Implications of 

T

School 

ravel and Environmental 

Siting 
TRAVEL AND ENVIRONMENTAL IMPLICATIONS OF SCHOOL SITING 

U.S. ENVIRONMENTAL PROTECTION AGENCY 

EPA 231-R-03-004 

OCTOBER 2003 
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

WASHINGTON, D.C. 20460 

Office of Policy, Economics, and 

Innovation 

Dear Reader: 

Over the next few decades, thousands of schools will be built or renovated. Decisions about the 

construction and renovation of these schools will have important implications for their adjoining 

communities. In making these decisions, communities will be challenged to meet multiple community 

goals – educational, environmental, economic and fiscal. 

For some time, there has been a trend toward construction of big schools and requirements for large sites. 

In many places, there is a policy bias in favor of constructing new schools rather than renovating existing 

ones. Requirements, recommendations, and standards that encourage or require building large schools on 

new campuses are embedded in a variety of regulations and laws. Many states have school construction 

funding formulas that favor new construction over renovation; and some state and local governments have 

minimum acreage requirements that dictate the use of large, new campuses, while discouraging the use of 

smaller existing schools. 

Realizing that use of large, new campuses may have adverse consequences for the environment and a 

community’s quality of life, communities are beginning to re-evaluate the impact of existing 

requirements. Recently, South Carolina’s Governor Mark Sanford signed into law a bill that eliminates 

state-mandated acreage requirements for new schools and that makes it easier for existing buildings to be 

renovated. Milwaukee, Wisconsin is using similar flexibility to create its Neighborhood School Initiative 

– constructing six new schools, adding on to nineteen existing schools, and renovating fifteen schools. In 

St. Paul, Minnesota, the newly renovated and expanded John A. Johnson multi-story school building fits 

seamlessly into the community it serves while providing added space for playing fields. 

These changes exemplify a growing trend. Citizens, school administrators, and parents are recognizing 

that schools do more than house children for the day. They affect home-buying decisions and traffic 

patterns. They present opportunities to create neighborhood centers for education and civic life. 

This study provides important information about the effect of school location on how children get to 

school. It shows that school siting and design can affect traffic congestion, air pollution, school 

transportation budgets, and children’s health and obesity. We hope that this study will be a catalyst for 

sound school investment decisions that will lead to the kind of quality schools, neighborhoods, and 

futures we all desire. 

Sincerely, 

Jessica L. Furey 

Associate Administrator 
Travel and Environmental Implications of School Siting 

ACKNOWLEDGEMENTS 

This work was conducted under EPA contract 68-W-99-054 for the Development, Community, and 

Environment Division (DCED) of US EPA’s Office of Policy, Economics, and Innovation. DCED staff 

members Amber Levofsky and Christopher V. Forinash managed this project. For their work conducting 

and documenting this research, EPA thanks Reid Ewing of the University of Maryland, and William 

Greene of New York University. 

EPA thanks the following members of the peer review and study advisory panel who commented on the 

study’s methodology and draft final report. 

• Dr. Robert Cervero, University of California at Berkeley 

• Dr. Larry Frank, University of British Columbia 

• Dr. Susan Handy, University of California at Davis 

• Rodney Sams, ICF Consulting 

To request additional copies of this report, call EPA’s National Service Center for Environmental 

Publications at (513) 891- 6561 and ask for publication number EPA 231-R-03-004. To access this report 

online, visit the Publications section of http://www.epa.gov/smartgrowth. 

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Travel and Environmental Implications of School Siting 

CONTENTS 

EXECUTIVE SUMMARY........................................................................................................... 1

1 INTRODUCTION................................................................................................................. 2

2 TRENDS IN SCHOOL LOCATION .................................................................................. 3

3 FACTORS INFLUENCING MODE OF TRAVEL TO SCHOOL................................... 4

4 MODEL SPECIFICATION AND STRUCTURE .............................................................. 6

4.1 FULLY SPECIFIED MODELS............................................................................................... 6 

4.2 ALTERNATIVE MODEL STRUCTURES ................................................................................ 6 

5 DATA SOURCES AND VARIABLES................................................................................. 9

5.1 TRAVEL DATA .................................................................................................................. 9 

5.2 TRAVEL TIME AND DISTANCE DATA ............................................................................... 10 

5.3 SOCIOECONOMIC DATA .................................................................................................. 11 

5.4 SCHOOL DATA................................................................................................................ 12 

5.5 BUILT ENVIRONMENT DATA........................................................................................... 12 

5.6 DATA SUMMARY ............................................................................................................ 15 

6 MODEL ESTIMATION AND RESULTS......................................................................... 18

6.1 INDIVIDUAL CHOICE SETS.............................................................................................. 18 

6.2 VARIABLE SELECTION.................................................................................................... 18 

6.3 MODEL PARAMETERS..................................................................................................... 19 

6.4 TRAVEL TIME INFLUENCES............................................................................................. 20 

6.5 BUILT ENVIRONMENT INFLUENCES ................................................................................ 21 

6.6 SOCIOECONOMIC INFLUENCES ....................................................................................... 21 

6.7 OMITTED VARIABLES ..................................................................................................... 22 

7 ENVIRONMENTAL IMPACTS ....................................................................................... 22

7.1 EMISSIONS FOR TWO SAMPLED SCHOOLS IN GAINESVILLE............................................ 22 

7.2 SIMULATION OF EMISSIONS FOR NEIGHBORHOOD SCHOOLS .......................................... 24 

8 CONCLUSIONS ................................................................................................................. 25

8.1 SCHOOL LOCATION AND THE BUILT ENVIRONMENT MATTER ........................................... 26 

8.2 MORE RESEARCH WOULD HELP COMMUNITIES BETTER UNDERSTAND WHAT WORKS ...... 26 

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Travel and Environmental Implications of School Siting 

EXECUTIVE SUMMARY 

Over the next few decades, thousands of schools will be built or renovated. The planning decisions 

around that construction and renovation will have important implications for education and community 

quality of life. 

Recent trends in travel to school have raised concerns at federal, state, and local levels. In 2001, less than 

15 percent of students between the ages of five and 15 walked to or from school, and 1 percent biked. In 

1969, 48 percent of students walked or biked to school. In response, governments at every level have 

launched a variety of policy initiatives. These include state and local “Safe Routes to School Programs,” 

and a federal KidsWalk-to-School Campaign. In addition, a variety of initiatives are helping schools stay 

or locate at the center of communities. This study evaluates the travel and environmental implications of 

these latter initiatives, and of trends toward constructing schools that are not located at the center of 

communities, and are not reachable by walking or bicycling. In short, it seeks to find the implications of 

school location for travel and the environment. 

This study is the first to empirically examine the relationship between school location, the built environment 

around schools, mode choices for trips to school, and air emissions impacts of those choices. It finds that: 

1. School proximity to students matters. Students with shorter walk and bike times to or from school are 

more likely to walk and bike. 

2. The built environment influences travel choices. Students traveling through higher-quality 

environments are more likely to bicycle and walk. 

3. Because of travel behavior differences, school location has an impact on air emissions. Centrally 

located schools that can be reached by walking and bicycling reduce air pollution. 

The results suggest that actions to improve students’ walking environments, and to support communities 

that wish to locate schools in neighborhoods, will result in increases in student walking and biking to 

school. Increased walking and biking can reduce emissions related to auto travel and improve 

environmental quality. 

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Travel and Environmental Implications of School Siting 

1 INTRODUCTION 

Communities across the United States will, in the next few decades, need to accommodate substantial 

new student enrollment. Thousands of schools will need to be built or renovated in order to respond to the 

predicted increase in school age children. This expected boom in school construction and renovation, and 

the planning decisions around that construction and renovation, will have important implications for 

education, travel, human health and the environment. 

For instance, to the extent that this construction encourages increases in driving it has the potential to 

exacerbate already serious air quality issues—with health implications for adults and children alike. 

Increased auto travel contributes to unhealthy air. Nationwide, mobile sources emit approximately 

30 percent of the ozone precursors nitrogen oxides and hydrocarbons, but these proportions increase in 

automobile-dependent metropolitan areas. There is overwhelming evidence linking ozone and other air 

pollutants to respiratory ailments in children, including upper respiratory infections and asthma. Almost 

five million children in the U.S. suffer from asthma, causing 14 million lost school days per year.

1

 Over 

the last 25 years, rates of asthma have increased 160 percent in children up to age 4 years and 74 percent 

in children ages 5 to 14 years.2 The traffic generated by auto travel to school exacerbates traffic 

congestion and contributes to the health impacts of auto emissions. The estimate by Gene Benton, city 

traffic engineer for Santa Rosa, California, that the number of cars on the road between 7:15 a.m. and 

8:15 a.m. jumps 30 percent during the school year,

3

 is typical. 

