OO
RESULTS OF THE FALL 2007
UC DAVIS CAMPUS TRAVEL ASSESSMENT
INSTITUTE OF TRANSPORTATION STUDIES
AND
TRANSPORTATION AND PARKING SERVICES
UNIVERSITY OF CALIFORNIA DAVIS
UCD- ITS- RR- 09- 01
FALL 2008
by
Christopher D. Congleton
with assistance from Caleb T. Cheng
under the guidance of Susan L. Handy
Institute of Transportation Studies
One Shields Avenue
University of California
Davis, California 95616
Tel: 530- 752- 6548 Fax: 530- 752- 6572
http:// www. its. ucdavis. edu/
email: itspublications@ ucdavis. edu
ii
Table of Contents
Acknowledgements ........................................................................................................ iv
1 Introduction.............................................................................................................. 1
2 Methods .................................................................................................................. 3
2.1 Survey Design ................................................................................................. 3
2.2 Sampling Plan.................................................................................................. 3
2.3 Population Weighting....................................................................................... 4
2.4 Primary Research Questions ........................................................................... 6
3 How does the campus community get to campus?.................................................. 7
3.1 Average Vehicle Ridership............................................................................... 7
3.2 Mode Split........................................................................................................ 9
3.3 Peak Periods vs Non- Peak Period Travel ...................................................... 10
3.4 Where are travelers coming from?................................................................. 10
3.4.1 Which travelers are coming from within Davis and outside of Davis? ..... 10
3.4.2 How are within Davis travelers traveling compared to their out of town
counterparts? ........................................................................................................ 12
3.5 What is the relationship of distance to mode choice?..................................... 14
3.5.1 Parking Demand .................................................................................... 18
4 What are the main barriers for people to walk, bike, bus, and carpool more to get to
campus? ....................................................................................................................... 22
5 Are people trying to make a difference through their transportation choices? ........ 29
5.1 Level of Concern............................................................................................ 31
5.2 Personal Actions............................................................................................ 31
5.3 Actions of UC Davis Travelers as a Group..................................................... 32
6 Evaluation of Existing Programs............................................................................ 34
7 Greenhouse Gas Emissions from the Daily Commute ........................................... 39
8 Target Areas for Reducing Carbon Emissions and Increasing AVR at UC Davis... 42
8.1 Campus Growth Will Likely Lead to Increased Carbon Emissions Without
Intervention ............................................................................................................... 42
8.2 The UC Davis Community is Concerned about Collective Transportation
Problems................................................................................................................... 43
8.3 LIM convenience is the gold standard of goals, across jurisdictions............... 44
8.4 The Future of the UCD Commute Carbon Footprint ....................................... 44
8.5 Get more people living within Davis, preferably close to or on- campus.......... 47
8.6 Get more people who live outside of Davis to use transit/ carpool/ vanpool
instead of driving alone.............................................................................................. 48
8.6.1 Aggressive Travel Demand Management .............................................. 49
8.6.2 Development of Lower Impact Mode Networks ...................................... 50
8.6.3 Implement Bike Stations at Transit Centers ( Davis, MPOs).................... 51
8.7 Get more people within Davis to walk/ bike/ bus .............................................. 51
8.7.1 Education/ Training Programs................................................................. 52
8.7.2 Cultural/ Promotional Programs .............................................................. 54
9 Appendix II: Differences between the Spring ‘ 07 and Fall ’ 07 Surveys.................. 55
10 References............................................................................................................ 56
ii
List of Figures
Figure 3- 1 AVR by Roles................................................................................................. 8
Figure 3- 2 AVR by Roles ( Condensed) ........................................................................... 8
Figure 3- 3 Primary Mode Split ......................................................................................... 9
