood Stoves, Natural Gas Fireplaces, and Wood Fireplaces

Currently, URBEMIS includes wood stoves and wood fireplaces as separate area source 

emission categories. We propose modifying how these two emission source categories 

appear within URBEMIS and adding a third category, natural gas fireplaces. 

The user, upon selecting this category, would be shown a screen with four tabs, which 

represent four separate screens that can be selected. As illustrated in the following table, 

the first screen would list the percentage of woodstoves, natural gas stoves, and fireplaces 

associated with the selected residential land uses. The remaining three screens would 

show the values that could be modified by the user for woodstoves, natural gas fireplaces, 

and wood fireplaces, respectively.

In the San Joaquin Valley, the percentage of homes with each of the three hearth options 

would vary based on housing density allowed by the SJVUAPCD’s Rule 4901. For 

example, if the residential land uses entered by the user had a housing density of three per 

acre, then URBEMIS would, on the first “hearth” screen, show the percentages in the 

second column of Table 1. 

The following table illustrates the percentages for the three situations allowed by Rule 

4901. Where the housing density is 3 per acre, 2 homes or 67% could use woodstoves and 

33% of the homes would be allowed to have wood stoves. For housing densities 

exceeding three per acre, the percentage of woodstoves would decrease and the percentage 

of natural gas stoves would increase. Where the density of homes is between 2 and 3, 
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100% of homes are assumed to have wood stoves. Finally, when the density of homes is 2 

per acre, half are assumed to have a fireplace and half are assumed to have woodstoves.

Table 1. Wood Stove, Natural Gas Fireplace, and Wood Fireplace Percentages

PERCENTAGE OF HOMES WITH THIS OPTION

3 or more homes per 

acre 

More than 2, 

less than 3 

homes per acre

2 or fewer 

homes per acre

Wood Stoves 67% 100% 50%

Natural Gas 

Stoves

33% 0% 0%

Fireplaces 0% 0% 50%

None of the 

Above

0% 0% 0%

Total 100% 100% 100%

Assumption Assumes 3 per acre Assumes 2.5 

per acre

Assumes 2 per 

acre

Based on SJVUAPCD Rule 4901.

For areas outside of the San Joaquin Valley, URBEMIS will assume the following 

percentages:

• Wood Stoves: 35%

• Natural Gas Stoves: 55%

• Fireplaces: 10%

• None of the above: 0%.

These percentages represent the existing percentages within URBEMIS for wood stoves 

and fireplaces while also assuming that the remaining homes will have natural gas stoves. 

Each air district would have the option to modify these default percentages.
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Wood Stove Emission Factors 

The emission factors currently used by URBEMIS to calculate wood stove emissions are 

the most recently available, based on a review of the wood stove emission factors at the 

U.S. EPA’s emission factor web site 

(http://www.epa.gov/ttn/chief/ap42/ch01/final/c01s10.pdf). Consequently, no changes are 

proposed to URBEMIS’ wood stove emission rates. The emission rate equations are listed 

in the URBEMIS2002 User’s Manual.

Natural Gas Stove Emission Factors

Jones & Stokes will incorporate natural gas stove emission factors into URBEMIS using 

the AP-42 emission factors for natural gas combustion (U.S. EPA, 1998). Emissions will 

be estimated using the following equations:

Pounds per day = A * B * C * D

Tons per year = Pounds per day * (E / 2000)

Where: A = fireplace emission rate for each pollutant,

ß 94 pounds NOx / standard cubic foot (scf),

ß 40 pounds CO/scf,

ß 5.5 pounds ROG/scf,

ß 7.6 pounds PM10/scf, and

ß 0.6 pounds SO2/scf.

B = amount of natural gas burned per day (scf),

C = number of residential units, 

D = percentage of units with natural gas fireplaces, and 

E = days per year (for tons per year emission calculations).

The emission equation will assume that the average stove is 30,000 Btu’s, that there is 

1,020 Btu’s per standard cubic foot of gas, that the stove is used for an average of 2 hours 

per day during the winter months, and 100 days per year (200 hours per year).

Wood Fireplace Emission Factors

The emission factors currently used by URBEMIS to calculate fireplace emissions are the 

most recently available, based on a review of the fireplace emission factors at the U.S. 

EPA’s emission factor web site http://www.epa.gov/ttn/chief/ap42/ch01/final/c01s09.pdf

Consequently, no changes are proposed to URBEMIS’ fireplace emission rates. 
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Wood Stoves, Natural Gas Fireplaces, and Wood Fireplaces Mitigation Measures

Currently, URBEMIS2002 has no mitigation measures for woodstoves or wood fireplaces 

(Natural gas fireplaces are not currently part of URBEMIS2002.). Mitigation measures 

will be included in URBEMIS2002. The proposed change will allow the user to make 

changes to the percentages of wood stoves, natural gas fireplaces, and wood fireplaces that 

will be installed in new residences.

Landscape Fuel Combustion

Emission Factors

Landscape maintenance equipment generates emissions from fuel combustion, from 

evaporation of unburned fuel, and from fugitive dust generated by equipment such as leaf 

blowers. Emissions include NOx, ROG, CO, and PM10. The emission factors used to 

estimate equipment emissions include exhaust and evaporation. Emission factors have not 

yet been developed for the fugitive dust generated by certain types of equipment generate. 

Equipment in the landscape category includes lawn mowers, roto tillers, 

shredders/grinders, blowers, trimmers, chain saws, and hedge trimmers used in residential 

and commercial applications. Engines in this category are 25 horsepower or less. This 

category also includes air compressors, generators, and pumps used primarily in 

commercial applications (California Air Resources Board 2004).

The California Air Resources Board has enacted regulations to limit emissions from 

landscape maintenance equipment. Beginning in 1994 these regulations imposed emission 

limits on all landscape maintenance equipment sold. Those regulations became more 

stringent for equipment sold in 1999 and later. Consequently, the emissions from this 

source category are similar to automobile emissions in that the turnover in the equipment 

fleet plays an important part in how quickly emission reductions are achieved.

URBEMIS2002 estimates emissions from this source category based on the year in which 

the user is attempting to estimate emissions. The California Air Resources Board has 

prepared estimates of emissions in 2000 and 2010. The proposed equations for this 

source category are divided into residential and commercial categories. The residential 

equation applies only to SFHU. The commercial equation is based on emissions per 

business unit and includes multifamily residential land uses.

The equations shown below are similar to those currently used by URBEMIS except that 

the pounds of pollutant per single-family residential unit and commercial business have 

been updated to reflect recent data developed by the California Air Resources Board 

(Cordero, M. and W. Wong, 2003).

2000 Emissions - Residential

ROG (pounds/day) = 0.0077 pounds ROG / SFHU/day * SFHU
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CO (pounds/day) = 0.0449 pounds CO / SFHU/day * SFHU

NOx (pounds/day) = 0.0003 pounds NOx / SFHU/day * SFHU

PM10 (pounds/day) = 0.0002 pounds PM10 / SFHU/day * SFHU

2000 Emissions - Commercial

ROG (pounds/day) = 0.1593 pounds ROG / Business Unit * Number Business Units

CO (pounds/day) = 0.9298 pounds CO / Business Unit * Number Business Units 

NOx (pounds/day) = 0.0061 pounds NOx / Business Unit * Number Business Units 

PM10 (pounds/day) = 0.0041 pounds / PM10 Business Unit * Number Business Units 

2010 Emissions - Residential

ROG (pounds/day) = 0.0038 pounds ROG / SFHU/day * SFHU

CO (pounds/day) = 0.0305 pounds CO / SFHU/day * SFHU

NOx (pounds/day) = 0.0005 pounds NOx / SFHU/day * SFHU

PM10 (pounds/day)= 0.0001 pounds PM10 / SFHU/day * SFHU

2010 Emissions - Commercial

ROG (pounds/day) = 0.0899 pounds ROG / Business Unit *Number Business Units

CO (pounds/day) = 0.6310 pounds CO / Business Unit * Number Business Units 

NOx (pounds/day) = 0.0094 pounds NOx / Business Unit * Number Business Units 

PM10 (pounds/day)= 0.0013 pounds PM10 / Business Unit * Number Business Units 

The residential emission factors shown in the 2000 emission equations are based on total 

California single family residential emissions divided by the total number of California 

SFHU in 2000. Similarly, the commercial emission factors for 2000 are based on total 

California non-farm business emissions divided by the California’s total 2000 business 

units (U.S. Department of Commerce 2003). For the commercial equations, 

URBEMIS2002 bases the number of business units on the number of non single-family 

housing land uses specified by the user.

The 2010 emission rates are based on ARB’s estimates of emissions for this source 

category, broken into residential and commercial categories.

URBEMIS2002 will use the emission rates shown for 2000 for 2000 emissions. For 2001 

through 2009, URBEMIS2002 will use interpolated emission factors by assuming a 

uniform decrease in the emission rate each year between 2000 and 2010. In 2010 and 

succeeding years, the 2010 emission rates will be used.

Average annual emissions assume that daily emissions would occur only during the 

summer period of 180 days. The end user will be able to modify the length of the summer 

period.

Mitigation Measures – Landscape Fuel Combustion
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URBEMIS2002 currently allows the user to specify a mitigation measure in which 

electric powered equipment can be used to replace gas-powered equipment. 

