SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 1 

## 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, 2005SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

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Rules 9510 and 3180 December 15, 2005

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- 2 This page intentionally blank.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Rule 9510 and Rule 3180 D

- 3 

## Revised Memorandum

Date: October 20, 2004

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

From: Tim Rimpo

Subject: Recommendatio ns 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 in

cluded 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 M EASURES

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 \ Ny gaard 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 me dium

- 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

t hat 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 h igh 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 4 Jo nes & 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 13 th , 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 wi th the air district to determine the emission reduction associated with a project. That emission reduction would then be entered directly into URBEMIS.

AREA SOURCE MITIGATI ON MEASURES

URBEMIS2002 currently can be used to estimate emissions from five cat egories:

• 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 modul e. Each of these six area source emission categories is discussed separately below.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 5 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 app roach 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 ga s 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, com

mercial, 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 fu el 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 include d, 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 m

easures 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

lan d use that proposes an increase in energy efficiency 10% beyond the Title 24SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 6 requirements would receive a 10% reduction in natural gas combustion emissions compared to uncontrolled emissions.

Title 24 requires that compliance (with Title 24) be demonstr ated 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 na tural gas combustion primarily includes space heating, water heating, and cooking. One comment raised during the May 13 th conference call was whether natural gas used for cooking is properly accounted for in the above mitigation. Data published by the

Cal ifornia 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 mitiga tion 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

emis sion 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 fo ur 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 densit y 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 fol lowing 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

exceedin g 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,SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 7 100% of homes are assumed to have wood stoves. Finally, when the density of homes is 2

pe r 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

h omes 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 withi n 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 8 Wood Stove Emission Factors

The emission factors currently u sed 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 = Pou

nds 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 SO 2

/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 p er 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 fire place 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/f inal/c01s09.pdf Consequently, no changes are proposed to URBEMIS’ fireplace emission rates.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 9 Wood Stoves, Natural Gas Fireplaces, and Wood Fireplaces Mitigation Measures

Currently, URBEMIS2002 has no mitigation measures for woodstoves or wood fireplace s (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 gener ate.

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 equipme nt. 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 es timate 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 SF HU. 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 * SFHUSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 10 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 (p ounds/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

- Residenti al 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 pound s 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 busines s 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 em ission 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 CombustionSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 11 URBEMIS 2002 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 en ter 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 p roducts 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 catego ries, 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 d evelopment. 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 dai ly 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) = 0.0 13 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).SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 12 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 b e 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 b ut 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 t otal 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 t he 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” buildi ng 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 factor s (2.7 for residential, 2.0 for non

-SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 14 References

California Air Resources Board. 2000. Em ission 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 an

d 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.

Cord ero, 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, Res earch Triangle Park, NC.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 15 **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 fo**  **r 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 mitigati on 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 theseSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 16 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. How ever, 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 le vel 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 mi les 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 S an 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 f unction 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 an d complex regional or area

- wide models on the one hand, and more site

specific 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 m itigation 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 uncerta inty. 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 no ted 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, howeve r, 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 colle

ction become a reality in many cities, the ability to identify factors critical to walking,

1 Agencies contacted included: New York Metropolitan Transportation C ouncil; Atlanta Regional Commission; Alameda
County, CA; and San Luis Obispo County, CA.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 17 bicycling, and transit use will be enhanced. The URBEMIS 2002 mitigation component provides a starting point for using currently available data to demonstrate the b enefits 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 re porting 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 appr opriate 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 withi n 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 sou ght 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

result s, 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 tr ip 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 wi th 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

loca l 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 18 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 sele cts a broad land use type (e.g. “High

Rise 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 p roject 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

t ransportation 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 aver age 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

pe destrian 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,

enh anced 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/Townhou se”, 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 19 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”.

Give n 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

s treets 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.



### Recommendatio ns

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 adde d 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 m easures, 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 smal ler, 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 environmenta l factors compared to mitigation measures is largely a function of scale, i.e. development size. LargerSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 20 projects, particularly on greenfield sites, will be starting from a “blank sheet,” and

on

- site mitigation measures will be paramount. The appropriate tr ip 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 u sers. 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, w

hichever 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 M itigation 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 im pact, 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 limi ts on bicycle routes

• Safe routes to schools

• Bicycle parking ordinance

• Transit stop amenities

• Route signs and displays

• Bus turnouts and bulbsSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 21 • Structured parking

4. Modifying Average Trip Generation Rates

In general, both the recommended trip rate modificati ons 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) t he 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 be tween 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 lan d 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 grea ter, 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 extrem es, 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 Are a

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, 2002SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 22SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 23 

### 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 t ransportation 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 wi ll 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 th ey 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 tra nsportation 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 resident ial 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 “pedes trian factor” index based on intersection density, sidewalk completeness, and bike lane completeness)SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 24 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 ty pe 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 n etwork 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

densi ty 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 c ould 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 single

family detached house. To achieve this rate, however, a neighborhood would have to

have remarkable char

acteristics, 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 non

re sidential 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 i ncome residents, which achieve such densities by omitting parking and
providing very small living quarters.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 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 measure s. A maximum additional reduction of 7.75% from the average single

- family house rate is possible through these measures.