Figure 3- 4 Percent of Travelers Living on Campus, with Davis, and Outside of Davis ... 11
Figure 3- 5 Mode Split of Residents within Davis ( Includes On- Campus Housing) ......... 13
Figure 3- 6 Number of Commuters by Primary Mode and Location ................................ 13
Figure 3- 7 Mode Split by Location ............................................................................. 14
Figure 3- 8 Number of Travelers by Mode by Mile – Within 6 Miles of Campus.............. 15
Figure 3- 9 Average Miles from Campus by Mode and Role ( in Davis)........................... 15
Figure 3- 10 Number of Travelers by Mode by Mile: Outside of Davis ( 5- 35 miles from
Campus) ....................................................................................................................... 16
Figure 3- 11 Distribution of Residential Distance in Miles by Role .................................. 16
Figure 3- 12 Distribution of Residential Distance in Miles by Role ( Condensed) .... 17
Figure 3- 13 Number of Travelers within 5 Miles of Campus by Role ............................. 17
Figure 3- 14 Number of Travelers Greater than 5 miles from Campus by Role............... 17
Figure 3- 15 Type of Long- Term Parking Permit Purchased by Each Role..................... 18
Figure 3- 16 Number of Parking Permits Purchased by Each Role................................. 19
Figure 3- 17 How often have you bought a single- use parking permit ($ 6) in the last 6
months? ........................................................................................................................ 20
Figure 3- 18 Parking On- Campus vs Off- Campus .......................................................... 20
Figure 3- 19 On- Campus Parking Location .................................................................... 21
Figure 3- 20 On- Campus Parking Location Breakdown by Commute Location............... 21
Figure 4- 1 Total Number of Travelers by Mode by Mile ( including On- Campus
Residents): Davis ........................................................................................................ 22
Figure 4- 2 Cycling Skill.................................................................................................. 23
Figure 4- 3 Number of Drivers within Davis Who Live Too Far Away to Bike by Distance
from Campus................................................................................................................. 24
Figure 4- 4 Average Frequency of Commute Errands per Person per Week by Mode.... 27
Figure 5- 1 Fall 2007 Summary Statistics for Collective Action on Transportation Related
Problems....................................................................................................................... 33
Figure 5- 2 Summary of Changes between the Spring ‘ 07 Survey and the Fall ‘ 07 Survey
............................................................................................................................... ...... 33
Figure 6- 1 TAPS Carpooling Program: Awareness, Usage, and Experience................. 34
Figure 6- 2 Discounted transit passes ( transit pool program).......................................... 34
Figure 6- 3 Emergency ride home service for carpool and transit/ train users ................. 35
Figure 6- 4 24 free daily parking days ( per year) for carpoolers, trainpoolers, and
transitpoolers................................................................................................................. 35
Figure 6- 5 Online Ridematching ( find a carpool partner) Service................................... 35
Figure 6- 6 Trainpool ...................................................................................................... 36
Figure 6- 7 Transitpool ................................................................................................... 36
Figure 6- 8 Yolo TMA Commuter Club ........................................................................... 36
Figure 6- 9 www. sacregion511. org................................................................................. 37
Figure 6- 10 Interest in In- Vehicle Parking Meters that charge by the minute ................. 37
Figure 6- 11 Interest in Hourly Car Rental ...................................................................... 38
Figure 7- 1 Average Commute Distance for Single Occupancy Vehicle ( SOV) Users by
Role .............................................................................................................................. 39
Figure 7- 2 Total Daily Commute Miles for Single Occupancy Vehicle ( SOV) Users by
Role .............................................................................................................................. 39
Figure 7- 3 Total Daily CO2 Equivalent Emissions for SOV, Carpool ( CP), and Bus Users
by Role.......................................................................................................................... 40
iii
Figure 7- 4 Per Capita Daily CO2 Equivalent Emissions for SOV, Carpool ( CP), and Bus
Users by Role................................................................................................................ 41
Figure 8- 1 Additional Commuters to UC Davis between 2001 and 2016 ....................... 43
Figure 8- 2 Mode Split – in terms of miles traveled by mode at UC Davis........................ 45