Yet, there is strong evidence that reducing air pollution from automobile use can protect children’s health. 

For example, during the 1996 Atlanta Olympic Games, when driving was reduced and ambient ozone 

levels fell by 27.9 percent, emergency room visits for asthma dropped by 41.6 percent. These results 

suggest that replacing some car trips with walking, biking and transit will reduce vehicle miles and 

associated pollutants. 

However, if future schools follow recent trends, additional schools will likely contribute to the problem. 

Increases in car travel to school are significant. According to the recently released 2001 National Household 

Travel Survey (NHTS), less than 15 percent of students between the ages of five and 15 walked to or from 

school, and 1 percent biked.4

 In 1969, at the time of the first Nationwide Personal Transportation Survey 

(predecessor to NHTS), 48 percent of students walked or biked to school.5 A survey by the Centers for 

Disease Control and Prevention (CDC) found that even children living close to school were not walking or 

biking in large numbers; only 31 percent of children ages five to 15 who lived within a mile of school 

walked or biked.6

 In 1969, the comparable figure was close to 90 percent.7 

1

 Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, 

“Adolescent & School Health”, available at www.cdc.gov/nccdphp/dash/healthtopics/. 

2

 Centers for Disease Control and Prevention, “Surveillance for Asthma—United States, 1960-1995: CDC Surveillance 

Summaries, April 24, 1998,” MMWR Morbidity and Mortality Weekly Report, Vol. 47 (SS-1), 1998, pp. 1-27. 

3 Ann Dubay, “See Dick and Jane Sit in Traffic,” The Press Democrat, September 7, 2003 

4

 Bureau of Transportation Statistics, National Household Travel Survey, NHTS Version 1.0 CD (Preliminary Release), 

January 2003. 

5 This figure applies to students in elementary and intermediate grades, the closest counterparts to the 5-15 age range 

reported for 2001. Federal Highway Administration (FHWA), “Transportation Characteristics of School Children,” 

Report No. 4, Nationwide Personal Transportation Study, Washington, D.C., July 1972, derived from Table 1. 

6 A.M. Dellinger and C.E. Staunton, “Barriers to Children Walking and Bicycling to School—United States, 1999,” 

Morbidity and Mortality Weekly, Vol. 51, No. 32, 2002, pp. 701-704. 

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Travel and Environmental Implications of School Siting 

Why the decline in walking and biking to school? In the CDC survey, parents cited long distances as a 

primary barrier to their children walking or biking to school.8 Yet even the majority of short school trips 

are made by auto or bus, indicating that other factors were also at work. One goal of this study is to shed 

light on what those other factors are. A poor walking environment is linked to auto dependence in the 

general population, and would be expected to discourage walking and cycling to school. “Poor walking 

environment” in this case means a built environment that has low densities, little mixing of land uses, 

long blocks, incomplete sidewalks, and other hallmarks of our current patterns of development.9 

A wide variety of institutions and governments are responding to the decrease in biking and walking to 

school by paying more attention to the factors they believe make a difference, including the quality of the 

walking environment. Numerous states and localities have launched “Safe Routes to School Programs.”10 

With the State of California leading the way, these programs provide funding for sidewalks, bike lanes, 

and other infrastructure improvements to encourage more walking and biking by school children. Some 

also provide funding for police enforcement of traffic laws around schools. The U.S. Department of 

Health and Human Services and CDC have started a Kids-Walk-to-School Campaign, citing rising rates 

of childhood obesity, diabetes, and asthma.11 

In sum, there is both growing attention to improving children’s walking environment, and growing 

interest in the effects of school location on a range of community values, including student’s ability to 

walk to school. However, the literature on the impact of walking environment, school location, and other 

factors that may influence travel choices, remains slim. This study develops a model of travel to school 

choices in an attempt to add to that literature and inform policymaking and community discussions. 

This study is the first to empirically examine the relationship between school mode choice, household 

socioeconomic characteristics, school attributes, and built environment and modal variables. We hope that 

this study adds to the developing literature on school location and design and helps foster discussion 

about appropriate responses to school travel trends. 

Gainesville, FL was chosen as the study area for two reasons: first, the availability of two regional travel 

diary surveys which, combined, offered a relatively large sample of trips to analyze; second, the 

availability of many variables characterizing the built environment in Gainesville, which can be used as 

independent variables in explaining mode choice. 

TRENDS IN SCHOOL LOCATION 

For some time, there has been a trend toward bigger schools and requirements for larger sites. Nationwide 

since World War II, the number of schools declined 70 percent while average size grew fivefold, from 127 

7

 FHWA, op. cit. 

8

 Dellinger and Stauton, op. cit. 

9

 Reid Ewing and Robert Cervero, “Travel and the Built Environment,” Transportation Research Record 1780, 2001, 

pp. 87-114. 

10 Transportation Alternatives, The 2002 Summary of Safe Routes to School Programs in the United States, New York, 

available at www.transact.org/PDFs/sr_2002.pdf. 

11 National Center for Chronic Disease Prevention and Health Promotion, “KidsWalk-to-School”, available at 

www.cdc.gov/nccdphp/dnpa/kidswalk/. 

3

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Travel and Environmental Implications of School Siting 

to 653.12 Many states have school construction funding formulas that favor new construction over 

renovation of existing schools—or which simply do not allow money to be used for renovation. Minimum 

acreage requirements adopted at the state and local level imply that schools not meeting these thresholds 

are inadequate. In fact, schools with high levels of academic achievement exist on both very small and 

very large sites. Communities are beginning to re-evaluate the need for and impacts of “one-size-fits-all” 

acreage standards on education and community quality of life. 

Communities are increasingly concerned about congestion, air pollution, school transportation budgets, and 

children’s health and obesity. More broadly, citizens, school administrators, and parents recognize that 

schools can and do have multiple roles and influences in the community. Schools can anchor 

neighborhoods, and be centers of learning and community. They drive home-buying decisions and traffic 

patterns. School size, location, and the design of the school’s neighborhood affect most of these roles and 

influences. As a result, states and communities have been looking for and finding alternatives to a one-sizefits-all approach. For example: 

• In Milwaukee, Wisconsin the Neighborhood School Initiative is constructing 6 new schools, adding 

on to 19 existing schools, and renovating 15 schools. While adding 750,000 square feet of academic 

space, all schools remain in walkable neighborhoods, and more students can now go to school in 

their own neighborhood. The new school designs encourage community use of library, gym, 

cafeteria, parent center, art and music rooms. 

• In St. Paul, Minnesota, the newly renovated and expanded John A. Johnson Achievement Plus 

Elementary School brings an outstanding academic environment to compact, multi-story building 

that fits seamlessly into the community it serves. Space saved by the traditional multi-story design 

increased the number of playing fields, and additional buildings are planned to accommodate future 

student increases. 

• In July 2003, South Carolina eliminated minimum acreage requirements for schools and allowed 

waivers in school square footage -- two steps that Governor Mark Sanford said will encourage 

more neighborhood schools.

13 

States and communities are already taking action, but there are few quantitative studies to help them 

understand which changes will have the greatest impact. 

FACTORS INFLUENCING MODE OF TRAVEL TO SCHOOL 

A review of the literature on travel to school found only two empirical studies examining the relationship 

between children walking or biking to school and school design and/or location. In the first, Kouri found 

that the percentage of students walking to school was four times higher for schools built before 1983 than 

those built after (an average of 16 percent walking to older schools vs. 4 percent to newer schools).14 

12 Between 1940 and 1990, the number of elementary and secondary schools decreased from 200,000 to 62,000 

despite a 70 percent rise in population. Local Government Commission, “Schools”, available at 

www.lgc.org/transportation/schools.html. 

13 Allison L. Bruce, “In visit to Moultrie Middle, Sanford signs school acreage bill”, Charleston Post and Courier, 

July 17, 2003. 

14 Christopher Kouri, Wait for the Bus: How Low Country School Site Selection and Design Deter Walking to School 

and Contribute to Urban Sprawl, South Carolina Coastal Conservation League, Duke University – Terry Sanford 

Institute of Public Policy, 1999. 

4

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Travel and Environmental Implications of School Siting 

School age is a questionable proxy for the range of factors that distinguish schools in walkable 

neighborhoods from others, so results of this study must be considered suggestive rather than definitive. 

McMillan focused on the effect of urban form on the trip to school, and found that walking or biking to 

school was more likely when a home was within a mile of school.15 Walking or biking was less likely 

when a household had more licensed drivers to provide rides. These were found to be the primary 

influences on school travel mode choice. Also influencing travel behavior, but of secondary importance 

were certain pedestrian-friendly design features such as the presence of street trees, short block lengths, 

and mixed land uses. The limitation of the study to only six school sites meant that there was little 

variance in built environmental conditions across survey respondents, and the significance of these 

variables was accordingly limited. 

