Children’s Bicycling to After- School Activities:
The Case of the Davis AYSO Bike to Soccer Program
Gil Tal
Susan Handy
Institute of Transportation Studies
University of California Davis
1 Shields Avenue
Davis, CA 95616
February 2008
ii
Table of Contents
1. INTRODUCTION........................................................................................................ 1
2. RESEARCH METHODS............................................................................................. 2
2.1 The Survey ................................................................................................................ 2
2.2 The Sample ............................................................................................................... 2
2.3 Calculating Non- Motorized Travel Distances .......................................................... 3
2.4 Limitations ................................................................................................................ 4
3. ANALYSIS OF PLAYER’S MODE ........................................................................... 5
3.1 Mode of Travel to the Game..................................................................................... 5
3.2 Biking to Practice.................................................................................................... 10
3.3 Multivariate Analysis of Travel Mode to the Game ............................................... 11
4. CHANGE IN TRAVEL MODE FROM 2005 TO 2006 .......................................... 13
5. TRAVEL DISTANCE ANALYSIS........................................................................... 16
6. DISCUSSION.............................................................................................................. 20
7. CONCLUSIONS ......................................................................................................... 22
ACKNOWLEDGEMENTS ........................................................................................... 24
REFERENCES................................................................................................................ 25
APPENDIX A: SURVEY .............................................................................................. 27
APPENDIX B: WEIGHTS AND HOME FIELDS...................................................... 29
1
1. Introduction
In recent years, transportation planners have devoted more attention to the goal of
increasing the non- motorized trips of children and adults. Non- motorized trips are
considered important as a mean of reducing motorized trips as well increasing physical
activity. Indeed, lack of physical activity has been identified as a major public health
problem for both adults and younger people ( Center of Disease control ( CDC), 2005;
Sallis, et al., 2004). A CDC report from 2002, for example, reports that about a third of
the teenagers are not physically active enough ( CDC, 2002). The lack of physical
activity among children is, in part, associated with travel behavior and urban form
( Ewing et al, 2003, Handy et al, 2002; Martin- Diener et al, 2005): children living in auto-oriented
areas in the U. S. use walking and biking as modes of transportation to nearby
destinations to a limited extent and less than in the past ( Sallis et al, 1993; Sallis et al
2000). Their high level of auto use is, of course, tied to a high level of auto use among
their parents, particularly their mothers ( McMillan, 2007).
For children, much discussion has focused on the journey to school, though
researchers are now addressing non- school travel as well. According to McDonald
( 2006), who studied children’s travel patterns based on the National Household Travel
Survey, only 12% of the trips to sport activities are made by bike. The NHTS data do not
allow a full estimate of non- motorized trips taken alone, versus trips taken with a parent,
but it is reasonable to assume that use of bikes is even lower when parents are involved in
the trip. Copperman and Bhat ( 2007) analyzed the determinants of children’s weekend
physical activity participation using data from the 2000 Bay Area travel survey. Their
models correlate socio- demographic and land use variables with active and passive travel
( i. e. non- motorized and motorized) and with physically active and passive activities.
Their findings suggest that children ( ages 5 to 17) rarely use non- motorized modes to get
to places where they engage in physical activities, and that individual and household
demographics, along with environmental factors, affect the level of physical activity.
One place where children might be expected to use non- motorized modes to get to
after- school activities is Davis, CA, well- known as a bicycle- friendly community. In Fall
2006, the Davis chapter of the American Youth Soccer Organization ( Davis AYSO) set
out to increase bicycling to soccer games through a two- pronged approach. First, the
organization adopted a new approach to scheduling games, spreading them to fields
throughout Davis rather than scheduling as many games as possible at three centralized
fields. For most games, at least one team ( and often both) was scheduled for a field
within their neighborhood. In addition, Davis AYSO undertook a promotional program to
encourage families to bike to games, including distribution of “ Bike to AYSO” bike
stickers, the creation of a special web site that included a Davis Bike Map, and e- mail
announcements to almost all families about the “ Bike to AYSO” effort. To evaluate the
effectiveness of this program in encouraging players and their families to bike more
frequently, Davis AYSO worked with researchers at the University of California, Davis.
This report describes the methods used in that effort and summarizes the results.
2
2. Research Methods
Ideally, interventions like the bike- to- soccer program that are designed to change
behavior are evaluated based on before- and after- surveys of the targeted behavior. For
the bike- to- soccer program, no before survey was conducted. Thus, the evaluation relies
on reported changes in behavior from one year to the next, rather than direct measures of
these changes.