Figure 8- 3 Mode Split by Location................................................................................. 47
Figure 8- 4 Percentage of Travelers Walking by Distance from Campus........................ 51
Figure 8- 5 Percentage of Travelers Biking by Distance from Campus........................... 52
Figure 8- 6 Percentage of Taking the Bus by Distance from Campus............................. 54
List of Tables
Table 2- 1 Sample Size and Response Rate.................................................................... 3
Table 2- 2 Campus Population and Confidence Interval ................................................... 4
Table 2- 3 Sample Weights .............................................................................................. 4
Table 2- 4 On- Campus Population ................................................................................... 5
Table 2- 5 Main Campus Travel Screener ........................................................................ 6
Table 3- 1 UC Davis Mode Split by Affiliation Fall 2007.................................................... 9
Table 3- 2 Timing of First Trip to Campus Based on Weekly Trips ................................. 10
Table 3- 3 Number of Commuters Living in Davis, On Campus, and Outside of Davis ... 11
Table 3- 4 Number of Commuters by Mode within Davis ( Includes On- Campus) ........... 12
Table 3- 5 Mode Split of Residents within Davis ( Includes On- Campus Housing) .......... 12
Table 3- 6 Average Miles from Campus by Mode and Role ( in Davis, All Travelers) ...... 16
Table 3- 7 Odds Against Purchasing a Permit by Role................................................... 18
Table 3- 8 Number of Long- Term Parking Permits Purchased by Each Role ................. 19
Table 4- 1 Number of working bicycles available in household....................................... 22
Table 4- 2 Number of working motor vehicles available to household ............................ 22
Table 4- 3 Distance between home and campus ( in miles) ............................................ 23
Table 4- 4 Average commute time ( in minutes) .............................................................. 23
Table 4- 5 I am very skilled at riding a bike..................................................................... 23
Table 4- 6 I live too far from campus to ride a bike ......................................................... 24
Table 4- 7 I regularly need more cargo capacity than a bike can provide ....................... 24
Table 4- 8 I don't ride a bike when it's raining................................................................. 24
Table 4- 9 I don't ride a bike when it's hot outside .......................................................... 25
Table 4- 10 I don't ride a bike when it's cold outside....................................................... 25
Table 4- 11 I don't want to arrive on campus sweaty ...................................................... 25
Table 4- 12 My job requires that I wear professional clothing ......................................... 25
Table 4- 13 The style of clothing I prefer is inconvenient for biking................................. 25
Table 4- 14 Riding a bike on campus ............................................................................. 25
Table 4- 15 Riding on a road with no bike lane............................................................... 25
Table 4- 16 Riding on a road with a bike lane................................................................. 25
Table 4- 17 Biking on a bike path ................................................................................... 26
Table 4- 18 More bicycle- friendly dress code ................................................................. 26
Table 4- 19 Locked box on campus in which I can store my bike ................................... 26
Table 4- 20 Low cost emergency rides home ................................................................. 26
Table 4- 21 After hours emergency flat tire repair........................................................... 26
Table 4- 22 Bike racks on Unitrans................................................................................. 26
Table 4- 23 Average Frequency of Errands.................................................................... 27
Table 4- 24 Children, age under 6.................................................................................. 27
Table 4- 25 Children, age 6- 15....................................................................................... 28
Table 4- 26 Youth, age 16- 17........................................................................................ 28
Table 4- 27 Total Adults, age 18- 65 ............................................................................... 28
Table 4- 28 Elderly, age 65 or older ............................................................................... 28
iv
Table 4- 29 Number of Years at UCD............................................................................ 28
Table 5- 1 Concern about Air pollution ........................................................................... 31
Table 5- 2 Concern about Traffic congestion.................................................................. 31
Table 5- 3 Concern about Global warming ..................................................................... 31