Given a limited literature on travel to school specifically, we looked to the substantial general literature on 

travel choice and impacts of land use on travel choice to provide a basis for developing a model of travel 

to school choice. That literature suggested a wide range of variables within the general classes of: 

 Travel time/distance by mode 

 Density and diversity of land uses near the school and on the route to school 

 pedestrian-friendliness of the school and the route to school 

 auto ownership and availability 

 Household income 

With only two quantitative studies to draw on, we also reviewed recent qualitative literature for variables to 

be tested for relationships to school mode choice. Rather than singling out travel to school, this literature 

treated travel to school as one of a variety of outcomes affected by school size and location.16 Small 

neighborhood schools are said to: 

 foster a better learning environment and higher student achievement, 

 promote neighborhood cohesion and pride, 

 discourage sprawl and preserve farmland, 

 lower busing costs and student parking requirements, 

 encourage children to walk or bike to school (with health and other benefits). 

The last of these posited benefits is the subject of this report, and the qualitative literature contains much 

anecdotal evidence that the five classes of variables mentioned above are also relevant to travel to school. 

15 Tracy E. McMillan, “The Influence of Urban Form on a Child’s Trip to School,” paper presented at the Association 

of Collegiate Schools of Planning Annual Conference, Baltimore, 2002. 

16 Kouri, op. cit.; Constance E. Beaumont and Elizabeth G. Pianca, Historic Neighborhood Schools in the Age of 

Sprawl: Why Johnny Can't Walk to School, National Trust for Historic Preservation, Washington, D.C., 2000; Stacy 

Mitchell, “Jack and the Giant School,” The New Rules, Summer 2000; Edward T. McMahon, “School Sprawl,” 

Planning Commissioners Journal, Vol. 39, Summer 2000, pp. 16-18; Sam Passmore, Education and Smart Growth: 

Reversing School Sprawl for Better Schools and Communities, Funders’ Network for Smart Growth and Livable 

Communities, 2002. 

5
Travel and Environmental Implications of School Siting 

From these claims and anecdotes comes a list of variables to test against student mode choice: 

 school size 

 school centeredness within neighborhoods 

 school location within the metro area 

 school proximity to other activity generators 

 pedestrian-friendliness of surroundings 

4 MODEL SPECIFICATION AND STRUCTURE 

4.1 Fully Specified Models 

Transportation modeling usually treats mode choice as an application of consumer choice theory, grounded 

in the notion that people choose among alternatives—such as means of getting to work or brands of ice 

cream—to maximize personal utility, or net benefit to themselves.17 After deciding to go between points A 

and B, people weigh the comparative travel times, costs, and other attributes of competing modes. Traveler 

characteristics (e.g., income) also influence mode selection. These two attribute sets—characteristics of trip 

interchanges and characteristics of travelers—are used by transportation modelers to explain mode choices. 

Travel behavior research by land-use analysts reveals a different approach to the same subject. While the 

effects of income and other traveler characteristics are captured in much the same way as in travel modeling, 

the focus is not on trip interchanges, but rather on trips ends—specifically, the characteristics of origins and 

destinations. Thus, those interested in how traditional neighborhood designs (TNDs) influence mode choices 

concentrate mainly on the densities, land-use mixes, and walking environments at the origin and/or 

destination ends of trips. Too often, how competing modes fare in terms of travel time and cost is ignored. 

Model mis-specification leads analysts to read too much or too little into estimated relationships. 

Statistically, the influences of omitted variables get soaked up by the modeled variables—which means 

transportation modelers end up overstating or understating the importance of travel time and cost, while 

land use researchers end up misinterpreting the importance of the built environment. 

In the case of school trips, the literature suggests that mode choice may additionally depend on school 

location (more or less accessible), school size, and grade level. Hence the utility function in a school 

mode choice model will ideally include: characteristics of trips, travelers, schools, and built environments 

at each end of the trip. 

4.2 Alternative Model Structures 

McFadden developed the multinomial logit (MNL) model to explain choices made among alternatives 

when attributes of the alternatives themselves, and attributes of decision makers, both influence 

outcomes.18 In the choice of travel mode for trips to school, the attributes of alternative modes such as 

travel time, and attributes of students and their households such as income, would be expected to 

17 This discussion draws upon the work of Robert Cervero, “Built Environments and Mode Choice: Toward a 

Normative Framework,” Transportation Research Part D, Vol. 7, 2002, pp. 265-284. 

18 Daniel McFadden, “Econometric Models of Probabilistic Choice,” in Structural Analysis of Discrete Data Using 

Econometric Applications, C. Manski and D. McFadden, eds. MIT Press, Cambridge, MA, 1981. 

6
Travel and Environmental Implications of School Siting 

influence choices. This is a classic application of McFadden’s MNL model. The mode choice model 

assumes the structure in Figure 1. 

Figure 1: Multinomial Logit Structure of Mode Choice 

Car Walk Bike

Mode choice 

Bus 

McFadden extended his discrete choice model to include situations in which certain alternatives share 

important, unobservable qualities. In these cases, the application of MNL violates of one of the basic 

assumptions, called the Independence of Irrelevant Alternatives (IIA), upon which the MNL is built. This 

leads to odd and erroneous predictions of discrete choice probabilities. 

A nested logit model structure overcomes the IIA problem. The structure in Figure 2 has an upper level nest 

with car and non-car modes as available choices, and the lower level nest with school bus, walk, and bike as 

available choices, conditioned on a non-car choice occurring at the higher level. The behavioral assumption 

is the students or their parents first choose between the private passenger car and all other modes, and once 

having chosen the latter, then make the choice among the three other modes. This structure might arise if, 

for example, school bus, walk, and bike modes all raise child security concerns for parents, while the private 

car does not. Other nesting combinations are possible as well, and we tested all combinations.

19 

19 Nested logit models were estimated using the full information maximum likelihood (FIML) method, which makes 

use of all the information at once, instead of the sequential estimation procedure, which estimates the lower nests and 

the upper nest separately. For this reason, FIML gives more efficient estimates compared to the sequential estimation. 

7
Travel and Environmental Implications of School Siting 

Figure 2: Nested Logit Structure of Mode Choice 

Mode Choice 

Bus Walk

Car Other 

Bike 

The mathematical form of the nested logit model is characterized by the appearance in the model of 

“inclusive values”20 in the probabilities of the alternatives. In order for the nested logit model to be 

consistent with an underlying theory of utility maximization, the coefficients of the inclusive values must 

be between zero and one. Models with coefficient estimates outside this range are inconsistent with the 

underlying theory. The most plausible structure considered, the one in the figure above, had a coefficient 

estimate of 1.14 with a standard error of 0.24. This value is not significantly different from one, which 

suggests that the MNL is preferred, especially in the absence of strong evidence of shared unobservable 

attributes. We have thus retained the MNL model as our preferred specification. 

A well-specified multinomial model of school mode choice would take the form: 

Pk = exp(Uk)/[Σl∈K exp(Ul)] 

where Pk is the probability of choosing mode k for a school trip and Uk is the utility function for mode k 

as defined below: 

Uk = αk + βTk

ij + γSE m +θSCn + δBEi +ωBE j + ε k 

Tk

ij and β are trip characteristics and corresponding parameter vectors for trips from i to j by mode k— 

including travel time; 

20 Where school bus, walk, and bike modes share unobservables, the inclusive value in a nested logit model is given by: 

I nonauto = ∑log(e

Ubus + eUwalk + eUbike ) 

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Travel and Environmental Implications of School Siting 

m SE and γ are socioeconomic characteristics and corresponding parameter vectors for traveler m— 

characteristics such as income and automobile ownership; 

n SC and θ are school characteristics such as enrollment and corresponding parameter vectors for 

school n; 

BEi and δ are built environment and corresponding parameter vectors for origin i, with i being a 

neighborhood, census tract, traffic analysis zone, or other small area—the vector may include measures of 

density, land-use mix, walking quality, and site design; 

BE j and ω are built environment and corresponding parameter vectors for destination j; 

and ε k is an extreme-value error vector specific to mode k. 

Given the requisite data, a logit model can be estimated that assigns a probability to person m, traveling 

between origin i and destination j, choosing mode k for the trip to school n. The multinomial logit model 

will capture most of the variables that affect the utility, or benefit, of choosing a particular mode for the 

school trip in question. 

5 DATA SOURCES AND VARIABLES 

5.1 Travel Data 

Two travel diary surveys were conducted at approximately the same time in Alachua County, Florida, 

home of Gainesville and the University of Florida. The first was a survey during the first half of 2001 

under the auspices of the Gainesville Metropolitan Transportation Planning Organization (MTPO). It was 

a standard travel survey, beginning with telephone interviews to screen and recruit households, followed 

by a mail-in travel survey with demographic questions and travel diaries, and concluding with inputting, 

geocoding, and editing of survey responses. 