However, this is currently an all or none mitigation measure. The following 

measure will allow the user to enter a percentage representing the proportion of 

residential and non-residential equipment that will be replaced by electrically 

powered equipment as follows:

q Replace ____% of residential landscape equipment with electrically 

powered equipment and provide electrical outlets at the front and rear of 

residences.

q Replace ____% of non-residential landscape equipment with electrically 

powered equipment and provide electrical outlets for the use of such 

equipment.

Consumer Products

Emission Factors

Consumer products comprise one of the largest solvent-use categories of reactive 

organic gases in California. Consumer product emissions are any chemically 

formulated product used by household and institutional consumers. They include a 

wide range of product categories, including air fresheners, automotive products, 

household cleaners, and personal care products (California Air Resources Board, 

2000).

Emissions associated with these products primarily depend on the increased 

population associated with residential development. URBEMIS estimates 

consumer products when one or more residential land uses have been selected by 

the user. ROG emissions associated with consumer product use are currently 

estimated with the following equation: 

ROG (pounds/day) = 0.0171 daily pounds of ROG per person * number of 

residential units * 2.861 persons per residence

The revised consumer products emission factor equation includes modifications of 

the pounds ROG per person and persons per residence variables:

ROG (pounds/day) = 0.013 daily pounds of ROG per person * number of 

residential units * 2.93 persons per residence (will vary by county).

The ROG emission factor is based on an average of the total estimated ROG 

emissions from consumer products divided by the total California population 

(California Air Resources Board, 2004; California Department of Finance, 2004). 
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The ROG pounds per person emission factor ranges from 0.1927 in 2003 to 0.2182 

in 2020. An average of these emission factors – 0.2055 pounds ROG per person –

will be incorporated into URBEMIS as the default emission factor. 

The persons-per-residence shown in the equation above is a California average. 

Within URBEMIS, separate estimates of persons per residence will be included for 

each county.

Mitigation Measures – Consumer Products

No feasible mitigation measures have been identified to reduce consumer product 

emissions. 

Architectural Coatings

Emission Factors

Currently, URBEMIS estimates architectural coatings emissions as part of the 

construction module but not as part of area sources. However, a percentage of 

buildings are repainted each year and the ROG emissions associated with those 

buildings will be incorporated as an area source emission category.

Architectural coating emissions will be calculated using the same approach as in 

the construction module except that two additional variables will be added, one for 

residential coatings and one for non-residential. Those variables, which can be 

edited by the user, will show the percentage of a project’s total square footage that 

will be repainted each year. We currently propose using 10% for both residential 

and non-residential buildings. However, this value may change as we continue to 

research this issue. Also, the individual air districts will have the option of 

modifying this percentage.

Mitigation Measures – Architectural Coatings

There are few feasible mitigation measures available to reduce evaporative 

emissions associated with architectural coatings. The best approaches involve 

using materials that require no architectural coatings during building construction or 

using coatings with lower ROG content than are currently required by regulation. 

Instead of being considered a mitigation measure, we will assume that the use of 

“no-coatings” building materials is part of the project. Consequently, the use of 

coatings free building materials would have to be accounted for within the 

unmitigated portion of URBEMIS. This could be accounted for be reducing the 

coating thickness or the conversion factors (2.7 for residential, 2.0 for non-
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D - 13

residential) that is used to convert building square footage to the amount of surfact 

area to be painted. 

Although the use of low ROG content coatings is a feasible mitigation measure, it is 

considered unenforceable, especially during the operational phase of a project. 

Consequently, no mitigation measures will be incorporated into the architectural 

coatings category of the URBEMIS area source mitigation measures.
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References

California Air Resources Board. 2000. Emission inventory source category 

Section 6.1 Consumer Products. Sacramento, CA.

California Air Resources Board. 2004. Staff Report: Report to the Board on the 

Potential Electrification Programs for Small Off-Road Engines. Sacramento, CA.

California Air Resources Board. 2004. 2003 Estimated Annual Average Emissions 

and 2020 Forecasted Annual Average Emissions. 2004 Almanac Emission 

Projection Data. Sacramento. CA.

California Department of Finance. 2004. Table 1: E-4 Population Estimates for 

Counties and State, 2001-2004 with 2000 DRU Benchmark. Demographic Research 

Unit, Sacramento, CA.

California Department of Finance. 2004. Table 1: E-5 County/State Population and 

Housing Estimates, Revised 1/1/2003. Demographic Research Unit, Sacramento, 

CA.

Cordero, M. and W. Wong. 2003. OFFROAD Modeling Changes Technical 

Memo: Change in Population and Activity Factors for Lawn and Garden 

Equipment. Revised 6/13/023. California Air Resources Board, Sacramento, CA.

South Coast Air Quality Management District. 1993. CEQA Air Quality 

Handbook. Diamond Bar, CA.

U.S. Department of Commerce. 2003. Table P-30 Households, July 1, 2000. 

Bureau of the Census, Washington, DC.

U.S. Environmental Protection Agency. 1998. AP-42, Section 1.4 Natural Gas 

Combustion, Research Triangle Park, NC.
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833 Market Street, Suite 900

San Francisco, CA 94103-1814

(415) 284-1544 FAX: (415) 284-1554

M E M O R A N D U M

To: Tim Rimpo

From: Patrick Siegman

Date: October 10, 2004

Subject: Final Recommendations for URBEMIS 2002 Mitigation Measures

Introduction 

This memorandum sets out recommendations to revise the operational mitigation 

component of URBEMIS 2002. These have been developed with three main aims in 

mind:

• Simplify the existing mitigation component, which while extremely detailed, is 

daunting to new users and has extensive data requirements. In particularly, the 

division between “environment factors” and “mitigation measures” can be 

confusing.

• Improve consistency. Many of the inputs to the mitigation component are 

extremely subjective (e.g. whether some, few or no bike routes provide wide 

paved shoulders and have few curb cuts). We propose making these more 

quantitative, and/or providing additional guidance in the users’ manual or within 

the program itself.

• Improve accuracy and transparency. While many of the inputs to the current 

mitigation component have been proven to have an impact of travel behavior, 

research is still at an early stage of assessing quantitative impacts, and how these 
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interrelate with other mitigation strategies. The recommendations here update the 

current mitigation component in the light of new research.

An extensive body of research has been compiled as to the impacts of particular 

mitigation strategies on travel behavior. However, in general, this has either had an 

academic focus, or been undertaken for the purposes of developing citywide or regional 

travel models. For example, many agencies have sophisticated procedures for assessing 

non-single occupancy auto travel at the level of TAZ or above, but not at the 

development level. There is extremely little guidance on how to use this data in the type 

of application needed for URBEMIS 2002 – namely, to provide quantitative estimates of 

the impact on trip generation and vehicle miles traveled (VMT) at the development level.

Many agencies do provide credits for individual developments that implement mitigation 

measures, for example when assessing impact fees or conducting traffic studies. Some 

California examples include C/CAG in San Mateo County and VTA in Santa Clara 

County. A brief, national review was also conducted for purposes of this memorandum.1

In general, however, these credit programs are only loosely based on the latest travel 

research, and it could be argued that they function more at a policy level, in providing 

incentives for developers to incorporate elements such as demand management 

programs that the agency considers desirable.

The recommendations here therefore attempt to bridge the gap between academic 

studies and complex regional or area-wide models on the one hand, and more sitespecific traffic assessments on the other hand. The emphasis is on providing the best 

possible estimate while minimizing data requirements. The overall effect, compared to 

the existing mitigation component, is to reduce the number of inputs required, but make 

them more quantitative.

It cannot be too highly stressed that the trip reductions recommended here are valid at a 

sketch-planning level only, and are subject to considerable uncertainty. While they 

should ideally be expressed as a range, in order to expressly account for this uncertainty, 

a single value is needed for purposes of the Indirect Source Review in order to allow the 

appropriate fee to be calculated. The same limitations noted in the documentation for 

the existing mitigation component still apply, and are worth repeating here:

The URBEMIS 2002 mitigation component is a significant advance over past attempts to quantify 

the benefits of air quality mitigation measures, however, users should recognize that travel behavior 

is very complex and difficult to predict. The component relies on the user to determine factors 

critical to travel behavior that are somewhat subjective. As GIS and electronic traffic monitoring 

and data collection become a reality in many cities, the ability to identify factors critical to walking, 

 

1 Agencies contacted included: New York Metropolitan Transportation Council; Atlanta Regional Commission; Alameda 

County, CA; and San Luis Obispo County, CA.
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bicycling, and transit use will be enhanced. The URBEMIS 2002 mitigation component provides a 

starting point for using currently available data to demonstrate the benefits of urban design and 

traditional mitigation measures in reducing air quality impacts.

The mitigation component results, however, should still be interpreted as the mid-point 

of a range. Recent research has pointed towards the dangers inherent in reporting precise 

values, when the results are the subject of considerable uncertainty (Shoup, 2003). 

However, although the methodological dangers are obvious, there is generally no 

question about the direction of the relationship, only its size and the appropriate 

variable. Some adjustment is better than none at all – which is what most conventional 

trip generation methodologies provide (Ewing & Cervero, 2001). In addition, existing 

project-level trip generation methodologies, even though well-accepted within the 

transportation planning and engineering profession, are themselves subject to 

considerable uncertainty, and results are reported with unwarranted precision (Shoup, 

2003).