Table 5- 4 Concern about Dependence on non- renewable energy................................. 31
Table 5- 5 Personal Efficacy to Reduce Air pollution ...................................................... 31
Table 5- 6 Personal Efficacy to Reduce Traffic congestion............................................. 31
Table 5- 7 Personal Efficacy to Reduce Global warming ................................................ 32
Table 5- 8 Personal Efficacy to Reduce Dependence on non- renewable energy............ 32
Table 5- 9 Group Efficacy to Reduce Air pollution .......................................................... 32
Table 5- 10 Group Efficacy to Reduce Traffic congestion............................................... 32
Table 5- 11 Group Efficacy to Reduce Global warming .................................................. 32
Table 5- 12 Group Efficacy to Reduce Dependence on non- renewable energy.............. 32
Table 8- 1 Target Areas for Reducing Green House Gas Emissions and Potential
Strategies..................................................................................................................... 46
Acknowledgements
This report is the product of the work of several campus departments, both administrative
and academic. The authors would like to thank Ernie Hoftyzer, Christina Adamson, Ning
Wan, and Bowen Li from ITS, the Roberta Devine Duo, Matt Dulcich, Camille Kirk, and
Chris Didio from ORMP, Linda Durst and support staff from the Campus Data
Warehouse, Ken Komoto and others from the Registrar’s office, Desiree Longoria, Becky
Vidales, David Takemoto- Weertz, and Cliff Contreras from TAPS for their consistent
and timely assistance, and members of the TAAG, TPAC, TPWG, and TAPS Bicycle
committees for their insightful input and feedback. Thank you to the City of Davis
Public Works Department, especially Tara Goddard and Bob Clarke. This work would
not have been possible without the support of Mark Lubell, Pat Mokhtarian, and Ken
Kurani. Lastly, there were many of our colleagues who contributed throughout the
research project, especially Alex Mandel, Jonathan Woolley, Nanako Tenjin, Carrie
Okma, Daniel Fink, Lauren Hilliard, Jon Li, Wei Tang, and Per Tonn.
1
1 Introduction
For both transportation planners and travelers, collective issues such as traffic and
parking congestion, sprawl, air quality, oil dependence, global warming, and more
recently obesity, have become important concerns. These collective problems are all
exacerbated by increasing suburbanization and the accumulation of individuals’ choices
to predominantly use automobiles. In the U. S., “ walking and cycling for transportation
has declined by about 40 percent since 1977, to approximately 6 percent of total trips,
while nearly 65 percent of Americans are currently either overweight or obese” ( SACOG
2008). In the Sacramento Metropolitan Region, daily peak period congestion has grown
from 17 percent of the region’s urban freeways in 1993 ( 27 out of 160 miles), to 38
percent in 2006 ( 61 miles) – the trend is only projected to increase ( SACOG 2008).
Though automobiles and suburbanization may confer high benefits to the individuals who
choose to utilize them ( Deakin 2008), they have also produced large unanticipated social
costs which has led planners, advocates, and governments worldwide to consider and
promote “ Lower Impact Modes” [ p. 66 ] ( OECD 1996).
Lower Impact Modes ( LIMs) include biking, busing, carpooling, taking a train, walking,
telecommuting, and any combination of these. They are low impact modes by virtue of
their relative per capita energy consumption, pollution production, road and parking
footprint, and safety risks vis a vis single occupant automobiles. These impacts are often
external to the private costs and benefits of choosing between different modes, so they
must be addressed through public policy and/ or collective action, not just in the market
place. The City of Davis and the University of California have become increasingly
concerned about these problems and so are promoting LIMs and seeking support,
cooperation, and participation from community members.
Previous research about the future of UC Davis campus travel has noted that “ many of
the negative side effects of exclusive dependence on automobile travel, such as air
pollution, congestion, and parking stress, would be reduced as more people shift some of
their trips to lower impact modes”( TAPS 2002). However, this purported shift is
contrary to current trends. In recent years an opposite shift has occurred – the number of
people using bikes in the city has reportedly been on the decline for over a decade
( Bicycle Advisory Commission and Public Works Department 2006). Even international
scholars studying the anomalous success of Davis have noted that the car continues to be
more convenient than lower impact modes in Davis:
“ Barriers to walking and cycling in Davis are… lack of a safe infrastructure, particularly safe
crossings Downtown… The Davis City Council has not been able ( wanted?) to implement
pedestrian streets, parking restrictions or other measures, which are regarded as negative
towards the car. Secondly, the alternative modes available and the time, cost and ease of using
these, govern mode choice. The car is a superior mode for most people for most journeys,
even in Davis.”