A second survey was conducted in the last half of 2000 by the Florida Department of Transportation 

(FDOT). This survey involved a much larger sample and had a much higher response rate than the first. It 

too involved a screener survey to recruit participants, and a mail-in travel diary survey with demographic 

questions. There was enough overlap in study area and survey content to permit the two surveys to be 

combined, thereby yielding a larger sample of usable responses. 

Table 1 summarizes sample data for the two surveys. A total of 15,980 trips were reported, from which 

school trips could be extracted and mode choices analyzed. 

Table 1. Sample Data for the Two Gainesville Surveys 

MPTO Survey FDOT Survey 

Households 374 1,766 

Persons 726 3,089 

Trips 3,073 12,907 

In both surveys, we wanted to identify all K-12 school trips, and only K-12 school trips. The FDOT 

survey includes two variables that should have made it easy to do so. One variable classifies respondents 

9
Travel and Environmental Implications of School Siting 

by main activity, one of which is “student.” The other classifies trips by main purpose, one of which is 

“school” trip. If all personal and travel data were reported correctly, identifying school trips would be a 

simple matter of selecting trips by students to or from school. 

Several factors complicated the identification of school trips: 

(1) Households did not always report trips by children. While the travel diary instructions called on 

participating households to report all trips, a surprising number simply disregarded children’s trips. We 

added “phantom” trips to the database where adult members of households made trips to or from school 

and three other conditions were met: the household had members under 16 years; the adult was 

accompanied on the trip by other people, and the number traveling together dropped on the next leg of the 

adult’s trip, implying a drop off at school (or increased following the school trip, implying a pickup). 

(2) Respondents did not always identify their main activity. If someone in the first two age categories 

(under 16 years) made a trip to or from school, he or she was assumed to be a student making a school 

trip whether student status was properly reported or not. 

(3) Students making school trips were not always in the K-12 age range, not surprising for a university 

town. Trips were dropped from the database if travelers began or ended their trips in a traffic analysis 

zone (TAZ) belonging to an institution of higher learning. About half of all school trips were to or from 

TAZs on the University of Florida campus. Other institutions with trips that had to be dropped from the 

sample were Santa Fe Community College and City College. 

The MPTO survey had more information on trips and travelers than the FDOT survey, but identifying 

school trips was still not easy. One variable identified “students”; another listed respondents' main 

activity, of which “other school” (non-college) was one alternative; a third variable listed trip purposes, 

two of which were relevant to school trips (school trips and pickup/drop off trips); and fourth and fifth 

variables listed trips’ start place and end place, some of which were named schools. 

Several factors complicated the identification of school trips: 

(1) Households did not always report trips by children. While the travel diary instructions called on 

participating households to report all trips by persons 5 years or older, many disregarded children’s trips. 

“Phantom” trips were added to the database where adult members of households had a pickup/dropoff as 

their trip purpose, traveled with at least one other person, made trips to or from a named school, but didn’t 

include student records of trips. 

(2) Respondents did not always list themselves as students, even when they clearly were. If both of the 

following conditions were met, respondents were reclassified as students and trips were classified as 

school trips: the trip was made by someone whose main activity was “other school” and whose origin or 

destination was a K-12 school. 

(3) Students making school trips were not always in the K-12 age range. Trips were dropped from the 

database if travelers’ start or end place was an institution of higher learning. 

5.2 Travel Time and Distance Data 

Interzonal travel times were obtained from the Gainesville regional travel model, a conventional four-step 

travel demand model. The model generates travel time “skims,” or minimum travel times from zone to 

zone, as inputs to trip distribution. Skims were available for all travel modes modeled in the four-step 

10
Travel and Environmental Implications of School Siting 

process, which in this case meant automobiles and transit. They were not available for walking or 

bicycling since these modes are not modeled in Gainesville. 

To estimate walk and bicycle travel times between (and within) zones, we extracted minimum path 

distances from zone-to-zone highway skims, and applied nominal speeds of travel to each. Typical 

walking speed of children was assumed to be 3 mph, while typical biking speed was set at 12 mph. 

School bus travel times could only have been determined with great effort. We would have needed to 

know the distance from home to pick up point, routing from pickup point to school, routing from school 

to drop off point, and distance from drop off point to home. We would have also needed to know the 

number of school bus stops along the way and, from that, average running speed for each route. It is not 

even clear that the choice of school bus as a mode of travel is sensitive to travel time, given other 

considerations such as parental convenience and service availability. 

This gave us the following set of travel time variables: 

 estimated automobile drive time between zones by minimum path 

 estimated walk time between zones by minimum path 

 estimated bicycling time between zones by minimum path 

We would assume that the longer the travel times by walking and biking relative to the automobile, the 

lower the utility of these modes. As for school bus travel, we would assume that beyond the threshold 

distance from home to school, where bus service becomes available to students, the utility of school bus 

travel is independent of travel time. This hypothesis was not testable with the current dataset, and a literature 

search uncovered no evidence one way or another on this point. This became an issue for future research. 

5.3 Socioeconomic Data 

As this study drew on two different surveys, only where equivalent questions were asked in both surveys 

could data be used. The FDOT survey, for example, asked about bicycle ownership, while the MPO 

survey asked about rainfall on the date of travel. These variables could not be used to explain school 

mode choices for lack of complete datasets.21 

Data that overlapped between the two surveys were: 

 number of household members 

 number of household motor vehicles 

 number of vehicles per household member 

 annual household income 

 driver’s license owned by student, 1 if yes, 0 otherwise 

The number of vehicles per household member was the only measure of vehicle supply relative to 

demand available for both surveys. Vehicle availability would have been better represented by vehicles 

per driver or vehicles per driving age household member. 

21 Though weather conditions generally could have been determined for the FDOT date of travel from historical 

weather reports, they would not necessarily apply to a particular time and place of travel within the Gainesville area. 

11
Travel and Environmental Implications of School Siting 

Regarding the driver's license variable, this was available for all reported student trips from both surveys. 

However, for unreported “phantom” trips, we had to assume that the students involved were without 

drivers’ licenses. 

The utility of walking, biking, and school bus riding was expected to decline with vehicle availability, 

possession of a driver’s license, and perhaps with household income. 

5.4 School Data 

School enrollment data for public schools were obtained from the Alachua County School District. For 

private schools, it was necessary to contact schools individually. Schools were located by TAZ from their 

addresses with the help of MAPQUEST and a Gainesville TAZ map. 

For FDOT survey respondents, matching school trips to specific schools proved tricky. Rather than the 

usual travel diary method of asking respondents for the addresses of destinations or closest cross streets, 

and geocoding the results, this survey provided respondents with a TAZ map of Alachua County and 

asked the respondents themselves to identify the TAZs of origin and destination. For about half of all 

school trips, respondents chose TAZs with schools in them, and the match was obvious. For the other half 

of trips, the generalized nature of the map left respondents with only a generalized idea of where trips 

began or ended. They were often off by a TAZ or two from the closest school location. Matches were 

made in these cases based on closeness of the TAZ to the one reported by the respondent, and based on 

the grade level of the respondent which corresponded to the grade levels of the school. 

For the MTPO survey, matching school trips to schools was pro forma, since respondents nearly always 

provided the names of places where trips started and ended. Schools were identified by name, and were 

already located within known TAZs. 

Two data elements were included in the data set: 

 school enrollment level 

 high school, 1 if yes, 0 otherwise 

The utility of walking and biking was expected to decline with enrollment, as schools would be drawing 

from larger areas. Whether this variable would be significant after controlling for travel time to and from 

school was debatable. It is certainly possible that school size would have an additional negative effect on 

walking and bicycling due to, for example, the tendency for larger schools to be placed in remote 

locations, and thus inaccessible by walking and bicycling. On the other hand, older large high schools 

located in or near town centers may have considerable residential density proximate to the school despite 

large enrollment. 

5.5 Built Environment Data 

The final set of variables related to the built environment around the school and home or other trip end. 

Many land use-travel studies have represented the built environment in sophisticated, multidimensional 

ways.

22 However, to our knowledge, the Gainesville database characterizes the built environment more 

completely than any to date, quantifying more qualities of the built environment. 

22 See the literature survey by Ewing and Cervero, op. cit. 

12
Travel and Environmental Implications of School Siting 

Data on the built environment were available from multiple sources. All variables were estimated for 

TAZs in the Gainesville metropolitan area. A subset of available variables was tested, those that held the 

most promise of explaining walk and bike trips. 