2.1 The Survey
In October and November 2006, a group of four UC Davis students and several
community volunteers administered a 2- page survey to AYSO parents. With the help of
coaches and team parents, the survey takers approached parents at Saturday morning
soccer games on three successive weekends. Survey takers focused on selected AYSO
divisions ( defined by age and gender) each weekend and attempted to collect one survey
for each player in the league. Survey takers covered 76 games over the three weekends.
The students and several paid assistants entered the data into an Excel spreadsheet. The
data were then checked for accuracy and consistency. The final database includes surveys
for 1,084 players, nearly half of all players in the league.
The survey included questions on mode of transportation to that day’s game for
both the parent and the player ( see Appendix A). To measure change in transportation
mode from the prior year, parents were asked to estimate the number of games out of a
season of 10 for which each transportation mode was used, both this season and the
previous season. In addition, parents were asked to provide information about each child
in the household, including age, whether or not the child was playing AYSO, and the
child’s bicycling abilities. The survey also included questions about the parent, including
his or her bicycling frequency. Open- ended questions also asked about the most
significant barriers to bicycling to soccer games, and another question gave parents a
chance to express whether they felt the program should be continued next year.
2.2 The Sample
A total of 1,084 surveys were completed. This total represents 49% of the 2210 players
participating in the U6 ( under 6 years old) through U19 Divisions of Davis AYSO in
2006. The distribution across division ( defined by age and gender) is shown in Table 2- 1.
About 55% of surveys were from boys divisions, and 45% from girls divisions. Nearly
55% were from U8 and U10 divisions. This distribution is similar to but does not
perfectly match the actual distribution of players, as shown in Table 2- 2.
Because the response rate is not consistent across divisions and because mode
choice is likely to vary with both age and gender, we calculate weights based on
age/ gender division ( weight = ( actual division share)/( sample division share)), as shown
in Table B- 1 in Appendix B. These weights were applied to the data for the descriptive
analysis reported in Sections 3 and 4, but omitted in the multivariate analysis.
3
Table 2- 1. Surveys by Division
Boys Girls Total Percent
U6 111 63 174 16.1%
U8 156 107 263 24.3%
U10 170 155 325 30.0%
U12 54 58 112 10.3%
U14 60 54 114 10.5%
U19 44 52 96 8.9%
Total 595 489 1,084
Percent 54.9% 45.1%
Table 2- 2. Players by Division
Boys Girls Total Percent
U6 147 97 244 11.0%
U8 299 204 503 22.8%
U10 305 231 536 24.3%
U12 215 194 409 18.5%
U14 151 113 264 11.9%
U19 129 125 254 11.5%
Total 1246 964 2210
Percent 56.4% 43.6%
2.3 Calculating Non- Motorized Travel Distances
Travel distance is a potentially importance influence on mode choice for soccer games, as
it is for mode choice for other trip types. To estimate distances, we first geo- coded home
addresses for respondents. Street addresses in Davis were reported for 920 of the 1048
completed surveys. About 60 respondents lived out of town or did not provide a valid
address, precluding geo- coding. Because Davis has an extensive system of off- street
bicycle and pedestrian facilities, we used a network that includes all of the minor and
primary roads in the city plus pedestrian and bike ways to estimate travel distance ( Figure
2- 1). This network includes 60 miles of off- street facilities and excludes freeways that
are not open to bicyclists and pedestrians. The potential travel distance by bicycle for
each player to his or her game, to Community Park fields, and to his or her home field
was calculated using the Network Analyst function of ArcGIS. The route was calculated
based on the shortest way from the player home address to the field.
4
Figure 2- 1: Davis biking Network
2.4 Limitations
One limitation of this method is the use of a single game to establish travel patterns.
However, the relatively large sample helps to ensure that the survey is representative of
the overall pattern of travel for the season, even if that particular day was not typical for
all individual players. The weather was nice ( e. g. sunny with temperatures ranging from
the 60s to the 80s) all three weekends of the surveys.
A second limitation is the reliance on parent surveys. This approach leaves out
children who came to their games without their parents, a group that might be more
inclined to bicycle. Anecdotal evidence suggests that for most children, parents attend
their games, however. In addition, counts of the number of bikes at games were largely
consistent with the reported number of players and parents biking to games.
The lack of data about travel behavior for previous years is a significant
methodological limitation. Parents where asked about their and their children’s travel
mode last year, but their responses depend on recall of behavior one year prior to the
survey. Furthermore, the survey did not include questions about the factors that affected
their travel mode choices in the previous year. In addition, families who had a player in
2005 but not in 2006 were not included in the survey population.