No n
- 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 d esign 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 man agement 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 rat es, 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 val ues 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 26 

### Figure 3 Summary of Recommended Trip Reductions

**Residential No** 

**n**

**- 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

Dem and Management sub

total 3 Up to 7.75% Up to 31.65%

3 This sub

- total excepts the measures for parking supply and telecommuting, which have no limit.SAN JOAQUIN VALLEY UNIFIED AIR POLLUTION CONTROL DISTRICT

Appendix D: Recommended Changes to UR BEMIS for Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 27 

### 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 Sidewalks Bike

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 Sidewalks Bike

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

ReductionsSAN JOAQUIN VALLEY UNIFIED AIR POLLUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 318 0 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 28 5. Data Requirements

Figure 5 shows the inputs that are required to complete the mitigation component in full, along with sug gested 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 grant

ed 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

c urrent 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 l ane 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

Telecommuti ng/flexibl

e work schedules Development

agreement or similar N/A

Other TDM programs Development

agreement or similar N/ASAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 29 6. Procedure for Small Projects

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

recommend allowin g 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 sim ilar 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 Method ologies

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 s tandard 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 R educing 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 Fact ors
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 mitigati on 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 elimi nated, 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, rathe r 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 evide nce 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 30 

### Detailed Justification of Recommended Mitigation Level s

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 stud ies. 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 fie lds 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 Apar tment” 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 reg arding 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 31 

## Figure 6 Resident ial Density Vs. Vehicle Travel

0 5000 10000 15000 20000 25000 30000 35000

0 50 100 150 200



### Households/Residential AcreAnnual 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 respec t 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 t hese 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 perSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 32 household, or VMT/Hh) relied on Households per Residential Acre (Hh/RA). For the Los Angeles region, San Francisco and Chicago regions, t hese 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 assume d 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

- (1974 9*((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 re duction 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

h igher 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 densit y, 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 Us es
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. theSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

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- 33 availability of services within walking distance. Key references, relate d 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 resp ect 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

i s 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 develo pments 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 reduc tion = ( 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Rule 9510 and Rule 3180 D

- 34 This formula assumes an “ide al” 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 wit h 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. b us 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 servi ce 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) o f +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

minu tes 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 BaySAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

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- 35 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 v s. Residential Density and Transit Use, San

Francisco Bay Area

0 300 600 900 450 300 150 2

0 5000 10000 15000 20000 25000Annual 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, fir stly 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 tra nsit 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 thi s based on a transit environment factor.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Rule 9510 and Rule 3180 D

- 36 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, Ra pid 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 Den sity, 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 ar

e 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 tr ansit 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

headw ays.)

• 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 fr equencies translate into Transit Service Index scores (note these are additive, if a location has more than one component).



### Figure 8 Example Transit Service Index ScoresSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 37 **Transit Service Score**  **Assumptions** BART (single line) 0.33 150 trips per day (15

- 20 mi nute 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 tr ip 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), simi lar 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 c onventional subdivision. There are various measures of connectivity (summarized in Dill, 2003), such as:

o Block length, size or density

o Intersection density

o Street densitySAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

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Rule 9510 and Rule 3180 D

- 38 o Connected node ratio (number of street intersections divided by the

number of interse ctions 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 rout e 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 dens ities (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, 1 993):

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

– 0.0 5 (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 disperse d. 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 bic ycle 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 i nclude 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).SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 39 However, we recommend that in order to keep data requiremen ts 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 “v alence.” 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 wi th 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 40 Trip reduction = 9% * ped/bike factor

No reduc tion 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/b ike 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 i ncome 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 b ecause the occupants are not known at the pre

development 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 S tate 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%SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 41 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, c ited 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).

Inde ed, 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 s urrounding 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 below

code 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 c ontrast, 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 Gene ration rateSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 42 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

- c are 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 Hea vy 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 t rip 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 Gen eration, currently under development by an ITE Task Force, is likely to provide dat e 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 URBE MIS, 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 43 and pedestrian/bicycle trip reductions amount t o 20%, the 20% figure would be

used.

• If the total of all other non

- residential trip reduction measures (r 1 ) is less than the trip reduction from parking supply measures (r 2 ), the total trip reduction is as

follows:

r 1 + 0.5 * (r

2

– r 1 )

In effect, the p arking 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 (Shou p & 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 considera ble 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 broa der 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 agr

eement 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 dis trict 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.SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 44 **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 befor e

- 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 recognitio n 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; Ca ltrans, 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

wo uld 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**SAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

Rules 9510 and 3180 December 15, 2005

Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 45 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 tr ips 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 alterna tives 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 f or 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 NoneSAN JOAQUIN VALLEY UNIFIED AIR POL LUTION CONTROL DISTRICT

Appendix D: Recommended Changes to URBEMIS for

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Final Draft Staff Report for Proposed

Rule 9510 and Rule 3180 D

- 46 

### Examples

It is important to recognize that any type of calibration is beyond the scope of t his 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 dr

awn 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 th e 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 signif icant 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 Lo** 

**cal**

**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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