From socioeconomic input data files for the Florida Standard Urban Transportation Model Structure 

(FSUTMS), Gainesville’s conventional four-step model, came three data elements: 

 overall density = (residents + jobs)/area 

which measures the overall density of a TAZ in terms of people either living or working within 

the TAZ. The use of a combined measure of density is desirable when the amount of land devoted 

to individual uses is unknown, as in Gainesville TAZs. 

 jobs-residents balance = 1 – [ABS (jobs - a*residents)/(jobs + a*residents)] 

which measures the degree of land-use balance between jobs and residents at the TAZ level. ABS 

is the absolute value of the expression in parentheses. a is the regional ratio of jobs to residents. 

This variable assumes a value of 1 when a TAZ has the same ratio of jobs to residents as the 

region as a whole and a value of 0 when a TAZ has only jobs or residents, not both. It assumes 

intermediate values when TAZs have both jobs and residents, but one predominates. 

 job mix = -[commercial jobs * LN (commercial jobs) + industrial jobs * LN (industrial jobs) + 

service jobs * LN (service jobs)] / LN (3) 

which measures the degree of land-use mixing at the TAZ level. LN is the natural logarithm of 

the expression in parentheses. Values of job mix range from zero when all jobs in a given TAZ are 

concentrated in one sector, to 1 when jobs are evenly divided among the three employment 

sectors represented in the FSUTMS database. This functional form is commonly known as an 

entropy variable. 

From the Property Appraiser’s database (parcel layer in the county’s geographic information system) 

came the following land-use intensity variable for commercial properties: 

 commercial FAR = commercial floor area / (43,560*commercial land area) 

The constant 43,560 converts acres of land into square feet which, when divided into square feet of floor 

area, yields a floor area ratio. Only pedestrian-oriented commercial uses were included in the calculation, 

specifically, retail uses; finance, insurance, and real estate offices; general office buildings; and 

commercial lodging. 

From the county’s bicycle and pedestrian LOS (level-of-service) database came the following data 

elements. 

 proportion of street miles with bike lanes or paved shoulders 

 proportion of street miles with sidewalks 

 average sidewalk width 

These variables were available only for arterial and collector streets. 

13
Travel and Environmental Implications of School Siting 

From the county’s geographic information system (GIS) came: 

 street density = centerline street miles per square mile 

This variable measures street network density, including local streets as well as arterials and collectors. 

Characterizing a TAZ’s location within the larger region are regional accessibility indices. Conventional 

four-step models such as Gainesville’s automatically generate regional accessibility indices as inputs to trip 

distribution. Regional accessibility indices, which appear as the denominator of a conventional gravity 

model, are computed by multiplying the number of trip attractions for each attracting zone by a “friction 

factor” inversely related to travel time from the trip producing zone to the attracting zone, summed over all 

attracting zones. The more attractions nearby, the higher the accessibility index of a producing zone. 

accessibilityip = ∑j∈J attractionsjp*friction factorijp 

where accessibilityip is the accessibility index of zone i for trip purpose p, attractionsjp is the number of trip 

attractions in zone j for the particular trip purpose, and friction factorijp is the interzonal friction factor for 

trips from zone i to zone j, again, for said trip purpose. Accessibility indices are available for five primary 

trip purposes in the Gainesville model, two of which are based on broad measures of trip attraction: 

 accessibility index for home-based other trips (which includes school trips) 

 accessibility index for non-home-based trips 

Both accessibility indices were normalized on a scale of 0 to 1 by dividing absolute values by the highest 

value for the entire urbanized area. 

Many variables characterizing the built environment are highly correlated with one another. The problem of 

multicollinearity was addressed by running principal components analysis on the full set of variables, and 

then running it again on the five variables that loaded most heavily on the first principal component. In the 

second run, the principal component that captured the largest proportion of the common variance among the 

five variables became our overall measure of “pedestrian-friendliness.” That this component represents 

pedestrian friendliness is apparent from the factor loadings on this component (in Table 2). It is highly 

correlated with overall density, commercial FAR, sidewalk coverage, sidewalk width, and street network 

density. The pedestrian-friendliness factor, a weighted combination of the five variables, explains 60 percent 

of the total variance the dataset, equivalent of three of the underlying variables combined. Because this 

component captures the majority of the combined variance, no additional components were considered. 

Table 2. Loadings on the Pedestrian-Friendliness Factor 

Variable Loading 

Overall density .719 

Commercial FAR .604 

Sidewalk coverage .839 

Average sidewalk width .825 

Street network density .851 

Eigenvalue 2.99 

Variance explained 60% 

From the land use-travel literature, we would expect the utility of walking, and perhaps bicycling, to 

increase with virtually all of the built environment variables defined in this section. 

14
Travel and Environmental Implications of School Siting 

5.6 Data Summary 

The original dataset contained 819 K-12 school trips for which origin and destination TAZs were known. 

Four cases were dropped because the mode of travel was transit bus, another seven because the mode was 

“other.” Samples were too small to model these mode choices separately. Two cases were missing school 

enrollment data. Ten cases were missing household size and vehicle ownership data, resulting in 

undefined per capita vehicle ownership. These cases were dropped to maintain a full complement of 

independent variables for subsequent analysis. 

The greatest loss of cases was due to unknown household income. As is often the case in travel surveys, 

household income went unreported by a large number of respondents. We could have maintained the sample 

size at 792 observations by excluding household income, but instead opted for a smaller sample and more 

complete set of variables. From a theoretical perspective, household income was too important to be omitted 

from our mode choice analysis. From a practical standpoint, the independence of household income from 

other explanatory variables including vehicle ownership per capita (r=0.11) meant that household income 

was bringing something unique to the analysis. Eighty-one cases had to be dropped for lack of income data, 

but only three cases were in the underrepresented categories of walking and biking. 

Two additional cases were lost when walk and bike modes were removed from all choice sets in which 

estimated travel times by these modes exceeded one hour (see below for a discussion of restricted choice 

sets). In these two anomalous cases, the bike mode was chosen even though estimated interzonal travel time 

by bike exceeded one hour. In all other cases, when either walk or bike was chosen, estimated travel times 

by these modes were less than an hour. 

Complete datasets, including all variables defined previously, were available for the remaining 709 school 

trips. We checked for the possibility that systematic bias had been introduced by dropping cases. Comparing 

the mean values of variables contained in the original and reduced samples indicated no such bias. 

Table 3 reports mode of travel for the final sample of school trips. Table 4 lists the independent variables 

in the dataset with their descriptive statistics. Figure 3 shows the built environments of two Gainesville 

high schools with contrasting mode splits. 

Table 3. Travel Modes for School Trips from Gainesville Surveys 

Mode Count 

Car 

School Bus 105 

Walk 

Bike 

Total 

548 

32 

24 

709 

15
Travel and Environmental Implications of School Siting 

Table 4. Descriptive Statistics for Independent Variables in the Database (n=709) 

Variable Descriptive Statistics 

Mean S.D. Min Max 

Travel distance by minimum path 4.82 4.49 0.15 24.4 

Travel time for the trip by automobile 11.78 7.99 2.5 48 

Travel time for the trip by walking* 29.1 17.3 3 60 

Travel time for the trip by biking* 19.3 14.7 0.75 58.5 

Number of household members 4.08 1.23 1 9 

Number of household vehicles 2.45 0.99 0 8 

Number of vehicles per household member 0.631 0.271 0.2 2.67 

Household income 62,221 31,444 12,500 130,000 

Driver’s license indicator (1 if student owns a license, 0 otherwise) 0.228 0.420 0 1 

School enrollment 895.8 750.2 71 2549 

High school indicator (1 if the school is a high school, 0 otherwise) 0.231 0.422 0 1 

Overall density in the school TAZ 1905.3 1683.5 21.5 7569.2 

Overall density in the other TAZ 2208.5 3550.2 0 42796.3 

Job-resident balance in the school TAZ 0.458 0.284 0 0.950 

Job-resident balance in the other TAZ 0.353 0.295 0 0.998 

Job mix in the school TAZ 0.499 0.388 0 0.993 

Job mix in the other TAZ 0.536 0.361 0 0.998 

Commercial FAR in the school TAZ 0.138 0.216 0 1.277 

Commercial FAR in the other TAZ 0.165 0.275 0 1.629 

Proportion of street miles with bike lanes or paved shoulders in 

the school TAZ 0.380 0.364 0 1 

Proportion of street miles with bike lanes or paved shoulders in 

the other TAZ 0.372 0.362 0 1 

Proportion of street miles with sidewalks in the school TAZ 0.743 0.345 0 1 

Proportion of street miles with sidewalks in the other TAZ 0.582 0.391 0 1 

Average sidewalk width in the school TAZ 4.49 1.30 0 7.00 

Average sidewalk width in the other TAZ 4.03 1.83 0 7.17 

Street network density in the school TAZ 10.11 4.49 2.69 21.24 

Street network density in the other TAZ 9.69 5.28 0.63 34.97 

Accessibility index for home-based other trips in the school TAZ 0.339 0.303 0.0007 1.000 

Accessibility index for home-based other trips in the other TAZ 0.275 0.280 0.0003 1.000 

Accessibility index for non-home-based trips in the school TAZ 0.485 0.283 0.0068 1.000 

Accessibility index for non-home-based trips in the other TAZ 0.437 0.327 0.0011 1.000 

Pedestrian-friendliness factor for the school TAZ, calculated as 

the weighted sum of overall density, commercial floor area ratio, 

sidewalk coverage, sidewalk width and street density 

0.097 0.394 -1.122 0.653 

Pedestrian-friendliness factor for the other TAZ, calculated as 

above -0.050 0.536 -1.183 1.828 

* Restricted to cases in which estimated travel time is 60 minutes or less. 