Other considerations that should be noted include:

• The key output that is sought here is reduction in vehicle trips. Research 

results, however, often report results in terms of VMT. Where no alternative is 

available, we assume that VMT is proportional to vehicle trips.

• Elasticities are generally used to make the calculations, since when used with 

care, they provide a satisfactory, means of preparing first-cut aggregate 

response estimates for various types of transportation system changes (Pratt et. 

al., 2000). They also provide a transparent and accessible method of reporting 

results, that can be transferred from one region to another (Ewing & Cervero, 

2001).

• There are major theoretical issues regarding the direction of causality that 

have still to be resolved in the research. For example, does an increase in 

density lower vehicle trip generation rates, or do more dense places attract 

people who tend to make fewer vehicle trips? For the purposes of this analysis, 

however, the distinction is unimportant. The key issue (using the same 

example) is that more dense places are associated with fewer vehicle trips. 

• Local planning controls and development economics are assumed to provide 

an important “reasonableness” check on the recommended trip reductions. For 

example, reductions in parking supply will not normally be allowed unless the 

local jurisdiction is confident that complementary trip reduction measures will 

be applied. Equally, it is unlikely that frequent transit service will be provided 

to a destination with low potential ridership, given competing demands on an 

agency for service.
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About the Trip Generation Manual

At its heart, the URBEMIS mitigation component is a tool for modifying the average trip 

rates reported in the Institute for Transportation Engineers’ Trip Generation manual to 

make them more accurate, so that they fairly reflect the particular characteristics of a 

proposed development. Before modifying these average rates, it is therefore useful to 

understand the manual itself: how the average rates were derived; the original data 

sources that underlie the manual; and the manual’s own recommendations about when, 

and why, its average trip generation rates should be modified. Some key points are these:

• The ITE manual normally predicts trip generation from new buildings using just 

two variables. Typically, the user first selects a broad land use type (e.g. “HighRise Residential Condominium/Townhouse”). Second, the user inputs the quantity 

of that land use type (e.g. “100 dwelling units”).

• An important advantage of this simple approach is that very little information 

about a project is needed to predict trip generation, and trip generation 

calculations are simple. 

• A primary disadvantage of such two-variable formulas is that they do not take into 

account the multiple other variables (parking price, transit service, etc.) that 

transportation research has shown to strongly affect trip generation, and so the 

variation in trip rates within each land use category is frequently very high. 

Recognizing these points, the Trip Generation manual therefore advises the reader that 

the average trip generation rates reported in the manual “represent weighted averages 

from studies conducted throughout the United States and Canada since the 1960s. Data 

were primarily collected at suburban locations having little or no transit service, nearby 

pedestrian amenities, or travel demand management (TDM) programs. At specific sites, 

the user may wish to modify trip generation rates presented in this document to reflect 

the presence of public transportation service, ridesharing or other TDM measures, 

enhanced pedestrian and bicycle trip-making opportunities, or other special 

characteristics of the site or surrounding area.”

However, while the studies may have been primarily conducted at such suburban sites, it 

appears from the sources referenced that for some land uses, particularly higher density 

residential land uses, many sites studied included at least some transit service, 

sidewalks, and other characteristics associated with lower vehicle trip rates. For the 

“High-Rise Residential Condominium/Townhouse”, for example, the manual’s text shows 

that sites were surveyed in such cities as Vancouver, Canada: a city where it is difficult 

to find high-density condominiums that lack sidewalks, transit service, and a mix of uses 

nearby.
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As part of our research, we made several calls to and exchanged correspondence with 

the staff at the Institute for Transportation Engineers. The staff was unable to provide any 

additional data (beyond the text of the manual itself) on the characteristics of the 

developments used in its trip generation studies, and was also unable to provide the 

actual studies – the original data – which underlie the manual’s conclusions. Therefore, 

it is not possible to define with certainty the precise characteristics of an “average site”. 

Given this paucity of information available on the original sources for the Trip 

Generation manual’s, conclusions about the average characteristics of the different land 

uses in the manual (e.g., average residential density, or the percentage of neighborhood 

streets with sidewalks) necessarily must be estimated, rather than precisely calculated. 

Fortunately, a large body of other research on travel behavior and land use is available, 

and reasonable estimates can be made based upon this research.

Recommendations

1. Combine “environmental factors” and “mitigation measures.”

URBEMIS 2002 distinguishes between “environmental factors” for pedestrians, cyclists 

and transit (i.e., the character of the existing neighborhood), and “mitigation measures” 

(i.e. those added by the development). The environmental factors both provide a 

mitigation measure in themselves (e.g. the credit for existing or planned transit service), 

and are also used to weight the mitigation measures (i.e., a lower credit is given for a 

mitigation measure in an area that has a low environmental factor). 

The distinction does make it easier to give credits for specific mitigation measures (e.g. 

bus bulbs, sidewalks and bicycle parking). However, we recommend that the distinction 

be removed, since it also brings several important disadvantages. Most of these relate to 

either complexity, or the relative advantages of infill vs. greenfield development, as 

follows:

• The pedestrian environmental factors appear to be given less weight than the 

mitigation measures, even when it is taken into account that the environmental 

factors are also used to weight the mitigation measures. The credit for the 

surrounding pedestrian environment is 2%, compared to the maximum allowable 

reduction of 9%. This means that smaller, infill developments will be eligible for 

lower credits, since by their nature they will be more dependent on the 

surrounding environment and have more limited ability to fund mitigation 

measures. 

• On a related point, the importance of the environmental factors compared to 

mitigation measures is largely a function of scale, i.e. development size. Larger 
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projects, particularly on greenfield sites, will be starting from a “blank sheet,” and 

on-site mitigation measures will be paramount. The appropriate trip reductions for 

smaller, infill developments, in contrast, will be more a function of the 

surrounding environment.

• Combining the environmental factors and mitigation measures would make the 

component easier to understand, particularly for inexperienced users. At present, 

the separation can be confusing.

2. Scale

This question relates to the area that should be analyzed. We recommend that this 

should be either the area within a half-mile radius from the center of the project, or the 

entire project area, whichever is larger. This is the same approach taken in the existing 

URBEMIS mitigation component. In effect, the smaller the development, the greater the 

consideration given to the wider project area.

3. Provide Post-Modeling Adjustments to Reward Other Mitigation Measures

One of the impacts of these recommendations would be to narrow the range of 

mitigation measures that are considered in the analysis. Some potential mitigation 

measures are excluded even though they are likely to have a travel behavior impact, 

either because they cannot be readily quantified, or because this would risk double 

counting an impact already quantified elsewhere (i.e. another variable, such as 

intersection density, serves as a proxy). We therefore recommend consideration of how 

post-model adjustments can be used to provide financial incentives for developers to 

incorporate these mitigation measures. This may include all those that are in the current 

mitigation component, but are not recommended for continued inclusion, including:

• Street trees

• Traffic calming

• Design maximizing visual interest for pedestrians, and “eyes on the street”

• Zero building setbacks

• Direct pedestrian connections

• Street furniture and artwork

• Pedestrian signalization and signage

• Street lighting

• Low speed limits on bicycle routes

• Safe routes to schools

• Bicycle parking ordinance

• Transit stop amenities

• Route signs and displays

• Bus turnouts and bulbs
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• Structured parking

4. Modifying Average Trip Generation Rates

In general, both the recommended trip rate modifications and the overall philosophy of 

the mitigation component are similar to those in the existing URBEMIS model, and build 

extensively off this work. The major differences between the existing mitigation 

component and these recommendations are found in (a) the input variables, which are 

designed to be more quantitative and less subjective, and are fewer in number, and (b) 

the formulas, which take advantage of the latest research on residential travel behavior. 

Neighborhood-level trip generation and vehicle miles traveled vary by more than 80% in 

California cities (Figure 1). As the documentation for the existing mitigation component 

recognizes, areas with low trip generation and VMT levels have the highest development 

densities, a wide variety of uses within walking distance, safe and comfortable 

pedestrian access, paid parking requirements, and a high level of transit service. 

Similarly, residential trip rates reported in the Trip Generation manual vary widely, both 

within individual land use types, and between land use types (Figure 2). For the land use 

type “Single Family Detached Housing”, for example, reported rates ranged from a low 

of 4.31 daily trips per dwelling unit, to a high of 21.85 daily trips. The Trip Generation 

manual reports that, “This land use included data from a wide variety of units with 

different sizes, price ranges, locations and ages. Consequently, there was a wide 

variation in trips generated within this category.” Between residential land use 

categories, the variation is still greater, as would be expected. For example, the average 

trip rate for the “Residential Condominium/Townhouse” land use type is 5.86 (or 39% 

lower than the average single-family detached house), while the lowest trip rate is 1.83 

(or 80.9% lower). At the extremes, considering all residential land uses, the highest 

residential rate reported (21.85 trips/day) is more than ten-fold higher than the lowest 

rate reported (1.83 trips/day).