5
3. Analysis of Player’s Mode
The survey collected data on biking both to games ( i. e. the mode of travel to the game
that day) and practices ( i. e. whether the play usually bikes to practices). In analyzing
biking levels, we considered a variety of factors that might explain why some players
bike and some don’t. In looking at these factors, we compared the share of players biking
in different categories. We tested the statistical difference between categories using the
chi- square statistic. The total values may vary for the different analyses because of
missing data for some survey questions.
3.1 Mode of Travel to the Game
Driving dominates travel to games for both players and parents: 76.8% of players drove
to the game the day of the survey, versus 18.4% biking and 4.8% walking; 78.1% of
parents drove, versus 14.6% biking and 7.3% walking. However, these shares vary by a
number of factors, including location, travel distance, age and gender, and family
characteristics.
Location
Mode to that day’s game varies by location within the city ( Figure 3- 1). Most notably,
players living in North Davis are more likely to bike than players in other parts of the
city. In addition to distance, as discussed below, the extensive off- street bicycle network
in this part of Davis might contribute to the higher bicycling share.
Figure 3- 1: Travel Mode
6
Travel Distance
The average distance from home to today’s game via the bicycle network for players who
live within the city of Davis was 1.82 miles with a range from 0.06 miles to 5.93 miles.
The average distance for players who walked to the game ( 5.8% of the sample with valid
addresses) was 0.73 miles. The average distance for players who biked ( 20% of the
sample with valid addresses) was 1.21 miles. The potential biking or walking distance for
players who were driven ( or drove) to their games ( 84.2% of the sample with valid
addresses) was 2.09 miles. Note that players from the west and east ends of towns have
longer distances to their games than other players ( Figure 3- 2).
Figure 3- 2: Travel Distance to the Survey Day Game
Driving is the dominant mode even at short distances. Among players who lived
less than 1.5 miles from the field ( 38% of the sample), more than 50% of the players
were driven ( or drove) to their games. Only a few players walked more than half of a
mile and none beyond 2.5 miles. At less than half a mile from the field, about 40% of
players bicycled and 20% walked, with just under 40% driving. The combined share of
players walking and bicycling shows a steep drop off between 0.5 and 2.0 miles but
remains relatively steady up to 4.0 miles ( Figure 3- 3 and Figure 3- 4).
7
0
10
20
30
40
50
60
70
0- 0.5 0.5- 1 1- 1.5 1.5- 2 2- 2.5 2.5- 3 3- 3.5 3.5- 4 4- 4.5 4.5- 5 5- 5.5 5.5- 6
Miles from Home to Field
Percent
Figure 3- 3. Share of Players Using Non- Motorized Modes by Distance
to Field
0
20
40
60
80
100
120
140
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Miles from Home to Field
Players
Walk
Bike
Car
Figure 3- 4. Number of Players Using Non- Motorized Modes by Distance
to Field
Age and Gender
The general pattern of modes to today’s game holds across divisions for both players
( Table 3- 1) and parents ( not shown), but with notable differences. Over 33% of the
players in the Under- 10 Boys ( U10B) division biked, the highest compared to the other
8
divisions. Gender of the player was associated with biking to the game, with 21.4% of
boys in all divisions biking to the game, in contrast to 14.4% of girls. This pattern does
not hold across all divisions, however: in the U6 division, girls were more likely to bike
to the game that day than boys. Players were equally distributed across the city by age
( Figure 3- 5).
Table 3- 1. Player's Mode to Today's Game by Division
% Bike % Walk % Drive N
U6B 14.6 5.5 80.0 71
U6G 15.9 4.8 79.4 48
U8B 18.1 6.5 75.5 146
U8G 11.2 5.6 83.2 100
U10B 33.1 7.8 59.0 146
U10G 21.4 5.2 73.4 113
U12B 18.9 1.9 79.3 104
U12G 10.3 1.7 87.9 95
U14B 18.3 5.0 76.7 74
U14G 14.8 7.4 77.8 55
U19B 18.2 0.0 81.8 63
U19G 11.5 3.9 84.6 61
Total Girls 14.4 4.6 81.0 472
Total Boys 21.5 5.0 73.5 603
Total 18.4 4.8 76.8 1076
Chi- square = 49.4; p= 0.0007; N= 1076
9
Figure 3- 5: Age Distribution
Family Characteristics
Families with more than one child were more likely to bike to games than families with
only one child ( Table 3- 2). Interestingly, as discussed below, one of the most frequent
challenges to biking to games mentioned by parents was having more than one player in
the family.