16
Travel and Environmental Implications of School Siting 

Figure 3: Sampled Schools-Gainesville High School and Eastside High School (same scale) 

Sampled Trips Sampled Trips

Auto trips 38 (85%) Auto trips 19 (100%) 

Walk trips 6 (13%) 

Bike trips 1 ( 2%) 

Average auto trip length: 4.24 miles Average auto trip length: 8.42 miles 

17
Travel and Environmental Implications of School Siting 

18 

6 MODEL ESTIMATION AND RESULTS

All multinomial logit and nested logit mode choice models were estimated using full information 

maximum likelihood and the LIMDEP/NLOGIT software. The “universal choice set” for the student 

population studied included four travel modes: automobile, school bus, walking, and biking. 

6.1 Individual Choice Sets 

Practically speaking, certain modes were unavailable to certain students, and their choice sets had to be 

restricted. For school trips in our sample, estimated walk times ranged up to 488 minutes, while estimated 

bike times were as high as 122 minutes. No student could be expected to walk or bike this far. Therefore, 

a cutoff value of 60 minutes was established for travel times by these modes. Walk and bike modes were 

removed from the choice sets for trips having travel times in excess of the cutoff value. Literally hundreds 

of school trips in our sample were restricted to two or three modes. Yet, nearly all of these trips were by 

auto or school bus anyway, so removing walk and/or bike modes from the choice sets did not deplete the 

sample appreciably. The model was estimated with these choices eliminated from the available choice set 

for these individuals.23

The opposite situation applied to school bus trips. To qualify for school bus service, students in the 

Alachua County School District generally must live two or more miles from school. Accordingly, the 

school bus mode was initially removed from the choice sets for school trips of less than two miles. 

However, this restriction was later lifted due to the large number of school bus trips lost to the sample. A 

review of the School District’s policy indicated that exceptions to the minimum distance rule are made 

when a student faces hazardous walking conditions, qualifies for “courtesy” busing by virtue living along 

a bus route, or for various other reasons. 

6.2 Variable Selection 

The automobile was treated as the base mode. The utilities of other modes were modeled relative to the 

automobile. Having selected the automobile as the base mode, the next decision was whether to include 

estimated auto time between zones as the sole variable in the automobile utility function, as is sometimes 

done in mode choice modeling; or alternatively, to set automobile utility equal to zero and add variables to 

other equations to achieve a similar fit. No model raised the significance of auto travel time to the 

conventional .05 level (though some came close). For this reason, and to simplify interpretation, automobile 

utility was zeroed out in our final model. 

Travel time estimates were included in the utility functions of walk and bike modes. As there was no reason 

to assume that time spent walking and biking would have the same disutility, travel time coefficients were 

estimated independently for the walk and bike modes. Travel time was left out of the school bus utility 

function, for lack of any credible estimate of travel time by that mode. 

All plausible combinations of socioeconomic, school, and built environment variables were tested as 

explanatory variables in the utility functions of the walk, bike, and school bus modes. Variables were 

retained only if they proved significant at the .05 probability level. 

 

23 Only two bike trips exceeded the cutoff value, and were lost to the sample. No walk trips exceeded the cutoff value, 

and none were lost to the sample. 
Travel and Environmental Implications of School Siting 

19 

6.3 Model Parameters 

The best-fit model is presented in Tables 5 through 7. These tables present the same basic information in 

different forms. In Table 5, coefficient values and t-statistics indicate the effects of independent variables 

on mode choice probabilities. The convergence of the MNL model was found to be satisfactory. The log 

likelihood at convergence was –425, and the log likelihood with constants only in the utility function was 

–494. The pseudo-R square of the model was thus [1 – (-425/-494)] or 0.14 relative to the model with 

only constants. 

In Table 6, the marginal effects of independent variables on mode choice probabilities are expressed as 

elasticities, that is, as percentage changes in probabilities associated with a one percent change in the 

independent variables. Elasticities are commonly used in travel research to summarize relationships 

between travel outcomes and explanatory variables. The values presented are point elasticities at the mean 

values of the independent variables. 

In Table 7, the results of simulations are presented. In each simulation, values of one independent variable 

were lowered by 25 percent across the board. Given the new values of the one variable, new probabilities 

of mode choice were computed. The mode choices for the sample are computed by summing the 

probabilities and multiplying by the number of individuals. The difference between the original mode 

shares, and the simulated mode shares, represents the effect of the change in this one variable. 

Table 5: Multinomial Logit Model Parameters for School Bus, Walk, and Bike Modes, with Auto as Base 

Mode 

Bus Walk Bike Variable 

Coeff. t-stat Coeff. t-stat Coeff t-stat 

Constant -1.054 -6.44 2.385 2.40 -1.301 -3.87 

Annual household income (in 

thousand dollars) -0.0334 -3.33 

Per capita household auto ownership -4.570 -3.61 

License ownership indicator (1 if the 

individual holds a drivers license, 0 

otherwise) 

-2.513 -4.23 

Walk time for the trip (minutes) -0.0527 -3.98 

Bike time for the trip (in minutes) -0.1504 -4.07 

Average sidewalk coverage for 

origin and destination TAZs 1.480 2.09 

Average home-based other 

accessibilities for origin and 

destination TAZs 

-1.130 -2.37 

Restricted log-likelihood -982.9 

Log-likelihood with constants only -493.9 

Log-likelihood at convergence -425.4 

pseudo-R2 0.139 

Number of observations 709 

 
Travel and Environmental Implications of School Siting 

20 

Table 6: Point Elasticity Estimates from the Multinomial Logit Model 

Variable Bus Walk Bike 

Annual household income (in thousand 

dollars) -0.84 

Per capita auto ownership for the household -1.16 

License ownership indicator (1 if the 

individual holds a drivers license, 0 

otherwise) 

-0.91 

Walk time for the trip (in minutes) -0.66 

Bike time for the trip (in minutes) -2.63 

Average sidewalk coverage for origin and 

destination TAZs 0.42 

Average home-based other accessibilities for 

origin and destination TAZs -0.31 

Table 7: Base Mode Share/Simulated Mode Share with a 25 Percent Change in Each Independent 

Variable 

Variable Change Car Bus Walk Bike 

Annual household income 

(in thousand dollars) -25% 77.3/76.2 14.8/14.6 4.5/5.9 3.4/3.2 

Per capita auto ownership for 

the household -25% 77.3/75.8 14.8/14.5 4.5/6.5 3.4/3.2 

Walk time for the trip (in 

minutes) -25% 77.3/76.5 14.8/14.7 4.5/5.5 3.4/3.3 

Bike time for the trip (in 

minutes) -25% 77.3/76.5 14.8/14.7 4.5/4.5 3.4/4.4 

Average sidewalk coverage 

for origin and destination 

TAZs 

-25% 77.3/77.9 14.8/14.9 4.5/3.7 3.4/3.5 

Average home-based other 

accessibilities for origin and 

destination TAZs 

-25% 77.3/76.5 14.8/15.7 4.5/4.5 3.4/3.4 

Values may not sum to 100 percent due to rounding. 

6.4 Travel Time Influences 

As expected, students with shorter walk and bike times to or from school were more likely to walk and 

bike, respectively. The probability of biking was particularly sensitive to travel time for reasons that are 

unclear; an elasticity value of –2.63 means that students are averse to even small increases in travel time 

by bike. The probability of walking was less sensitive to travel time, but still significantly affected by it. 

In the simulations, a 25 percent across-the-board reduction in walk time (to a mean of 21.8 minutes) 

resulted in a 1.0 percent increase in walk mode share, from 4.5 to 5.5 percent of school trips in our 
Travel and Environmental Implications of School Siting 

21 

sample. A 25 percent reduction in bike time also resulted in a 1.0 percent increase in bike mode share, 

from 3.4 to 4.4 percent of school trips. Put another way, if the average walk time falls 25% (in distance 

terms, from 1.5 miles to 1.1 miles), for every 100 students, one more student walks to school. 