Figure 1 Daily Trips by Density, San Francisco Bay Area

Households/Residential Acre

<2 2-5 5-10 10-20 20-50 >50

Mean Households/Residential Acre 1.4 3.6 6.7 13.5 30.6 121.9

Daily Vehicle Trips/Household 6.4 5.9 5.0 3.8 2.9 1.2

% Reduction in Daily Vehicle 

Trips/Household compared to 

lowest density areas

0% 9% 23% 41% 55% 82%

Source: MTC Household Travel Survey, 1990, cited in Holtzclaw, 2002
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Figure 2 ITE Trip Rates for Selected Residential Land Uses 

Land Use 

Code Land Use Type Low Average High

210 Single-Family Detached Housing 4.31 9.57 21.85

221 Low-Rise Apartment 5.1 6.59 9.24

230 Residential Condominium/Townhouse 1.83 5.86 11.79

222 High-Rise Apartment 3 4.2 6.45

232 High-Rise Residential Condo./Townhouse 3.91 4.18 4.93

ITE Trip Rate

Based on these data in Figures 1 and 2, and a wide range of additional transportation 

research, we have developed a set of formulas for modifying the average trip rates for 

residential land uses has been developed. For the URBEMIS user, the procedure for 

modifying residential trip generation rates will remain generally similar to the existing 

process, with three basic steps:

1. In the “Land Use Selection” screen, the user will enter the land use types (e.g. 

“Apartment, Low-Rise”) and the number of dwelling units of each type.

2. Next, if the mitigation component is used, the user will be prompted to review the 

default values for several key variables (e.g. residential density, level of transit 

service) for each residential land use type. If the project’s land uses have 

characteristics that are different from the default values (as they usually will be), 

the user will enter the correct values, in place of the default values.

3. Within the program, the formulas described hereafter will be used to calculate the 

resulting trip generation rates.

In keeping with the conclusions of current transportation research, a single set of 

formulas is used to modify the trip rates for all residential land use types. The input 

variables for these formulas assess five key land use characteristics (or “mitigation 

measures”, in URBEMIS terms): 

• Net residential density (measured by Households per Residential Acre)

• Mix of uses (using a jobs/housing measure)

• Presence of local-serving retail 

• Level of transit service (measured by a transit service index)

• Bicycle and pedestrian friendliness (measured by an “pedestrian factor” 

index based on intersection density, sidewalk completeness, and bike lane 

completeness)
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For each ITE residential land use type, a set of default values for these variables has been 

defined. If the default values for a residential land use type are left unchanged when 

running the mitigation component, then the resulting trip generation rate will be the 

standard ITE average trip generation rate for that land use type. For single-family 

detached housing, for example, the default values include a residential density of three 

units per residential acre, a transit service index score of 0 (representing no transit 

service within one-quarter mile of the site), and an intersection density of 250 

intersections per square mile (typical of post-war cul-de-sac residential subdivisions). 

Figure 4 shows the default values for each land use type.

To achieve the lowest residential trip rate reported in Trip Generation (a manual which 

primarily measures stand-alone, single-use projects with little or no transit service), the 

input values required would include a density of 160 units per residential acre, the 

maximum level of transit service, the best possible mix of uses and local retail, and a 

pedestrian score equivalent to a complete sidewalk coverage with a network of blocks no 

larger than 300 feet on a side. This would result in a rate of 1.83 trips/day, or an 81% 

reduction from the average single-family house rate). 

This is similar to the 82% difference in household trip generation between the lowest 

density areas with the poorest transit service (6.4 vehicle trips per household per day), 

and the highest-density areas with good transit and a higher quality pedestrian 

environment (1.2 vehicle trips per household per day), as shown in Figure 1. Figure 4 

shows the input values that would be required to achieve this rate, as well as the input 

values required to achieve maximum possible reduction allowed. 

In theory, choosing the maximum possible values for each of the physical design 

variables described above could result in a residential trip generation rate as low as 0.9 

daily trips per unit. This represents a 90% reduction from the average rate for a singlefamily detached house. To achieve this rate, however, a neighborhood would have to 

have remarkable characteristics, similar to Manhattan or Hong Kong: a density of 380 

units per acre, or more than three times the average density of San Francisco’s densest 

neighborhoods (North Beach and Chinatown), the highest possible level of transit 

service, and so on.2 

The recommended reductions for the individual physical design mitigation measures for 

residential uses are summarized in Figure 3. The remainder of the memorandum 

discusses the justification for these levels, along with the mitigation measures for nonresidential uses. In general, the recommended maximums for individual components 

have been set at a level so that this overall 90% maximum reduction from the average 

 

2 While rare in California, these extreme cases of Manhattan-like densities can be seen in projects such as San Francisco’s 

single-room occupancy hotels for very low income residents, which achieve such densities by omitting parking and 

providing very small living quarters.
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D - 25

single-family house rate is maintained for residential land uses. While a greater 

reduction may sometimes seem warranted for an individual measure, a lower value has 

been selected to stay within this 90% maximum – a practice that helps avoid the 

considerable dangers of double counting.

In addition to the variables above, which primarily measure physical design 

characteristics, the formulas include mitigation measures that assess demand 

management programs and similar measures. A maximum additional reduction of 7.75% 

from the average single-family house rate is possible through these measures. 

Non-Residential Land Uses

For non-residential land uses, the general procedure for modifying rates is similar, and 

based upon many of the same research results. To modify non-residential trip generation 

rates, the following procedure is used:

1. For physical design mitigation measures, the formulas to determine percentage 

reductions are identical to the formulas for residential land uses, except for the 

‘Residential Density’ measure, which cannot apply. 

2. Additional mitigation measures are applied for demand management programs 

and similar measures. For non-residential uses, the number of available demand 

management measures is greater, as is the possible percentage reduction.

However, there is a key difference between the formulas used to modify residential rates, 

and the formulas used to modify non-residential rates:

1. For residential land uses, the percentage reductions shown for each mitigation 

measure refer to the percentage reduction from 9.57 trips per day (the rate for 

single family homes). The default values for each residential land use (Figure 4) 

are set at levels such that keeping these values generates the average trip rate for 

that land use.

2. For non-residential land uses, the percentage reductions shown for each 

mitigation measure refer simply to the percentage reduction from the average ITE 

trip generation rate for that land use. No special default values are required: they 

are simply set to create a 0% reduction as the starting value.
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Figure 3 Summary of Recommended Trip Reductions

Residential Non-Residential Comments

Physical Measures

Net Residential Density Up to 55% N/A

Mix of Uses Up to 9% Up to 9%

Local-Serving Retail 2% 2%

Transit Service Up to 15% Up to 15%

Pedestrian/Bicycle Friendliness Up to 9% Up to 9%

Physical Measures sub-total Up to 90% Up to 35%

Demand management and similar measures

Affordable Housing Up to 4% N/A

Parking Supply N/A No limit Only if greater than 

sum of other trip 

reduction measures

Parking Pricing/Cash Out N/A Up to 25%

Free Transit Passes 25% * reduction for 

transit service

25% * reduction for transit 

service

Telecommuting N/A No limit Not additive with 

other trip reduction 

measures (see text)

Other TDM Programs N/A Up to 2%, plus 10% of the 

credit for transit and 

ped/bike friendliness

Demand Management subtotal3Up to 7.75% Up to 31.65%

 

3 This sub-total excepts the measures for parking supply and telecommuting, which have no limit.
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Figure 4 Default Values for Residential Land Use Trip Generation Formulas

Default Values for Residential Trip Rate Formulas

Land Use 

Code Land Use Type

Residential 

Density

Housing 

Units

Employees Retail?

Transit 

Service

Intersection 

Density

SidewalksBike 

Lanes

Ped 

factor Low Average High

210 Single-Family Detached Housing 3 100 17 no 0.00 250 0 0 0.06 4.31 9.57 21.85

221 Low-Rise Apartment 16 100 26 no 0.06 250 0.5 0 0.23 5.1 6.59 9.24

230 Residential Condominium/Townhouse 16 100 60 yes 0.10 400 1 0 0.44 1.83 5.86 11.79

223 Mid-Rise Apartment 38 100 60 yes 0.14 400 1 0 0.44 NA 4.68 NA

222 High-Rise Apartment 62 100 60 yes 0.14 400 1 0 0.44 3 4.2 6.45

232 High-Rise Residential Condo./Townhouse 64 100 60 yes 0.14 400 1 0 0.44 3.91 4.18 4.93

Trip Rates Resulting When Default Values Are Used

Land Use 

Code Land Use Type

Residential 

Density

Mix of 

Uses

Local 

Retail Transit

Bike/ 

Ped Total

Resulting 

Trip Rate

210 Single-Family Detached Housing 0.0% -0.6% 0.0% 0.0% 0.6% 0.0% 9.57

221 Low-Rise Apartment 27.9% 0.5% 0.0% 0.6% 2.1% 31.1% 6.59

230 Residential Condominium/Townhouse 27.9% 3.9% 2.0% 1.1% 3.9% 38.8% 5.86

223 Mid-Rise Apartment 39.8% 3.9% 2.0% 1.5% 3.9% 51.1% 4.68

222 High-Rise Apartment 44.8% 3.9% 2.0% 1.5% 3.9% 56.1% 4.20

232 High-Rise Residential Condo./Townhouse 45.1% 3.9% 2.0% 1.5% 3.9% 56.3% 4.18

Example Residential Trip Rate Calculations

Land Use 

Code Land Use Type

Residential 

Density

Housing 

Units

Employees Retail?Transit 

Service

Intersection 

Density

SidewalksBike 

Lanes

Ped 

factor Low Average High

210 "Worst Case" Single-Family 0.1 100 0 no 0.00 80 0 0 0.02 - - 21.85

230 "Best Case" Res. Condo/Townhouse 160 100 150 yes 1.00 1300 1 0 0.67 1.83 - -

NA Maximum Possible Reduction 380 100 150 yes 1.00 1300 1 1 1.00 NA NA NA

Trip Rates Resulting When Example Values Are Used

Land Use 

Code Land Use Type

Residential 

Density

Mix of 

Uses

Local 

Retail Transit

Bike/ 

Ped Total

Resulting 

Trip Rate

210 "Worst Case" Singe-Family Detached -20.7% -3.0% 2.0% 0.0% 0.2% -21.5% 11.63

230 "Best Case" Res. Condo/Townhouse 51.4% 9.0% 2.0% 12.5% 6.0% 80.9% 1.82

NA Maximum Possible Reduction 55.0% 9.0% 2.0% 15.0% 9.0% 90.0% 0.95

Reductions

ITE Trip Rate

ITE Trip Rate

Reductions
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5. Data Requirements

Figure 5 shows the inputs that are required to complete the mitigation component in full, 

along with suggested data sources. Note, however, that the mitigation component can 

still be run, even if some of these inputs are missing. While no reduction would be 

granted for the particular mitigation measure for which the input was required, credits 

could be granted for other trip reduction measures.