-- Anders Langeland, “ Sustainable Transport in Davis”
World Transport Policy and Practice, Vol. 13- 2 ( Langeland 2007)
2
Yet in spite of this, Davis remains a special city, in no small part because of the
university, and in large part because of the collective mobilizations of its active citizenry
over the last half century ( Buehler 2007). For those who have heard of Davis and its
bicycles, the city stands as lighthouse above the sea of U. S. bicycle usage ( Moritz 1997;
Pucher, Komanoff et al. 1999), yet as we have demonstrated, its role as a guiding light is
in danger. The majority of cycling in Davis is done by students, faculty, and staff of UC
Davis, as they represent around 40% of the population of the city. The university is the
largest employer in the city - in fact, it’s the largest employer in Yolo County. People
travel from disparate parts of northern California to work at UC Davis, some from the
Bay Area, many from all corners of the Sacramento region. At present, more than half of
those employed at the university live outside of Davis, while almost 20% of the
undergraduate students travel to campus from outside of Davis. Because the university
continues to grow rapidly according to the directives of the state while the city has
adopted a slow growth policy, the portion of campus affiliates who commute from
outside of Davis is projected to rise. This report is a snapshot of the commute patterns of
UC Davis campus affiliates in the fall of 2007. It is the best available picture we have to
illustrate, understand, and scrutinize our community’s current travel choices.
The initiation of this project is partly the outgrowth of efforts by the California Student
Sustainability Coalition, which lobbied for the UC system to have a comprehensive
sustainability policy. As a result, the University of California has developed such a
policy, the UC Policy on Sustainable Practices, within which the university adopted
specific policies to pursue more sustainable transportation. In this document, the UC
Office of the President called for campuses to collect average vehicle ridership data
( AVR) 1 with the aim of reducing fuel consumption, and to collect data on mode split and
commute distance in order to analyze the effect of location on mode choice. The policy
also calls for ongoing involvement of graduate and undergraduate students in efforts
toward achieving sustainable campus transportation. This project spawned our local
effort to meet those goals at UC Davis, while also aiming to provide valuable data about
and for the UC Davis community. It is hoped that the report will inform policy decisions
to improve access to UC Davis while reducing dependence on fossil fuels and emissions
of greenhouse gases.
This is the first annual survey of UC Davis campus travel, following a pilot effort in
spring 2007. The ongoing project is a collaborative effort of the Sustainable
Transportation Center ( STC) of the Institute of Transportation Studies ( ITS),
Transportation Parking Services ( TAPS), and the Office of Resource Management and
Planning ( ORMP) at UC Davis. This assessment of campus travel provides a baseline
measurement of the campus Mode Split for academic year 2007- 2008, and is conducted
on an annual basis by students, under the guidance of Professor Susan Handy.
1 Average vehicle ridership ( AVR) is a measure of the proportion of travelers using modes other than
driving alone. It is calculated by dividing the total number of people arriving on campus by the number of
private automobiles arriving on campus. It is therefore the average number of people traveling per private
vehicle to campus. Increased use of carpools would increase AVR for a given community of travelers.
3
2 Methods
2.1 Survey Design
We sampled the campus population using a stratified sample of email addresses in order
to represent the following groups: freshmen, sophomores, juniors, seniors 2 , Masters
students, PhD and post- docs ( taken together as a group), faculty, staff, and administration.
The target population was all people affiliated with UC Davis who traveled regularly to
the central campus. Travelers were contacted via e- mail during the spring quarter and
invited to a web- based survey. Survey invitations were distributed by the UC Davis
postmaster via email and included a link to an online survey. Survey reminders were sent
to non- respondents once a week for two weeks following the survey.