Table 3- 2. Player’s Mode to Today's Game by Number of
Children in Family
% Bike % Walk % Drive N
One child in family 10.9 1.9 87.7 172
More than one child 20.1 5.1 74.8 876
Total 18.6 4.6 76.8 1047
Chi- square = 14.08; p= 0.0009; N= 1047
The player’s mode of travel to the game is also associated with the frequency with
which their parents bike as a mode of transportation: of players whose parents bike daily,
34.1% biked to their game; of players whose parents never bike, only 2% bike to their
games ( Table 3- 3). Not surprisingly, players who bike to practice are more likely to bike
to games, as are players who bike to school ( results not shown).
10
Table 3- 3. Player’s Mode to Today's Game by Parent’s Biking Frequency
% Bike % Walk % Drive N
Never 2.0 6.0 92.0 207
Less than once per month 6.8 6.8 86.4 276
Between once per week and once per month 12.6 4.7 82.7 250
More than once a week but less than daily 25.2 4.1 70.6 166
Daily 34.1 4.4 61.4 115
Total 18.3 5.0 76.7
Chi- square = 93.66; p< 0.0001; N= 1013
3.2 Biking to Practice
Parents were also asked if their player usually bikes to practice. Overall, 57.4% of
players usually bike to practice. The differences by division are significant, however.
Biking increases steadily with age until peaking for the U12 and U14 divisions then
declines for the U19 divisions ( Table 3- 4). The differences by sex are not significant,
however.
There are several reasons why the travel mode to practice may differ from the
travel mode to the weekend game. First, the average travel distance to practice is shorter,
as practices are generally located on fields or at parks within the team’s home
neighborhood. Second, time and activity restrictions may be different for the player on
weekdays and for parents as well. Third, players may be more likely to travel to practices
on their own, while weekend games are traditionally a family event to which player,
parents, and siblings travel together.
Table 3- 4. Player's Biking to Practice by
Division
% Drive % Bike N
U6B 72.6 27.4 69
U6G 70.5 29.5 46
U8B 61.2 38.8 143
U8G 68.6 31.4 95
U10B 51.2 48.8 148
U10G 61.6 38.4 110
U12B 48.0 52.0 98
U12G 36.8 63.2 94
U14B 43.1 56.9 72
U14G 60.0 40.0 51
U19B 58.1 41.9 62
U19G 71.1 38.9 61
Total 57.4 42.6 1049
Chi- square = 49.4; p= 0.0007; N= 1076
11
3.3 Multivariate Analysis of Travel Mode to the Game
Family members often travel to games together. Even so, the factors influencing mode
choice may be different between the parent and the child for a several reasons. First,
parents and children may choose the same travel mode for different reasons, such as time
constraints after the game, the need to carry equipment, etc. Second, in the case where
one parent has two or more children playing simultaneously in different divisions, his or
her mode choice may be constrained by the need to travel between games at different
locations. Finally, at least for older children, parents may travel via a different mode than
the children, usually driving while the player is biking.
We estimated two binary logistic regression models for driving relative to not
driving ( i. e. biking or walking), one for the mode choice of the player, and one for the
mode choice of the parent. These models use as explanatory variables ( 1) socio-demographic
indicators such as age and sex of both the parent and the child, and the
number of children in the household, ( 2) estimated trip distance, ( 3) a dummy variable
indicating whether the player can bike, and dummy variables indicating whether the
player bikes to school and to soccer practice, and ( 4) an indicator of parent biking
frequency in the form of a dummy variable for biking at least once a week. These models
are not conventional mode choice models as they do not include mode specific variables.
The player travel mode model ( Table 3- 5) shows that only three variables are
significant predictors of driving rather than biking or walking to games: trip distance,
with a positive effect on driving; player bikes to school, with a negative effect on driving;
and parent’s biking at least once per week, with a negative effect on driving. The odds
ratios show the magnitude of the effect. For each additional mile to the field, the odds of
driving increase by a factor of 3.4. For players who bike to school the odds of driving are
0.6 times the odds of driving for those who don’t bike to school. If the player’s parent
bikes at least once per week, the odds of driving are 0.4 times the odds for those whose
parents don’t bike. The child’s age, gender, and ability to use a bike were not significant,
nor were parent’s age or gender or the number of children in the household. The model
for parent’s mode of travel ( Table 3- 6) is similar to the model for the child’s mode of
travel. The small differences between the models likely result from the reasons noted
above.
Confirmation of these results and insights into additional factors that influence
mode choice come from responses to an open- ended question about barriers to biking to
the game that will be discussed in Section 5.