Figure 5 Data Requirements and Suggested Sources

Suggested Source

Required Input Project

Surrounding 

Development Comments

Net residential density Project plans Block-level census data Net residential data 

excludes land not devoted 

to residential uses

Number of housing 

units

Project plans Block-level census data Same basic source as for 

net residential density

Number of jobs Project plans Census Transportation 

Planning Package. Local 

jurisdiction may provide more 

current or fine-grained data

If data are only available 

per square foot, US Dept. 

Energy produces figures on 

average employee density

Local serving retail Project plans Site observations

Below-market-rate 

units

Project plans N/A

Parking supply Project plans N/A

Transit service Transit agency maps/schedules

Intersection density Project plans Street plans Count can be automated if 

available in GIS

Sidewalk 

completeness

Project plans Site observations Count can be automated if 

available in GIS

Bike lane 

completeness

Project plans Site observations Count can be automated if 

available in GIS

Parking pricing Development 

agreement or similar

Site observations (if 

applicable)

Free transit pass 

provision

Development 

agreement or similar

N/A

Telecommuting/flexibl

e work schedules

Development 

agreement or similar

N/A

Other TDM programs Development 

agreement or similar

N/A
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6. Procedure for Small Projects

For developments in an established urban area below a certain size threshold, we 

recommend allowing them to adjust their trip generation rates based on the mode share 

in that census tract. This would avoid a disproportionate burden in gathering the data to 

document their likely trip reduction. (The analyst would need to certify that the project 

was similar in character to the existing development.) The recommended threshold is 50 

average daily baseline vehicle trips, with the baseline being that calculated by URBEMIS 

before any of the reductions from mitigation measures are applied.

7. Substitute Methodologies

The recommended mitigation levels are, in our judgment, the most appropriate for a 

model that must apply to an extremely wide range of projects and geographic contexts. 

However, it must be recognized that there may be “special cases,” where these standard 

reductions may not apply. For this reason, we recommend that any methodology for 

calculating reductions in VMT and vehicle trips may be substituted, provided that this is 

mutually agreed between the Air District and project proponent.

8. Measures Reducing VMT 

The existing mitigation component allows for reductions in VMT (but not trip generation) 

for park-and-ride lots and satellite telecommuting centers. We do not recommend any 

changes to this aspect of the mitigation component.

9. Correction Factors 

The existing mitigation component provides for trip type correction factors, based on 

evidence suggesting that certain trips are more likely to be captured by one mode rather 

than another. We do not recommend any changes to this aspect of the mitigation 

component.

A second correction factor in the existing mitigation component relates to trip distance, 

because, the documentation argues, bicycle and walking trips replace mostly shorter 

automobile trips. We recommend that this correction factor be eliminated, as there is 

little evidence to suggest that this phenomenon exists. Indeed, more complex changes in 

travel behavior are likely, such as mode shift to bicycling and walking trips being 

accompanied by a shift to closer destinations. For example, rather than drive to a 

grocery store on a freeway interchange, a household may walk to a smaller store in the 

neighborhood. Mixed use, compact neighborhoods are characterized by short overall trip 

lengths (see, for example, Kuzmyak et. al., 2003). Further evidence comes from the 

elasticities for trip reduction with respect to density, which are the same for both vehicle 

trips and VMT (Ewing & Cervero, 2001), suggesting that there is no impact on trip length.
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Detailed Justification of Recommended Mitigation Levels

Default Values for Residential Land Uses

To develop the default values for residential land uses shown in Figure 4, we had to 

overcome a significant hurdle: ITE retains no data on the characteristics of the 

developments used in their trip generation studies. Default values for average density, 

transit service levels, and other variables had to be estimated using two alternative 

methods. First, we reviewed representative projects through research of literature and 

discussions with professionals in the fields of architecture and town planning, to 

ascertain typical ranges for density and other characteristics of each land use type (for 

useful summaries, see Calthorpe, 1993, and Local Government Commission,2002). 

Second, these ranges of values were plugged into the formulas for the mitigation 

measures, and adjusted until the baseline values for each characteristic equaled the 

average ITE trip generation rates for each land use. For example, baseline density for 

Mid-Rise Apartments (64 units per residential acre) falls within the typical range 

observed from research of 45 to 125 units/acre, and when combined with other baseline 

characteristics for the land use, results in a 56.1% reduction in trip generation from the 

average rate for single family homes – the average reduction set forth in the ITE manual.

 

Finally, since the Trip Generation manual provides no daily trip generation rate for the 

“Mid-Rise Apartment” land use, we estimated a rate by extrapolating from the daily trip 

rate for the “High-Rise Apartment” land use type. The PM peak hour trip rate of 0.39 trips 

per unit for mid-rise apartments is 11.4% higher than the PM peak hour rate for high-rise 

apartments (0.35 trips/unit). Therefore, the daily trip rate for the “Mid-Rise Apartment” 

land use was estimated to be 4.68 trips per unit, or 11.4% higher than the daily trip for 

high-rise apartments (4.2 trips/unit).

Density

A considerable volume of research has investigated the links between density, 

particularly residential density, and travel behavior (for summaries, see Kuzmyak et. al, 

2003; Boarnet & Crane, 2001). Overall, the conclusions can be summarized thus: there 

is a significant, quantifiable relationship between residential density and automobile use 

(see Figure 6), but there is uncertainty regarding the degree to which this effect is due to 

the inherent effects of density, as opposed to factors for which density serves as a proxy, 

such as parking price, local retail, transit service frequency and pedestrian friendliness.
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Figure 6 Residential Density Vs. Vehicle Travel

0

5000

10000

15000

20000

25000

30000

35000

0 50 100 150 200

Households/Residential Acre

Annual VMT/Hh

SF

LA

Chicago

Source: Holtzclaw et. al. (2002).

Fewer studies have attempted to disentangle the effects of density itself. Three of the 

main exceptions are:

• Typical elasticities for vehicular travel with respect to density are –0.1 to –0.04. 

These elasticities refer to the effect of density itself, isolated from variables that 

tend to be correlated with density such as transit frequency, and are additive to 

elasticities of other built environment factors. When these factors are not isolated, 

typical elasticities for VMT with respect to density are –0.22 to –0.27 (Kuzmyak 

et. al, 2003). 

• The elasticity of density, when isolated from three other variables (diversity, 

design and destinations), is –0.043 with respect to vehicle trips, and – 0.035 with 

respect to VMT (Criterion and Fehr & Peers, 2001). However, this does not control 

for transit service levels.

• Cervero & Ewing (2001), in an update to this work, suggest a slightly higher 

elasticity of –0.05 with respect to both vehicle trips and VMT.

Note that density has been shown to have a nonlinear relationship with vehicle travel, 

with a threshold value of 25-30 units per acre below which the travel impacts of 

increased density are particularly large (Holtzclaw et. al, 2002). Holtzclaw et. al found 

that the best single variable equations to predict household vehicle travel (VMT per 
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household, or VMT/Hh) relied on Households per Residential Acre (Hh/RA). For the Los 

Angeles region, San Francisco and Chicago regions, these equations varied only slightly, 

producing the curves shown in Figure 6. For the Los Angeles region, this formula takes 

the form:

Based on this formula, the following elasticity formula is recommended for vehicle trips 

with respect to density. It is the same as Holtzclaw et. al’ work, but reduced by 40% to 

take account of the fact that much of this impact will be realized through transit service, 

mix of uses and bicycle and pedestrian levels (which tends to correlate with density). 

The baseline assumed to correspond to a zero percent trip reduction is three units per 

acre, at which density the Holtzclaw formula results in 25,914 annual vehicle miles 

traveled per household. This translates into the following formula:

Trip reduction 

 =0.6*(1-(19749*((4.814+ households per residential acre )/(4.814+7.14))-0.639

)/25914)

An apartment development of 16 units per residential acre, for example, would be 

estimated to generate 27.9% fewer trips than a three unit per acre project. The maximum 

allowable reduction recommended is 55% (equivalent to a 380 unit per acre 

development). 