2.2 Sampling Plan
The total initial sample size was 13,770 people ( 10,539 students and 3,231 employees).
We used a disproportionate random sample, meaning a different share of the population
was included in the sample of each stratum. This approach produces close to a +/- 5%
confidence interval with a 95% confidence level for each strata. An ideal sample size for
each strata subpopulation was calculated using a standard sample size formula including
a finite population correction. Because response rates can reduce sample sizes
significantly, a majority of administrators was included in the sample.
The survey was completed by 1438 employees and 2411 students, yielding of a response
rate of 44.5% for employees, 22.9% for students, and 28.0% overall.
Table 2- 1 Sample Size and Response Rate
Role invited responses response rate
Freshmen 1808 476 26.3%
Sophomores 1765 384 21.8%
Juniors 1805 386 21.4%
Seniors 1830 369 20.2%
Masters Students 1570 300 19.1%
PhD & Post- Docs 1761 496 28.2%
Faculty 1340 496 37.0%
Staff 1448 724 50.0%
Administration 443 218 49.2%
Students ( summed) 10539 2411 22.9%
Employees ( summed) 3231 1438 44.5%
Overall 13770 3849 28.0%
2 We added all the “ other” undergraduates ( Post Baccalaureates, etc.), numbering 362 in total, to the
population of senior students.
4
Table 2- 2 Campus Population and Confidence Interval
Role Population 3
Confidence
Interval
Freshmen 4527 4.25%
Sophomores 4891 4.80%
Juniors 5703 4.82%
Seniors 8547 4.99%
Masters Students 1873 5.19%
PhD & Post- Docs 3660 4.09%
Faculty 2073 3.84%
Staff 8888 3.49%
Administration 430 4.67%
Students ( summed) 29201 0.76%
Employees ( summed) 11391 1.46%
Overall 40592 1.50%
The confidence levels listed in Table 2- 2 mean that our survey statistics for each of the
groups are within +/- the percent of whatever measurement we report from the survey 4 .
For example, we later reveal that our survey indicates that 37.64% of the overall campus
population rides a bike to campus as their primary means of transport – looking up at
Table 2- 2, we see that the confidence interval is 1.5% for “ Overall”, which means that
our results suggest the actual percentage of people biking lies somewhere between
36.15% and 39.15% ( 37.64- 1.5= 36.15% and 37.64+ 1.5= 39.15%).
2.3 Population Weighting
Sample weights were calculated by dividing the number of employee and student
respondents by the total numbers of employees and students estimated to commute to the
main campus. Weights varied by the number of cases available for each analysis. For
our AVR, Mode Split, and most other estimates, we used the following weighting scheme.
Table 2- 3 Sample Weights
Role Population Respondents Weight
Freshmen 4527 476 10.35927
Sophomores 4891 384 10.70241
Juniors 5703 386 13.41882
Seniors 8547 369 22.37435
Master's Students 1873 300 6.022508
PhD's Students 3660 496 8.061674
Faculty 2073 496 4.327766
Staff 8888 724 7.635739
Administration 430 218 6.056338
Total 40592 3849 10.54612
3 Employee counts come from ORMP, student totals come from the Registrar.
4 To be precise, the number we measure would be between these numbers at least 95% of the times that we
would perform the survey, since they are at the 95% confidence level.
5
The employee population figure from which we drew our weighting factors for the AVR
and mode split figures come from ORMP’s official population statistics for the on-campus
population, but for student strata, we used information from the campus
registrar’s office.