12
Table 3- 5. Model for Player Driving versus Biking or Walking
Coefficient Odds Ratio
Intercept 2.260** 9.650
Trip distance ( miles) 1.214 ** 3.368
Game is not at home field 0.224 1.251
Player age - 0.052 0.949
Player sex ( female = 1) - 0.171 0.843
Player can bike - 0.753 0.471
Player bikes to school - 0.528 * 0.590
Number of children in the household ( 1 to 4+) - 0.286 0.751
Parent sex ( female = 1) .0.037 1.037
Parent age - 0.008 0.992
Parent bikes more than once per week - 0.889 ** 0.411
Adjusted R2 = 0.23
N = 705
** P value < 0.05; * P value < 0.1
Table 3- 6. Model for Parent Driving versus Biking or Walking
Coefficient Odds Ratio
Intercept - 1.106 3.210
Trip distance ( miles) 1.295 ** 3.650
Game is not at home field 0.298 1.347
Player age - 0.011 0.990
Player sex ( female = 1) - 0.029 0.972
Player can bike - 0.819 * 0.441
Player bikes to school - 0.575 ** 0.563
Number of children in the household ( 1 to 4+) - 0.161 0.851
Parent sex ( female = 1) - 0.056 0.946
Parent age 0.002 1.002
Parent bikes more than once per week - 0.951 ** 0.386
Adjusted R2 = 0.23
N = 706
** P value < 0.05; * P value < 0.1
13
4. Change in Travel Mode from 2005 to 2006
In the survey, parents were asked to estimate how many games out of a 10- game season
they would bike, walk, or drive to this year. For players who also played in 2005, parents
were asked to make the same estimation for the previous year. The difference in the
number of games to which the player biked is used as an indicator of change in biking.
Reported biking, walking, and driving trips did not sum to 10 for all respondents.
In many cases, respondents left blank the modes that they did not use. Before analyzing
these data, we filled in missing values using three rules: ( 1) If the total number of trips
reported for 2005 or 2006 was less than five we excluded it from the analysis; ( 2) if only
one travel mode was reported ( between 5 to 10 trips of the same mode) but the other
modes were left blank, we assumed that this was the only travel mode in use; ( 3) if the
total number of trips reported was higher than 5 but lower than 10, we inflated the
number to 10 trips while preserving the reported ratio between the modes.
For players who also played in 2005, the number of games to which a player
biked over a 10 game season increased by an average of 1.53 games, from an average of
1.65 trips in 2005 to an average of 3.18 trips in 2006 ( Table 4- 1). Note that these
changes reflect not only the effect of the neighborhood fields program, but also the aging
of players by one year. Despite the average increase in biking, 49% of players did not
bike to any games in 2006, and 64% of players did not increase their biking in 2006.
Table 4- 2 compares 2005 AYSO participants, parents and players, with the 2006
participants. The 2006 numbers include players who didn’t play in 2005, including all of
the youngest players. This comparison thus reduces the effect of aging. Driving trips to
games declined between 2005 and 2006 by 2.2 for players and nearly 2 for parents, while
both biking and walking trips increased. Additional analysis shows that the small group
that had a higher number of driving trips in 2006 biked to almost half of their games in
2005 on average but very few in 2006.
Table 4- 1. Change in Number of Trips by Mode for Continuing
Players, 2005 to 2006
Drive Trips Bike Trips Walk Trips
Players
2006 6.20 3.08 0.72
2005 8.14 1.65 0.18
Change - 1.94 1.43 0.54
Parents
2006 6.41 2.76 0.83
2005 8.19 1.59 0.21
Change - 1.78 1.17 0.62
N= 680
14
Table 4- 2. Change in Number of Trips by Mode, , 2005 to 2006
Drive Trips Bike Trips Walk Trips
Players
2006 5.81 3.39 0.79
2005 7.99 1.80 0.21
Change - 2.18 1.59 0.58
N= 586
Parents
2006 6.11 2.98 0.91
2005 8.08 1.70 0.22
Change - 1.97 1.28 0.69
N= 570
We estimated binary logistic regression models for driving less in 2006 versus
driving the same or more, for players who also played in 2005 ( Table 4- 3) and for their
parents ( Table 4- 4). The models revealed possible correlations between current
characteristics and change in travel behavior. We used similar variables as with the
travel mode models, this time adding variables for biking to school and biking to practice
and omitting trips specific variables.