With this formula, “negative” reductions also apply, with less dense developments below 

the baseline level of three units per acre (for example large-lot housing) resulting in 

higher trip generation rates. (However, as long as the mitigation component is optional 

for developers or project proponents to complete, they will be unlikely to use it for 

projects whose overall score, for all components, will result in a finding to their

disadvantage. For purposes of more accurately predicting vehicle trips and emissions, 

however, this negative reduction is useful and reflects the findings of the research 

literature.

Trip generation at the non-residential end is also influenced by density, but to a much 

lesser degree (Cervero, 1989, cited in Kuzmyak et. al, 2003). There are also far fewer 

studies investigating this relationship, and there is no comparable dataset to that for 

residential density. No reduction is recommended here.

Mix of Uses

Many references point to the impact of “diversity” or mix of uses on travel behavior. This 

is true both at the macro-scale, e.g. jobs-housing balance, and the micro-scale, e.g. the 
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availability of services within walking distance. Key references, related to both the 

direction and magnitude of this relationship, include:

• Higher densities are most beneficial to transit ridership when they result in a mix 

of residential, commercial and office uses (Lund et. al., 2004).

• The elasticity of vehicle trips with respect to “diversity” is –0.051. The elasticity 

of VMT is –0.032. In this case, “diversity” is a measure of how the project affects 

regional population/employment balance. (Criterion and Fehr & Peers, 2001)

• Typical elasticities for vehicle trips with respect to local diversity (mix) are –0.03, 

and those for VMT are –0.05 (Ewing & Cervero, 2001).

• A balance of 1.5 jobs per household is estimated to produce a bus mode share 2 

percentage points over the share for a single use area, although the degree of mix 

is not a useful estimating variable (Messenger & Ewing, 1996, cited in Kuzmyak 

et. al, 2003).

• Suburban activity centers with some on-site housing had 3-5% more transit, bike 

and walk commute trips (Cervero, 1989, cited in Kuzmyak et. al, 2003).

• The presence of retail reduces auto mode share by 2-5%, depending on 

neighborhood density. (Parsons Brinkerhoff, 1996, cited in Kuzmyak et. al, 2003).

• At suburban activity centers, the presence of retail in office buildings lowers 

vehicle trip rates by 6-8% (NTI, 2000, cited in Kuzmyak et. al, 2003).

• Employment sites with “good” nearby retail and commercial services have a 

vehicle trip rate 21.5% below the ambient rate. Sites with “fair” services showed 

an 8.3% reduction, and those with “poor” services a 5.3% reduction. This is 

attributed not just to the presence of these services, but the fact that they make 

TDM programs more likely to succeed (Comsis, 1994, cited in Kuzmyak et. al, 

2003).

The analysis is complicated by the fact that some of the most beneficial developments 

from this perspective may be single-use, in an area where another use is predominant 

(e.g. residential in an employment area). To take this into account, the following 

procedure is proposed (adapted from Criteron and Fehr & Peers, 2001):

Trip reduction = ( 1- ( ABS ( 1.5 * h – e ) / ( 1.5 * h + e )) – 0.25 ) / 0.25 * 0.03

Where: h = study area households (or housing units)

e = study area employment 

Negative reductions of up to 3% can result, and should be included.
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This formula assumes an “ideal” housing balance of 1.5 jobs per household, based on 

Messenger & Ewing (1996), and a baseline diversity of 0.25. The maximum possible 

reduction using this formula is 9%. 

This reduction takes into account overall jobs-population balance. The presence of local 

serving retail can be expected to bring further trip reduction benefits, and an additional 

reduction of 2% is recommended. This is towards the lower end of the values presented 

in the research discussed above, in order to avoid double counting with the diversity 

indicator.

Transit

The existing URBEMIS 2002 mitigation model places its primary emphasis on mode, i.e. 

whether service is provided by high-speed rail, commuter rail or bus. Within this 

framework, consideration is given to frequency (e.g. bus headways of 15 minutes or less 

score more highly than headways of 15-30 minutes).

For example, the current mitigation component would award the maximum score of 100 

to a development 0.5 miles from a BART station, even if no other transit were available. 

A part of the city with several bus lines offering 10-minute service, in contrast, would 

score much lower, even though these transit lines would carry many more passengers.

Current transit planning thinking, however, emphasizes that frequency and speed are two 

of the most important factors determining mode choice, rather than whether the service 

is provided by bus, bus rapid transit, or rail. Researchers have found that there is no 

inherent preference for rail over bus, provided that the quality of service is the same (for 

example, Ben-Akiva & Morikawa, cited in Transportation & Land Use Coalition, 2002).

Key references include:

• The average elasticity of ridership with respect to frequency is +0.3 to +0.5. 

Higher elasticities of +1.0 have been observed in suburban systems, with the 

+0.3 value more typical of urban systems. (Kittselson & Associates et. al, 2003). 

• Pratt et. al. (2003) suggest an elasticity of ridership with respect to service hours 

(i.e. a combined measure of frequency and service span) of +0.5. Ridership is 

most sensitive to frequency changes when the past service was infrequent.

• Modeling in Massachusetts suggests that halving transit service headways from 30 

to 15 minutes leads to an 8% drop in vehicle trips. A further decrease to 5 

minutes leads to a further 4% drop in vehicle trips (Pratt et. al., 2003). 

• Holtzclaw et. al. (2002) show that vehicle travel falls as transit service levels 

increase, even when holding density constant (Figure 7). In the San Francisco Bay 
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Area, a doubling of transit service from 300 to 600 (using the index described 

below) is associated with a 13% drop in VMT. An increase from 300 to 900 is 

associated with a 20% drop in VMT. In the Los Angeles region, the decreases in 

VMT are 12% and 18% respectively. However, the variable was omitted from the 

vehicle travel model presented in this paper, since density was used as a proxy for 

transit service.

• The maximum distance that people are willing to walk to transit tends to be 0.25 

miles for bus, and 0.5 miles for rail (and, presumably bus rapid transit). (Kittelson 

& Associates et. al, 2003). It is unclear whether there is a “distance decay” effect, 

whereas people are more likely to use transit at closer distances within this range 

(see Lund et. al, 2004).

Figure 7 VMT vs. Residential Density and Transit Use, San 

Francisco Bay Area

0 300 600 900

450

300

150

2

0

5000

10000

15000

20000

25000

Annual VMT/Household

Zonal Transit Density

Hh/Res Acre

Source: Holtzclaw et. al. (2002).

Unfortunately, the elasticity of service with respect to transit ridership is difficult to 

convert to vehicle trip reduction, firstly because the baseline ridership needs to be 

known, and secondly because only a proportion (18-67% is cited by Pratt et. al., 2003) 

of new transit trips were formerly made by private auto. While it is clear that there is a 

direct correlation between transit service and vehicle trips, it is difficult to employ these 

elasticities directly. For this reason, the approach here is more in line with the existing 

mitigation component, which assumes a maximum percentage reduction for transit, and 

then reduces this based on a transit environment factor.
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Various frequency-based transit service indices have been developed which have shown 

strong correlations with ridership. For example:

• In Los Angeles, the quality of four components of transit service (MTA rail, Rapid 

Bus, local bus and regional services) were rated on a scale of 0-3 for each 

community area, and then summed to provide the Transit Service Index on a scale 

of 0-12. (Nelson\Nygaard, 2002b).

• The studies by Holtzclaw et. al. (2002) used Zonal Transit Density, defined as the 

daily average number of buses or trains per hour times the fraction of the zone 

within 1/4 mile of the bus stop, or 1/2 mile of the rail station or ferry terminal, 

summed for all transit routes in or near the zone.

The Transit Service Index recommended here would combine the important features of 

all these approaches, with emphasis on frequency but with greater weighting given to 

rail services. Greater weight is also given to dedicated shuttles, in recognition of the fact 

that these are likely to be more closely targeted to the needs of the development. The 

Transit Service Index would be determined as follows:

• Number of average daily weekday buses stopping within 1/4 mile of the site; plus

• Twice the number of daily rail or bus rapid transit trips stopping within 1/2 mile 

of the site

• Twice the number of dedicated daily shuttle trips

• Divided by 900, the point at which the maximum benefits are assumed. (This 

equates to a BART station on a single line, plus four bus lines at 15-minute 

headways.)

• Developments that are larger than 0.5 miles across in any direction must be 

broken into smaller units for purposes of determining the transit service index. 

The average of all units would then be used. 

Figure 8 shows some examples of how service frequencies translate into Transit Service 

Index scores (note these are additive, if a location has more than one component).

Figure 8 Example Transit Service Index Scores
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Transit Service Score Assumptions

BART (single line) 0.33 150 trips per day (15-20 minute headways in each 

direction from 4 AM-12 AM)

15-minute bus, 5 AM – 12 AM 0.17

30-minute bus, 5 AM – 7 PM 0.06

Amtrak San Joaquin 0.03 6 trips per day in each direction

Dedicated commute shuttle 0.02 5 trips per commute period (single direction)

As well as existing service, planned and funded transit service would be included in the 

calculation. Purely demand responsive service would not be included.

A maximum trip reduction of 15% is recommended. This is the same as the existing 

URBEMIS 2002 trip reduction for existing and planned transit service.

In order to account for non-motorized access to transit, we also recommend that half the 

reduction be dependent on the pedestrian/bicycle friendliness score (calculated in the 

following section), similar to the approach taken in the existing mitigation component. 