Table 2- 4 On- Campus Population5
Category 2006/ 07 Fall 2007
Faculty 6
Ladder Rank 1,459 1,486
Faculty- other ( not ladder rank) 653 587
Total 2,111 2,073
Staff 7,8
Academic Support 4 2,120 2,068
Senior Management 28 24
MSP 414 406
SSP 9 6,811 6,820
Total 9,372 9,318
Total Employees 11,483 11,391
Students 10
Undergraduates 22,059 23,067
Post- baccalaureate 132 126
Graduate Academic and Professionals 5,411 5,556
Total Students 27,602 28,749
Revision date: December 3, 2007
The survey attempted to target travelers to the main campus, as opposed to UC Davis
affiliates who travel to other locations such as the UCD Medical Center or other research
locations outside of Davis. To make sure that our sample only includes these types of
folks, we included a screener as the first question in our survey. The results of the
screener indicate that we were largely successful in our targeting ( see Table 2- 5).
5 Campus and Davis area only. Data is consistent with annual publication UC Davis Total On- and Off-
Campus Headcount Population Annual Averages distributed by the UC Davis Office of Resource
Management and Planning.
6 Includes without salary designations. Annual averages for faculty and staff represent averages of October
and April snapshot figures.
7 Includes “ Affiliated” such as co- op extension ( in Davis), ANR ( in Davis), etc.
8 Such as Academic Administrative Officers, Librarians, Research, Post- Docs, etc.
9 Includes most staff categories and job titles.
10 Annual averages for students represent Fall- Winter- Spring quarter averages ( or in the case of Law, Fall-
Spring semester averages).
6
Table 2- 5 Main Campus Travel Screener
Regularly work or go
to classes in Davis?
Role No Yes
Freshman 3.20% 96.80%
Sophomore 0.43% 99.57%
Junior 1.17% 98.83%
Senior 0.26% 99.74%
Master's 2.24% 97.76%
PhD 1.75% 98.25%
Faculty 1.69% 98.31%
Staff 1.98% 98.02%
Administrator 4.19% 95.81%
2.4 Primary Research Questions
1. How does the campus community get to campus?
In Section 4, we explore survey questions related to the how of campus travel, to gain an
understanding of the overall picture of the campus community’s travel choices. We
report and discuss the following measurements: Average Vehicle Ridership ( AVR), mode
split, and travel during peak travel periods vs off- peak periods, compare travelers from
within Davis and outside of Davis, and analyze the relationship between distance and
mode choice in general.
2. What are the main barriers for people to walk, bike, bus, and carpool more to
get to campus?
In this survey, we sought to explore bicycling in Davis more thoroughly. We included a
set of questions for all Davis residents related to bicycling, which we report in Section 5.
Given the group of campus travelers who live in Davis, what are the differences between
those who drive alone and those who use lower impact modes?
3. Are people trying to make a difference through their transportation choices?
As in the spring 2007 survey, we asked questions about how mobilized the campus
community was regarding community- level transportation- related problems. Since the
spring survey did not break down responses by role categories except students and
employees, a few select questions were repeated to observe differences between campus
roles. The results are presented in Section 6.
4. How do people feel about the campus’ transportation programs?
Every year, we hope to measure awareness and usage of TAPS programs, as the
university continues to adapt and improve its programs in response to feedback from the
survey and campus planning groups. The results of these questions are presented in
Section 7.
7
3 How does the campus community get to campus?
3.1 Average Vehicle Ridership
AVR is an index of what share of people are using alternative modes of travel. It is a
measure of the total number of people traveling to the campus divided by the number of
personal vehicles traveling to the campus ( the personal vehicles category doesn’t include
buses, but does include single occupancy vehicles, carpools, vanpools, and motorcycles).
If everyone drove alone to the campus, the AVR would be 1. The more people carpool,
take the bus, walk, or bicycle to campus, the larger the AVR becomes.
The AVR calculation was performed according to " Rule 2202 – On Road Motor Vehicle
Mitigation Options: Compliance Forms" from the South Coast Air Quality Management
District’s website. 11 Adjustments to the raw numbers were made only for the number of
telecommuting trips, the number of Zero Emission Vehicle trips, and compressed work
week scheduling in a manner consistent with the compliance form. No off- peak or other
credits were included for the calculation of the UC Davis AVR. Because carpooling
respondents may or may not be part of the same carpool, we estimated the “ total” number
of carpoolers by multiplying the number of carpool trips by the average carpool size.