Table 4- 3. Model for Player Drives Less in 2006 versus Same/ More
Estimate Odds
Ratio
Intercept 1.744
Player age 0.042 * 1.04
Player sex ( female = 1) 0.091 1.10
Player cannot bike - 0.076 0.93
Player does not bike to practice 0.696 ** 2.01
Player does not bike to school - 0.164 0.85
Parent age - 0.004 1.00
Parent sex ( female = 1) - 2.695 0.07
Parent bikes more than once per week 0.305 ** 1.36
Number of children in the household ( 1 to 4+) - 0.139 0.87
Adjusted R2 = 0.12
N = 473
** P value < 0.05; * P value < 0.1
15
Table 4- 4. Model for Parent Drives Less in 2006 versus Same/ More
Estimate Odds
Ratio
Intercept 1.185
Player age 0.047 * 1.05
Player sex ( female = 1) 0.056 1.06
Player cannot bike - 0.007 0.99
Player does not bike to practice 0.664 ** 1.94
Player does not bike to school - 0.199 * 0.82
Parent age 0.002 1.00
Parent sex ( female = 1) - 2.620 0.07
Parent bikes more than once per week 0.398 ** 1.49
Number of children in the household ( 1 to 4+) - 0.085 0.92
Adjusted R2 = 0.13
N = 472
* P value < 0.05; ** P value < 0.1
These models suggest that the variables included in the model provide a limited
explanation for change in driving from one year to the next ( adjusted R- squares of 0.13
and 0.15). In both models, parent’s biking frequency has a significant effect: if the parent
bikes more than once per week, both the player and the parent are more likely to have
driven less in 2006 than in 2005. The odds of driving less also increase with player age.
The other statistically significant variable in both models has a counter- intuitive effect: if
the player does not bike to practice, both the player and the parent are more likely to
drive less. In the model for parents, if the player does not bike to school, the odds of
driving less are lower.
16
5. Travel Distance Analysis
In this section we analyze travel distances from home to three locations: ( 1) the home
field, ( 2) the Community Park fields and ( 3) the field for that day’s game ( i. e. where the
survey was conducted).
As expected, the home field is the closest to home with an average distance of less
than a mile ( Table 5- 1) 1. Average travel distances to the Community Park fields and to
the field for that day’s game are similar at 1.94 miles and 1.79 miles, respectively.
Figure 5- 1 shows that players who live in West Davis and South Davis tend to have
longer travel distances to both home field and to Community Park fields than players
living in other parts of the city.
Table 5- 1. Travel Distance to Fields
Home Community Game N
U6B 0.88 1.89 0.94 39
U6G 0.94 1.97 2.01 55
U8B 0.94 1.36 1.94 137
U8G 0.85 1.65 1.99 92
U10 0.68 1.52 2.08 280
U12 0.88 2.42 2.04 85
U14 1.32 2.23 1.08 61
U19 1.16 2.48 2.22 81
Mean 0.96 1.94 1.79 Total 830
The home field for all divisions except U8B is half the distance and nearly a mile
closer on average than the fields at Community Park ( Table 5- 2). The total estimated
savings in driving distance, assuming the rate of driving for 2006 ( Table 4- 1), is 13, 428
miles ( 0.98 miles per player x 6.2 driving trips per player x 2210 players). Thus, the
reduction in driving is substantial, even without accounting for the shift from driving to
bicycling.
The difference in distance varies by area of the city ( Figure 5- 2). Although for
most players, the home field was closer to home than the Community Park fields ( green
dots), many players had a longer distance to their home field than to Community Park
( orange and red dots). Players in the latter category tended to live in Central and East
Davis, including Wildhorse, with smaller concentrations of such players in West and
North Davis and few in South Davis.
1 In this section we aggregate age and sex groups that played in the same location.
17
Table 5- 2. Comparison of Travel Distances to Fields
Home/
Community
Home -
Community N
U6B 0.47 - 1.01 39
U6G 0.48 - 1.02 55
U8B 0.69 - 0.42 137
U8G 0.52 - 0.79 92
U10 0.45 - 0.83 280
U12 0.37 - 1.53 85
U14 0.59 - 0.91 61
U19 0.47 - 1.31 81
Mean 0.50 - 0.98 Total 830
Figure 5- 1: Travel Distance to Home Field
18
Figure 5- 2: Travel Distance Ratio, Home Field vs. Community Park Field
As discussed in Section 3, travel distance has a significant effect on mode choice
up to a range of 1.5 miles. Figure 5- 3 shows actual travel distance to that day’s game
( categorized roughly as under 0.5 mile, 0.5 to 1 mile, 1 to 1.5 miles, and greater than 1.5
miles). The pattern of travel distances appears to be similar for all areas of the city,
although players in the eastern areas of South Davis tend to have the longest distances,
and players in North Davis tend to have shorter distances than players in other parts of
the city.