These increases sound small, but from small bases they represent large percentage increases: from 3.4 to 

4.4 percent is a 29 percent increase in bike mode share. Results indicate that students must live close to 

school before they are likely walk or bike in more significant numbers. How large a school can be and 

still allow significant walking and biking depends in part on the density of the neighborhoods from which 

it draws, of course, but in general this result suggests that walking and biking will be much more 

prevalent at smaller neighborhood schools than at large schools that draw from larger areas. 

6.5 Built Environment Influences 

Of the many built environment variables, the proportion of arterials and collectors with sidewalks along 

them proved to be the most significant influence on walking. Values of sidewalk coverage for origin and 

destination zones were highly correlated for walk trips, precluding the use of both variables in the same 

utility function. Instead, values of sidewalk coverage for the origin and destination zones were averaged, 

and the average was then used as an explanatory variable. The probability of walking to school had an 

elasticity of 0.42 with respect to average sidewalk coverage. In the simulation, a 25 percent across-theboard reduction in sidewalk coverage resulted in a 0.8 percent decrease in walk mode share, from 4.5 to 

3.7 percent of trips. 

Interestingly, the built environment did not have a significant effect on bicycling. Even the proportion of 

arterials and collectors with bike lanes or paved shoulders along them proved insignificant. The arterials 

and collectors with paved shoulders tend to be in less developed areas, so this particular variable may not 

reflect the general bicycle-friendliness of the area. 

One built environment variable—regional accessibility for home-based other trips—was related to school 

bus use. The more accessible the location, the less attractive the school bus relative to other modes, 

including the automobile. School buses may be serving as a mode of last resort for parents, chosen when 

parents cannot provide rides themselves due to excessive distances between home and school. As with 

sidewalk coverage, home-based other accessibilities were correlated for origin and destination zones, and 

therefore were averaged to create a single variable that reflected conditions at both origin and destination. 

The probability of taking a school bus had an elasticity of –0.31 with respect to average regional 

accessibility. In the simulation, a 25 percent across-the-board reduction in regional accessibility resulted 

in a 0.9 percent increase in school bus mode share, from 14.8 to 15.7 percent of sampled school trips. 

6.6 Socioeconomic Influences 

Students from households with higher incomes and more vehicles per capita were less likely to walk to 

school compared to taking a car, school bus, or bicycle. The probability of walking was most strongly 

related to vehicles per capita; its elasticity was –1.16. Less strongly related was household income, with 

an elasticity of –0.84. It is obvious why greater vehicle availability would make walking less attractive 

relative to car travel. It is less obvious why greater vehicle availability would make walking less attractive 

relative to other modes, or why higher income would have this effect independent of vehicle availability. 

These two variables individually and together may have a strong enough influence on mode choice to 

overwhelm other factors favoring walk trips, such as a short distance to and from school. 

Students holding drivers’ licenses were less likely to take a school bus than those without drivers’ 

licenses. This makes perfect sense. Students living too far from school to walk or bike are prime 
Travel and Environmental Implications of School Siting 

22 

candidates for school bus service until they reach driving age, at which time they became prime 

candidates for driving themselves if their families’ financial situation permits it. 

6.7 Omitted Variables 

Notably absent from the utility functions of different modes were school variables. Enrollment did not 

prove significant after controlling for travel time between home and school. Larger schools may draw 

students from larger areas, increasing travel time and thereby indirectly affect mode choices. But school 

size does not appear to have a direct effect on mode choices. 

Also absent from the utility functions were land use variables such as density and mix. The travel behavior 

literature emphasizes the importance of such variables in travel decision-making. It is not clear from this 

study whether school trips are different. On the one hand, they tend to be unlinked to other activities, thus 

reducing the need for proximity to other land uses. They are mandatory, which may render the walking 

environment less important than with discretionary travel. And they involve children, who may be less 

sensitive to walking conditions than are their adult counterparts. On the other hand, density and use mix 

affect travel choices in large measure by bringing destinations closer together and thus reducing travel time. 

As with school size, it may be that after controlling for travel time, any additional impacts of use mix and 

density were not strong enough to be modeled given the data limitations in this case. 

7 ENVIRONMENTAL IMPACTS

Travel behavior has important impacts on environmental quality, especially air quality, so as a final step 

we estimated the air emissions impacts of differences in school locations and resulting travel choices. 

An analysis of emissions impacts must ask “compared to what?” Typical approaches to this question 

include a “build/no-build” comparison and “build it here or build it there” comparison, neither of which is 

strictly relevant in this case. Nationally, schools will either be built or renovated, so there is no no-build 

scenario, although there are certainly build/renovate scenarios in individual places. Similarly, although 

there are and will be many individual cases of “build it here or build it there” for school districts across 

the country, conducting such a case study is beyond the scope of this paper. Rather, we illustrated the 

emissions impacts of school location and the built environment in two ways. First, we estimated 

emissions for two schools in the Gainesville sample. While this illustration did not constitute a “build it 

here or build it there” case study, it usefully illustrated the impacts of differences in location and built 

environment using two schools for which data were readily available. Second, we used the best-fit logit 

model to simulate the travel and emissions differences between the Gainesville sample and an alternative 

consisting of neighborhood schools. We believe that these two illustrations will be mutually supportive. 

7.1 Emissions for Two Sampled Schools in Gainesville 

In Section 4, we gave as examples two high schools. One school, Gainesville High School, is centrally 

located and surrounded by development. The other school, Eastside High School, is located at the edge of 

the urbanized area amidst undeveloped land. In our sample, Gainesville High School had a mode split of 

auto, 85%, walk, 13%, and bicycle, 2%. Eastside High School had 100% auto use. What if Eastside’s 

mode split and average auto trip length matched Gainesville’s? We emphasize that this example is 

illustrative; we do not suggest that the two schools are identical in other respects. 
Travel and Environmental Implications of School Siting 

23 

For each school: 

Automobile: Average trip to school distance multiplied by average national emissions/mile for 2002 auto 

fleet. We assumed one child per car. Given the limitations of the travel data, it was not possible to analyze 

carpool trips separately. Instead, carpool trips were lumped in with other automobile trips. Source: 2002 

national data from MOBILE 6, using the national light-duty fleet mix for 2002. 

School bus: For the two high schools in question, our sample contained no school bus trips. While clearly 

each school has school bus trips, for the purpose of this example we did not calculate school bus 

emissions. See below for a discussion of how this affects the validity of this example. 

Enrollment: Gainesville High School: 1914, Eastside High School, 1739 

We normalized simply by using the higher enrollment of Gainesville High School. 

The calculation, then, is: 

Enrollment * auto mode split * average trip length * emissions/mile * 2 trips/day = emissions/day 

Table 8: Emissions for Travel to or from Two High Schools

Mode Trip length Emissions/day, grams 

Auto Non-auto 

Average 

auto, mi. VOC CO NOx CO2

Gainesville 85% 15% 4.24 15,472 191,931 12,894 5,936,186 

Eastside 100% 8.42 36,147 448,408 30,123 13,868,670 

Eastside/ 

Gainesville 1.18 1.98 2.34 2.34 2.34 2.34

While the small sample sizes limit the ability to generalize, one can observe that: 

1. Both longer auto trips and higher auto mode shares contribute to higher emissions (more than 

twice as high) for sampled Eastside students compared to their Gainesville counterparts, and 

2. the Eastside students’ much longer trip lengths (twice as long) contribute much more to the higher 

emissions of Eastside students than does their higher auto mode split. 

The limitations of this brief example are numerous. Our inability to account for school bus and carpool 

trips should have minimal effect on relative emission levels. School bus and carpool mode shares are 

likely to be small for both schools, and trips by each mode are certain to be appreciably longer for 

suburban Eastside High School. Use of MOBILE’S average emissions rates per mile will understate 

emissions associated with short unlinked trips, and this underestimate will be greater for Gainesville High 

School with its shorter automobile trips. However, many of the automobile trips to both schools are part 

of longer linked trip tours by parents dropping off or picking up their children, and for these trips, 

emissions estimates should be reliable. 

To move to the next level of analysis one would have to know something about both school bus trip 

lengths and loads per bus, difficult to obtain for individual schools, and about carpool shares, which 

would require another travel survey. 
Travel and Environmental Implications of School Siting 

24 

7.2 Simulation of Emissions for Neighborhood Schools 

A second way to illustrate the emissions impacts of school location and resulting travel choices is through 

simulation. Using the best-fit logit model, we simulated mode choice probabilities for a scenario of 

neighborhood schools and walkable streets, and compared the results to actual mode choices for the 

Gainesville sample. In the neighborhood schools scenario, walk time to school was set at 10 minutes, 

corresponding to a half-mile trip at a speed of 3 miles per hour. Bike time to school was set at 

2.5 minutes, which corresponds to a half-mile trip at a speed of 12 miles per hour. All arterials and 

collectors were assumed to have sidewalks, raising sidewalk coverage to a uniform value to 1.0. All other 

variables were held constant at their base values. 