This ensures that places with good pedestrian and bicycle access to transit are rewarded.

Trip reduction = t * 0.075+ t * ped/bike score * 0.075

Where t = transit service index

Bicycle and Pedestrian

Since bicycle mode share and pedestrian mode share depend on similar neighborhood 

characteristics, such as a fine-grained street grid, we recommend that a single factor be 

used to account for both modes. The bicycle and pedestrian components of the URBEMIS 

2002 mitigation component are already well developed. However, the inputs are largely 

subjective, and there is still little evidence to justify the precise amount of credits for 

many of the individual mitigation measures (e.g. street trees). 

Many street design factors have, however, been shown to promote walking and cycling. 

These include:

• Street connectivity, with traditional street networks that are more New Urbanist or 

grid-like, as opposed to the loops, lollipops and cul-de-sacs of most conventional 

subdivision. There are various measures of connectivity (summarized in Dill, 

2003), such as:

o Block length, size or density

o Intersection density

o Street density
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o Connected node ratio (number of street intersections divided by the 

number of intersections plus cul-de-sacs)

o Link-node ratio (links are roadway or pathway segments between two 

nodes, which are intersections or cul-de-sac ends)

o Grid pattern (percentage of intersections that are four- or more way).

o Pedestrian Route Directness (ratio of route distance to straight line 

distance)

o Effective Walking Area (% of parcels within 1/4 mile, that are also within 

1/4 mile walking distance)

• Human-scale streetscapes with adequate pedestrian amenities, access to shopping 

and other amenities, and higher densities (Lund et. al., 2004)

Other relevant research includes:

• A composite indicator, the “Pedestrian Environment Factor,” provides a 

statistically significant correlation with trip generation and VMT. It is comprised 

of four inputs (Parsons Brinkerhoff, 1993):

o Ease of street crossings

o Sidewalk continuity

o Local street characteristics (grid vs. cul de sac)

o Topography

• In Portland, OR, an increase in the PEF from “pedestrian hostile” to “almost 

average” reduces daily vehicle trips by 0.4 per household (7%). An increase from 

“almost average” to “fairly good” provides a daily reduction of 0.2 trips (Parsons 

Brinkerhoff, 1993, cited in Kuzmyak et. al, 2003).

• Sidewalk completeness, route directness and network density together have a 

vehicle trip elasticity of –0.05 (Ewing & Cervero, 2001).

• For a high degree of walkability, block lengths of approximately 300 feet are 

recommended. Short blocks provide more pedestrian crossing opportunities and 

direct walking routes, and mean that traffic is more likely to be dispersed. 

Downtown Los Angeles, for comparison, has about 150 intersections per square 

mile. (Ewing, 1999).

There is a strong tradeoff here between simplicity and low data requirements on the one 

hand, and robustness and accuracy on the other. Pedestrian and bicycle level of service 

work for the Florida Department of Transportation and FHWA, for example, has shown 

that there are numerous statistically significant factors that can be included to assess the 

quality of the bicycle and pedestrian environment. These include motor vehicle volumes 

and speeds, truck volumes, roadway widths, urban design, and lateral separation 

between pedestrians and motor vehicles (for example, FHWA, 1998; Landis et. al, 2001).
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However, we recommend that in order to keep data requirements to a minimum, one or 

two of the street design indicators discussed by Dill (2003) and Ewing and Cervero 

(2001) be used, together with a single bicycle measure. Since route directness and 

network density measure similar characteristics, we recommend the use of one of these 

(network density, which is inversely related to block size) plus sidewalk completeness 

and bicycle network completeness. The pedestrian/bicycle factor would then be 

calculated as follows:

Ped/bike factor =

( network density + sidewalk completeness + bike lane completeness ) / 3

Where:

Network density = intersections per square mile / 1300 (or 1.0, whichever is less)

Note: In most GIS applications, intersections are counted based on the number of line 

segment terminations, or each “valence.” Intersections have a valence of 3 or higher – a 

valence of 3 is a “T” intersection, 4 is a four-way intersection, and so on.4 (Georgia 

Institute of Technology, 2002). Therefore, if intersections are counted manually on a 

map or project plan, care needs to be taken to distinguish between 3-, 4- and 5-way 

intersections, and factor them up accordingly. The 1,300 value roughly equates to a 

dense grid with four-way intersections every 300 feet, per the recommendation of Ewing 

(1999). Intersections with dedicated routes for pedestrians and/or bicyclists should be 

included in this calculation.

Sidewalk completeness = 

% streets with sidewalks on both sides + 0.5 * % streets with sidewalk on one 

side

Bike lane completeness = 

% arterials and collectors with bicycle lanes, or where suitable, direct parallel 

routes exist

A maximum reduction of 9% is proposed, based on the existing URBEMIS mitigation 

component.5 The trip reduction would then be calculated as:

 

4 A valence of 1 indicates that a line segment has terminated, e.g. in a cul-de-sac. A valence of 2 means that the street is 

continuing.

5 Note that this excludes the bicycle reduction in the current mitigation component. However, this compensates for the fact 

that the reductions recommended for the mixed use and density variables will be realized in practice through pedestrian and 

bicycle mode share.
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Trip reduction = 9% * ped/bike factor

No reduction should be allowed if the entire area within a half-mile walk of the project 

center consists of a single use. (Note that this applies to a half-mile walk, rather than 

straight-line distance, to account for barriers such as freeways.) However, the ped/bike 

factor can still be used to calculate pedestrian access to transit, as part of the transit 

mitigation measure.

Affordable and Senior Housing

A significant amount of evidence points to the fact that lower-income households and 

senior citizens own fewer vehicles and drive less. Research includes:

• Russo (2001) cites evidence from the San Francisco Bay Area travel survey, which 

shows that households earning under $25,000 per year make 5.5 vehicle trips per 

day, compared to a regional average of 7.6. High income households (earning 

more than $75,000 per year) make an average of 10.5 trips. Note that this data 

does not control for other factors, such as density and transit access.

• In the San Francisco Bay Area, Los Angeles and Chicago, income was one of four 

variables with sufficient independent explanatory power to include in the model 

of VMT and vehicle ownership (Holtzclaw et. al., 2002).

Obviously, it is difficult if not impossible to account for the exact incomes of residents 

in URBEMIS, most obviously because the occupants are not known at the predevelopment stage. However, the percentage of deed-restricted below-market-rate (BMR) 

housing does offer a way to incorporate this effect.

We recommend a 3% reduction in vehicle trips for each deed-restricted BMR unit.6

Thus, the total reduction is as follows:

Trip reduction = % units that are BMR * 0.04

A development with 20% BMR units would thus gain a 0.8% reduction. A development 

with 100% BMR units would gain a 4% reduction.

 

6 Calculated from Holtzclaw et. al. (2002), assuming 12,000 average annual VMT per vehicle, median per capita income of 

$33,000 (2002 figures per California State Department of Finance), and an average income in BMR units 25% below 

median. Holtzclaw calculate the coefficient of -0.0565. Therefore, expected VMT reduction can be calculated as 0.0565 * 

33,000 * 0.25 / 12,000 = 4%
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Parking Supply

Significant correlations between parking supply and employee mode split have been 

observed. For example, a study of the link between parking availability and transit use in 

eight Canadian downtowns found an extremely high elasticity of –0.77 (Morrall & 

Bolger, 1996, cited in Kuzmyak et. al., 2003b). In California, the number of parking 

spaces per worker was found to be one of the main two elements of a binomial logit 

model predicting transit mode share among TOD office workers (Lund et. al, 2004).

As with residential density, the extent to which parking supply itself is a causal factor is 

uncertain. In practice, it probably serves as a proxy for variables such as price, high 

quality public transit, mix of uses, and pedestrian friendliness (Kuzmyak et. al., 2003b). 

Indeed, in practice there is a two-way relationship between parking supply and mode 

split. Free parking, for example, can be seen as both a cause of high parking supply 

(more parking is needed to satisfy the greater demand), and a consequence (the market 

price of parking is zero once an effectively unlimited supply is provided) (see, for 

example, Shoup, 1999).

Theoretically, it is possible to reduce parking provision to below the level of actual 

demand, should drivers park in neighboring lots or on-street in surrounding areas. 

However, planning approval is not likely to be granted for developments that 

significantly under-provide parking, unless complementary Residential Permit Parking 

programs or other measures to combat this type of overspill are introduced. Indeed, the 

main reason for minimum parking requirements levied by local jurisdictions is to 

address these overspill issues (Shoup, 1999). 

Similarly, market realities are likely to prevent a developer from providing too little 

parking. The challenges in persuading lenders to finance developments that have belowcode parking are difficult enough to overcome, even where there is clear, documented 

evidence to show that parking supply will be enough to meet demand (see for example, 

Parzen & Sigal, 2004). In contrast, the opposite tendency is likely to be apparent – that 

developments are prevented from taking full advantage of the opportunities to reduce 

parking supply by zoning codes (see, for example, Nelson\Nygaard, 2002).

The measure proposed here uses the Institute of Transportation Engineers’ Parking 

Generation handbook as the baseline. This is assumed to equate to unconstrained 

demand. The trip reduction can therefore be calculated as follows:

Trip reduction = Actual parking provision / ITE Parking Generation rate
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Since ITE parking generation rates use the same land use codes as the trip generation 

rates, these could be provided within the URBEMIS model itself. The user would only be 

required to enter the actual parking provision for each land use.