19
Figure 5- 3: Travel Distance to Today’s Game
20
6. Discussion
Our analysis suggests several factors that may explain why some players and
parents bike and others do not. The most significant predictor of biking to games for both
players and parents is the parent’s frequency of biking for transportation purposes in
general. If the parent bikes regularly, the probability of biking to the game is higher for
the player and for the parent. As would be expected, travel distance also significantly
affects whether players and parents bike to the game.
To shed further light on mode choice, the survey asked an open ended question
about barriers to biking to the game ( Table 6- 1). Over three- quarters of parents reported
one or more barriers to biking to games. At the top of the list was distance from home to
the field. The second most frequently named barrier was having multiple children at
different fields, and the third was carrying equipment and snacks. Only 1.9% of parents
indicated that willingness prevented them from biking but 8.5% said that time to get
organized is too long and 6.5% that travel time is too long. Combining these three as a
willingness factor, and checking their reported ability to bike and the number of available
bikes at home, we estimate that about 16% can bike but choose not to do so.
Table 6- 1. Barriers to Biking to Games
Category Count Percent of
Comments
Percent
of
Parents
Distance from house ( Davis resident) 258 25.3 23.8
Multiple children at different fields/ times 151 14.8 13.9
Carrying equipment/ snacks 142 13.9 13.1
Time to get ready/ organized 92 9.0 8.5
Time it takes to get to field 70 6.9 6.5
Schedule conflicts before/ after game 63 6.2 5.8
Safety ( dangerous route/ traffic/ poor bike access) 50 4.9 4.6
Age of certain child 33 3.2 3.0
Distance from house ( non- resident) 27 2.7 2.5
Child not on neighborhood team 25 2.5 2.3
Willingness 21 2.1 1.9
Children's riding ability 21 2.1 1.9
Punctuality at game 21 2.1 1.9
Lack of or damaged bikes/ equipment 16 1.6 1.5
Weather 13 1.3 1.2
Adult physical disability 8 0.8 0.7
Irrelevant response 5 0.5 0.5
No place to put bikes 2 0.2 0.2
No barrier mentioned 243 22.4
21
Parents were also asked whether they wanted Davis AYSO to continue the Bike
to AYSO program next year. Over 92% of parents said that AYSO should continue the
neighborhood fields program next year. Support was strong across all divisions, from
87% in U19G to 100% in U10B, and in all areas of the city, from 89.6% in east Davis to
95% in west Davis. Awareness of the program was high: over 78.2 percent of parents
said they had heard of the program. Still, a sizable share of parents had not heard of the
program before the survey. Increased awareness of the program ( for example, through a
challenge to teams as to which is doing the most biking) might itself lead to more biking.
Many parents offered comments on the program, including suggestions for
improvements for the next year, other benefits from the program, and criticisms of
various sorts. One quarter of parents offering comments said that the program worked
well and that no improvements were needed next year. The next most frequent comment,
at 17.3% of respondents, suggested moving games closer to the home field. Another
major concern expressed by 6.4% of respondents is ensuring that practices are closer to
home so that biking can be a viable option. Some suggestions were made that would
enhance and expand the program including: small incentives for kids to bike ride,
increasing communication and promotion of the program, installing bike racks at fields,
and providing bike maps of Davis.
About 11% of respondents conveyed wishes for the neighborhood fields program
to be eliminated, preferring all games to be at a central location like Community Park as
in previous years. Of this group, many miss the community feel that is foregone by
splitting up the fields. The major problem expressed by parents is the difficulty in
managing to bike to multiple games due to multiple children participating in AYSO. For
this reason, many prefer the convenience of one central playing location.
Table 6- 2. Comments on Neighborhood Fields Program
Category Number
Percent of
Comments
Percent of
Parents
Program worked great - needs no improvements 97 25.0% 8.9%
Move games closer to home field 67 17.3% 6.2%
All games at one field - community park 43 11.1% 4.0%
Practices closer to home 25 6.4% 2.3%
Divide teams based on neighborhood location 23 5.9% 2.1%
Hard to manage attending multiple games - conflict with
multiple children 13 3.4% 1.2%
Need restroom facilities at some parks 13 3.4% 1.2%
Irrelevant 10 2.6% 0.9%
Games are too early 9 2.3% 0.8%
More communication and promotion of the program 8 2.1% 0.7%
Fields are not well maintained - safety hazard 8 2.1% 0.7%
Provide incentive for kids to bike ride 7 1.8% 0.6%
Need bike racks at some fields 7 1.8% 0.6%
Fields are too wet 6 1.5% 0.6%
Keep games in Davis - not Antelope, Esparto, or
Winters 6 1.5% 0.6%
Better bike access 5 1.3% 0.5%
Note: Data are not weighted by division; only categories with 5 or more responses included in table.