Mode choice results are reported in Table 9. The reductions in walk and bike times, and increase in 

sidewalk coverage, cause a significant shift in mode shares. The percentage of students walking to school 

more than doubles, from 4.5 to 10.3 percent of all trips. The percentages of students biking to school 

almost triples, from 3.4 to 11.1 percent. Together, the non-motorized modes increase from 7.9 to 

21.4 percent. These results are consistent with the earlier simulations reported in Section 5.4; the mean 

walk and bike times for the Gainesville data are so high that even 25% reductions will not bring kids 

close enough to school to see a substantial effect on numbers. However, with the larger travel time 

changes inherent in the neighborhood schools scenario, we see substantial change. Gainesville students’ 

travel preferences, as revealed in their current travel choices, are such that they will bike and walk in 

substantial numbers if walk and bike distances are reasonable. 

Table 9: Base Mode Shares, Simulated Mode Shares, and Mode Share Changes with a Neighborhood 

Schools Scenario 

Variable Base Simulation Simulation/ 

base 

Car 77.3% 66.0% 85%

School Bus 14.8% 12.7% 86%

Walk 4.5% 10.3% 229%

Bike 3.4% 11.1% 326%

Values may not sum to 100 percent due to rounding. 

In order to estimate the emissions impacts of such a shift, we use national values for school bus average 

trip length (6.8 miles, 1995 NPTS) and student loads (25). School bus emissions are also national 

averages from MOBILE 6. Automobile trip distances were set at the Gainesville sample average, 

4.82 miles. Finally, we assumed an enrollment of 400 for a neighborhood school, although this 

assumption makes no difference to the relative results. 

Emissions are thus estimated for a set of travel outcomes—bike/walk distances of a half mile, mean auto 

trip of ~4 miles, and ~7 mile mean school bus trip—consistent with a variety of compact development 

patterns, although we did not strictly model any one in particular.24

 

24 The closest might be Clarence Perry’s “neighborhood unit”, which explicitly located the school in the center of the 

neighborhood. Perry, “The Neighborhood Unit”, in Neighborhood and Community Planning, Volume VII of The 

Regional Survey of New York and Its Environs, 1929. 
Travel and Environmental Implications of School Siting 

25 

Table 10: Emissions Levels for Base Case and Neighborhood Schools Simulation

Emissions/day, grams

VOC CO NOx CO2

Sample mean 3,907 43,202 13,043 2,599,545 

Simulation 3,338 36,894 11,180 2,225,186 

Simulation/ 

Sample mean 

0.85 0.85 0.86 0.86 

The simulated neighborhood school reduces emissions by 14 to 15%. The reductions are not uniform 

because of the different emissions profile of school buses and personal vehicles. 

This simulation likely substantially underestimates the change in emissions, however, because it assumes 

fixed auto and bus trip distances. That is, although we simulated shorter distances for biking and walking, 

we did not do so for the motorized modes because, as explained in Section 5.2, the auto and school bus 

travel times did not enter the chosen model specification. As a result, the emissions difference between 

scenarios is not as dramatic as between Eastside and Gainesville High Schools, because most of the 

difference between the high schools came from shorter auto trip lengths. 

Not accounting for auto and school bus trip lengths with smaller tripsheds of neighborhood schools may 

overstate the impact of a change in land use patterns on nonmotorized travel. On the other hand, it may be 

that the chosen model specification is on target. The auto and school bus may be default modes of travel, 

relatively insensitive to travel time. Shortening walking and biking trips may simply make these modes 

viable for a higher proportion of trips. 

For the purposes of examining the emissions impacts of neighborhood schools, however, this analysis 

puts a floor under the likely size of the difference. If auto and school bus trip lengths were to be 

introduced into the analysis, any change in mode shares from the current simulation result would be more 

than offset in emissions terms by the decrease in trip lengths, increasing the emissions advantage of the 

simulated neighborhood schools over the base. 

As expected, the two illustrations support each other. The neighborhood schools simulation includes 

school bus emissions and draws on the whole sample. It provides the most credible estimates of emissions 

impacts of different school locations and built environments. However, due to the structure of the model, 

it cannot account for the emissions impacts of shorter auto trips to school and therefore tends to 

understate the advantage of neighborhood schools. The two schools comparison, despite relying on a 

small sample that does not include any school bus trips, adds very important information about the 

significance of auto trip length in determining emissions: first, auto trip length is not fixed between 

schools with different locations and built environments. Second, changes in auto trip length in response to 

differences in location and built environment can produce the majority of the emissions difference 

between two schools. 

8 CONCLUSIONS

According to the US Department of Education, student enrollment is expected to increase by one million 

from 2000 to 2006. In addition, many older and historic schools need to be modernized to meet today’s 

educational and technology requirements. To meet this enrollment demand, communities will need to 

build new schools and renovate older and historic schools. Increasingly, communities recognize that the 

decisions school districts make with respect to size, design and location of new and renovated schools 
Travel and Environmental Implications of School Siting 

26 

will have a visible impact on a community’s education, environmental and physical health, as well as 

community character, traffic congestion, and neighborhood vitality. In response, many states and 

communities are looking for ways to keep schools in their neighborhoods, close to students and the 

communities they serve. Further, even communities not facing school construction questions are 

increasingly concerned about the decline in walking and biking to school. In response, the goal of this 

study was to provide to communities information on influences on travel to school. 

8.1 School location and the built environment matter 

This study’s findings show that school location and the quality of the built environment between home 

and school affect how children get to school. As a result, schools located closer to their students help 

improve public health, not only by helping children stay active, but also by reducing traffic and 

automobile emissions. Improving the quality of the built environment around schools, and between homes 

and school also reduces driving and improves environmental quality. 

Schools built close to students, in walkable neighborhoods, can be called neighborhood schools. We 

conclude that compared to our sample from existing schools, neighborhood schools would reduce traffic, 

produce a 13 % increase in walking and biking and a reduction of at least 15% in emissions of concern. 

Numerous state and local initiatives are responding to the health and environmental impacts of school 

location. We hope this study is the first of many efforts that add to our understanding of how school 

location and design can support educational, civic, health, and environmental goals. 

8.2 More research would help communities better understand what works 

This study also points to the need for more investigation of these effects. To advance understanding of 

school travel decision-making and impacts, advances will need to be made in both the conceptual 

modeling of these decisions and in the quality of data available to test and refine models. Some future 

research needs are detailed in previous sections of this report, and others are described here. 

As described in Section 4 above, this study assembled data from several sources. Observations of modechoice came from household-interview surveys that had previously been conducted for general 

transportation-planning purposes. As a result, the number of school trips, while sufficient for this analysis, 

could not support much more detailed analyses. A special-purpose survey, or emphasis in other surveys on 

this population, would help further research. Such surveys should collect more detailed information on the 

characteristics of the student and household, as well as on the choices they face for school travel. For 

example, do both parents work? Is a school bus available? Is an older sibling in the household who can 

walk the younger to a shared school? 

In addition to travel data, detailed descriptions of the physical environment need to inform model 

development. This study used data available at or aggregated to the “transportation analysis zone”, or 

TAZ. A TAZ is typically a large area and cannot support detailed street-network and other land-use 

information required to convey some decision-influencing factors to the choice models. Some 

metropolitan areas have significantly upgraded their detailed land-use databases. In future research, this 

information can be used to better describe factors affecting the travel of students and their families. 

As described in Sections 2 and 3 of this report, the transportation planning community has not developed 

a full understanding of the decision processes affecting school travel. This study presents one likely 

element of that understanding, a single logit model describing the choice of mode. Nested logit model 

structures, as discussed in Section 2.2, may better capture what may well be a nested decision-making 
Travel and Environmental Implications of School Siting 

27 

process, but would require deeper samples. Additional research should develop a more comprehensive 

framework, incorporating a range of related choices and factors affecting those choices. For example: 

• How does the work or school status of the parent(s) affect the choice of mode to school? 

• Who actually makes the decision? 

• Do young and older students make decisions the same way? 

The value of additional research notwithstanding, the results of this study solidly suggest that the actions 

currently being taken to improve students’ walking environments, and to support communities that wish 

to locate schools in neighborhoods, will result in increases in student walking and biking to school. This 

benefit can be added to the others found in the literature for neighborhood schools. 
United States Environmental Protection Agency

Development, Community, and Environment Division (1808T)

EPA 231-R-03-004

September 2003

Recycled/Recyclable

Printed with Vegetable Oil-Based Inks on Recycled Paper 

(Minimum 50% Postconsumer) Process Chlorine Free