For land uses with rates for both weekday and weekend, the formula will use whichever 

rate is higher. The Parking Generation handbook covers most common land uses. For 

some land uses, however, no parking generation rates are available: in these cases, this 

particular mitigation measure may not be used.7 Those land uses without parking 

generation rates include:

• Single Family Detached Housing

• Mid-rise Apartments

• High-rise Condominium/Townhouse

• Mobile Home Parks

• Residential Planned Unit Development (PUD)

• Day-care center

• Elementary school

• Junior High school

• Library

• City Park

• Discount Superstore

• Discount Club

• Electronic Superstore

• Home Improvement Superstore

• Gas/Service Station

• Pharmacy/Drugstore with and with/out Drive Through

• Medical Office Building

• General Heavy Industry

To avoid double counting with other trip reduction measures, the impacts of parking 

supply are proposed to be assessed in conjunction with all other non-residential trip 

reduction measures as follows:

• The total of all other non-residential trip reduction measures should be used if this 

is greater than or equal to the trip reduction from parking supply measures. For 

example, if parking supply is reduced 10% from ITE levels, and transit, mixed use 

 

7 The next edition of Parking Generation, currently under development by an ITE Task Force, is likely to provide date for 

some of these missing land uses. While it would be ideal to have parking generation data for every single land use before 

introducing this mitigation measure into URBEMIS, the data does not yet exist. Rather than abandoning this mitigation 

measure entirely until perfect data exists, we recommend allowing the measure to be used for the many land uses where 

reasonable data is available.
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and pedestrian/bicycle trip reductions amount to 20%, the 20% figure would be 

used. 

• If the total of all other non-residential trip reduction measures (r1) is less than the 

trip reduction from parking supply measures (r2), the total trip reduction is as 

follows:

r1 + 0.5 * (r2 – r1) 

In effect, the parking supply reduction is only used if it is greater than the impact 

from other trip reduction measures, and the difference is discounted by 50%. For 

example, if parking supply is reduced 25% from ITE levels, and transit, mixed use 

and pedestrian/bicycle credits amount to 15%, the total reduction would be:

15 + 0.5 * (25-15) = 20%. 

This reduction should only be granted if measures to control overspill are in place, such 

as Residential Permit Parking programs, time limits or meters.

Transportation Demand Management

Transportation Demand Management programs have been shown to have a major impact 

on travel behavior. Site-level employee vehicle trip reductions of up to 38% have been 

achieved, particularly for programs that have included parking pricing (Shoup & Willson, 

1980; Comsis, 1993; Valk & Wasch, 1998; Pratt, 2000). Parking price elasticities of –0.1 

to –0.3 have been reported (Pratt, 2000).

This component of the existing URBEMIS 2002 mitigation component is well developed. 

However, there is considerable scope to adapt it in two ways:

• Provide greater emphasis for the three elements that have the greatest impact on 

travel behavior – parking pricing/cash out; free transit passes; and telecommuting. 

• Simplify the remaining elements, through offering broader options such as “major 

program”, “minor program”, and “no program,” for elements that are likely to 

have a smaller trip reduction potential. 

We recommend that none of these reductions be permitted, unless they form part of a 

legally enforceable agreement specifying, for example, minimum parking prices and 

other TDM measures. This might form part of a development agreement, be enforced 

through any TDM ordinance in the local jurisdiction, or consist of another mechanisms 

mutually agreed by the air district and project proponent. Otherwise, there is little to 

guarantee that some of the promised measures (e.g. parking pricing) will actually be 

implemented and maintained.
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Parking Pricing and Cash Out

We recommend that a maximum trip reduction of 25% be applied to projects that 

commit to introducing parking pricing. This is based on the approximate midpoint of 

observed reductions, which range from 15% to 38% (Shoup & Willson, 1990; Comsis, 

1993; Pratt, 2000). Note that most of these studies apply to before-after or with-without 

comparisons, with no increase in transit service or other measures to reduce vehicle 

trips. This maximum reduction should apply to prices of $6 per day or greater (in 2004 

dollars).

The trip reduction will therefore be as follows:

Trip reduction = daily parking charge / 6 * 0.25

If the parking charge is more than $6, the 25% reduction is taken. If parking charges do 

not apply to all trips to a site (e.g. customers are exempt), the reduction is pro-rated by 

the percentage of trips that the charges apply to. If little or no on-site parking is 

provided, the parking charges should be those of surrounding public facilities. 

Parking cash-out programs should be eligible for 50% of the reduction for direct parking 

charges, in recognition of the fact that their impacts tend to be significantly lower (Pratt, 

2000). This is partly due to the fact that cash-out payments are a taxable benefit.

Free Transit Passes

Some California transit agencies, most notably VTA in Santa Clara County, have EcoPass 

or similar programs, whereby employers or property managers bulk-purchase transit 

passes for (free) distribution to their employees or tenants. Eco Pass programs have been 

shown to increase transit ridership by 50-79% (City of Boulder, undated; Caltrans, 2002), 

and reduce vehicle trips by 19% (Shoup, 1999). (Note that many of these new riders 

were making new trips, or ones previously made by walking or cycling.)

We therefore recommend that any project committing to providing free transit passes 

would receive an additional credit equivalent to 25% of the reduction granted for transit 

service. Thus, the credit is more valuable in places that have good transit service. This 

reduction would only apply to the portion of trips generated by those granted the free 

transit passes (e.g. residents and/or employees, but excluding shoppers and other 

visitors).

Telecommuting
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We recommend the retention of the reductions granted for telecommuting and 

compressed work schedules in the existing mitigation component, with two 

clarifications:

• As with the reductions for other mitigation measures, there must be an 

enforceable commitment (e.g. development agreement), which covers both the 

take-up rate (employees actually telecommuting or using compressed work 

schedules) as well as the provision of the option. 

• The percentage reduction should not be additive (in contrast to most other trip 

reduction measures). For example, if 20% of employees telecommute, and other 

trip reduction measures are estimated to reduce vehicle trips from 1,000 to 800 

per day, the 20% reduction would apply to the 800 trips, not the original 1,000.

Other TDM Programs

Other TDM program elements, that do not include financial incentives, tend to have a 

smaller impact on travel behavior. We recommend that reductions be based on the 

number of the following elements incorporated into the program, per Figure 7:

• Secure bicycle parking (at least one space per 20 vehicle parking spaces)

• Showers/changing facilities

• Guaranteed Ride Home

• Car-sharing services

• Information on transportation alternatives, such as bus schedules and bike maps

• Dedicated employee transportation coordinator

• Carpool matching programs

• Preferential carpool/vanpool parking

The impact of a TDM program will also depend on the travel alternatives available. A 

program will have more impact if the site is served by frequent transit, for example 

(although note that a TDM program can do much to promote carpooling even in other 

locations). For this reason, we recommend that part of the TDM credit be used to adjust 

the credits granted for transit service and pedestrian/bicycle friendliness (see Figure 9).

Figure 9 Recommended TDM Program Reductions

Level Number of Elements Recommended Reduction

Major At least 5 elements 2%, plus 10% of the credit for transit and pedestrian/bike friendliness

Minor At least 3 elements 1%, plus 5% of the credit of transit and pedestrian/bike friendliness

No program None None
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Examples

It is important to recognize that any type of calibration is beyond the scope of this 

analysis, which relies on existing references to build on the ranges established in the 

existing mitigation component. Figure 10, however, does provide some examples to 

indicate the trip reductions that would apply to specific places. 

The data are drawn from the database compiled for the Location Efficient Mortgage 

program (for details, see Holtzclaw et. al., 2002), and from the San Francisco Bay Area 

Metropolitan Transportation Commission’s TAZ files. For these reasons, the examples are 

limited to the San Francisco Bay Area. Transit service was estimated from schedules and 

route maps. Sidewalk and bike lane completeness were estimated based on local 

knowledge. For these reasons of limited data, the examples are intended as illustrations 

only, rather than to refer to a particular project.

The reductions are calculated for the physical and environmental factors only, for 

residential uses. They exclude any additional reductions from TDM programs and 

affordable housing.

The final column compares average vehicle miles traveled (no vehicle trip data were 

readily available) in these neighborhoods to the Brentwood baseline, as a rough 

comparison to the reductions granted through the proposed trip reductions for URBEMIS. 

As can be seen, while there are significant discrepancies, the overall correspondence is 

acceptable for this type of sketch planning model.

Figure 10 Example Trip Reductions

Vehicle Trip Reduction Granted For:

Example TAZ

Residential

Density

Mix of

Uses

Local

Retail Transit

Ped/bike

friendliness

Total

Reduction

% Reduction

in VMT from

Brentwood

Brentwood 899 1.4% -3.0% 0.0% 0.1% 1.7% 0.3% 0.0%

Orinda 831 -9.5% 5.8% 0.0% 3.7% 1.4% 1.4% 5.6%

Pleasant Hill BART 806 14.4% 7.2% 3.0% 8.3% 3.3% 36.3% 40.2%

Emeryville 723 39.0% 1.7% 3.0% 4.4% 4.9% 53.1% 47.8%

Downtown Palo Alto 245 19.8% 4.4% 3.0% 6.1% 7.5% 40.8% 50.6%

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