22
7. Conclusions
From a policy standpoint, two questions are of interest: ( 1) was the Bike to AYSO
Program a success? ( 2) What lessons does this case offer for the goal of increasing biking
in the future?
An increase in biking from 2005 to 2006 provides evidence, though not
conclusive evidence, that the bike- to- soccer program had an impact on mode of travel to
games, despite an already substantial level of biking to games ( and even more to
practices) in Davis. Nevertheless, it appears that there is still room for further increases.
Some of the barriers to biking that parents mentioned reflect a lack of willingness to bike
rather than insurmountable constraints on biking. In addition, even among players whose
parents report that they bike daily, a majority is driven to games. A more aggressive
promotional program might achieve additional increases in biking.
The first set of strategies addresses physical barriers. Distance to games is a
critical factor in the decision to bike but its effect diminishes after 1.5 miles. Davis
AYSO has already addressed this factor by scheduling games on fields within teams’
neighborhoods, though the schedule includes many “ away games” that necessitate travel
across town ( and to other towns, in many divisions). In future years, a scheme that
maximizes the number of players within 1.5 miles of their home field ( rather than
minimizing average travel distance, for example) might help to increase bicycling further.
The city might also be able to reduce distances by adding new links in the bicycle
network in key locations. A program to identify and implement such links could also
increase bicycling to school, as many of the playing fields are located at elementary and
junior high schools. The need to carry equipment and snacks was also cited as a barrier
to bicycling. Many families use bicycle trailers to carry these items to games, and a
program run by AYSO or the city to loan trailers to families for individual games or for
the season could enable more families to bicycle.
The second set of strategies addresses attitudinal barriers. The significance of
both biking to school for the player and the frequency of bicycling for the parent suggests
that the more a family bikes for some purposes, the more they bike for other purposes.
City programs to promote bicycling in general could thus lead to increases in bicycling to
soccer games. Such programs might also push parents to overcome the challenge of
getting out of the house early enough to bike to games and might counteract a lack of
willingness on the part of players and/ or parents to bike rather than drive. Programs that
focus on increasing bicycling safety or that help children learn how to bicycle could also
help. “ Bike pooling” programs that help players get to their games by bicycle when
parents have scheduling conflicts ( e. g. other children playing games at the same time on
other fields) might also increase bicycling, at least among players.
Although this study focuses on a unique case – the very specific trip to soccer
games and the very special setting of Davis, CA – it points to the need for future research
to explore in more depth the influence of physical, attitudinal, and logistical factors on
the choice to bicycle. Issues highlighted in this study and deserving of further
exploration include the relationship between the travel choices of parents and children
and the connection between mode choice for trips of different purposes. Implementation
of this survey in a sample of communities reflecting a range bicycling environments
could yield further insights, as could the implementation of similar surveys targeting
23
other specific trip purposes. With active travel among children and their parents on the
decline, such research could provide a basis for the formulation of policies that would
reserve this trend and generate significant health benefits.
24
Acknowledgements
This project was supported by a grant from Davis AYSO, under the direction of
Commissioner Steve Brown. Kanani Brown, Allison Chan, Noah Hochman, and Loren
Suslow, while students at UC Davis, participated in the design, implementation, and
analysis of the survey. Lise Smidth of the Davis Bicycle Commission contributed to
survey implementation. Joe Krovoza of the Institute of Transportation Studies at UC
Davis was instrumental in the conception and execution of the study.
25
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27
Appendix A: Survey
28
29
Appendix B: Weights and Home Fields
Table B- 1. Weights by
Division
Boys Girls
U6 0.65 0.76
U8 0.94 0.94
U10 0.88 0.73
U12 1.95 1.64
U14 1.23 1.03
U19 1.44 1.18
Table B- 2. Fields by Division and Home Teams
Division
Home
Teams
Arroyo U6 W
Arroyo East U12 W
Emerson North U10 W
Emerson South U8 W
Community 1 U19G -
Community 2 U8 N, E
Community 3 U8 N, E
Community 5 U6 N, E
Community 6 U12 N, E
Community 8 U10 N, E
Northstar East U12 N
Northstar West U14 NW
Mace North U10 E
Mace South U10 E
Nugget Center U19B -
Nugget West U14 SE
Putah Creek Park U6 S
Sandy Motley North U8 S
Sandy Motley South U6 S
Walnut Northeast U10 S
Walnut South U8 S