Students living on campus were excluded from the analysis to match the methodology
used by other UC campuses.
Figure 3- 1 AVR Calculation Summary
To calculate our AVR, we followed the instructions from the SCAQMD AVR compliance forms used by
the southern UC Campuses. For inputs into these forms, we used data from questions 3.0.1.1 ( Time of
Day), 3.0.1.2 ( Daily Travel Mode), 3.0.1.3 ( Reason Not Traveled), 3.1.2.1 ( Carpool Size), and 3.1.3.1
( Type of Vehicle).
We exclude all of our cases who do not work/ go to class in Davis and all who live on campus 12 from the
analysis ( 15.7% of total travelers were excluded).
For FACULTY, STAFF, and ADMIN we adjusted for compressed work week and other days off. Students’
AVR are not adjusted.
Lastly, we applied weighting by role when calculating the overall AVR.
11 Specifically, " Section IV- 1. AVR Verification Process" starting on page 5. See:
http:// www. aqmd. gov/ trans/ doc/ regform/ all_ registration. pdf
12 We exclude those on campus because that is the preferred method at other UC Campuses. However,
since the purpose of AVR is to show the success of alternative transportation and one strategy to achieve
this is on- campus housing, we believe AVR should include students, faculty, and staff living on campus.
8
Figure 3- 1 AVR by Roles
4.53
7.89
5.09
4.39
5.31
4.33
2.57
1.66 1.53
0
1
2
3
4
5
6
7
8
9
Freshmen
Sophomores
Juniors
Seniors
Masters
PhDs
Faculty
Staff
Administrators
Campus- wide peak AVR is 4.17 passengers per vehicle, indicating that over three
quarters of trips made to campus are made using an alternative mode, a slight
improvement from the spring quarter’s estimate ( 3.87).
Figure 3- 2 AVR by Roles ( Condensed)
5.31
4.66
1.82
0
1
2
3
4
5
6
Undergraduates Graduates Employees
For undergraduate students AVR was 5.31, and for graduate students it was 4.66. For
employees, the AVR of 1.82 indicates that just over half of employee trips are drive alone
trips, a slight improvement over the spring quarter’s assessment ( 1.72). While AVR is a
common measure for the success of alternative transportation programs, it is less
informative than mode split ( explained below).
9
3.2 Mode Split
Bicycling, driving, busing, etc. are all different modes of travel, and mode split ( also
called mode share) is the breakdown of commute choices in a population. We will look
at the mode split of UC Davis as the proportion of the total number of commute trips to
campus estimated to be made by each mode of travel. These estimates are based on
reported modes of travel to campus over a five day period, with respondents being asked
to report their first trip to the campus 13 for each day of the previous week of travel.
Figure 3- 3 Primary Mode Split
Bike
38%
Drive
28%
Bus
18%
Other
1%
Carpool
5%
Multimodal
6%
Walk
4%
Table 3- 1 UC Davis Mode Split by Affiliation Fall 2007
Role Drive Bus Bike Walk Carpool Other
Multi-modal
Freshman 3.74% 7.24% 74.08% 8.41% 1.40% 0.92% 4.20%
Sophomore 10.53% 44.96% 31.15% 2.62% 3.73% 0.66% 6.35%
Junior 18.40% 32.08% 33.73% 4.71% 3.53% 0.70% 6.84%
Senior 21.99% 27.23% 32.99% 4.97% 4.97% 0.53% 7.33%
Master's 28.70% 6.82% 47.92% 5.20% 3.25% 1.30% 6.82%
PhD 20.33% 6.48% 57.82% 4.68% 4.02% 1.33% 5.34%
Faculty 43.74% 1.69% 39.10% 2.95% 7.30% 1.45% 3.77%
Staff 58.24% 3.63% 20.19% 2.07% 10.61% 0.95% 4.32%
Administrator 63.49% 2.79% 18.37% 0.00% 11.16% 1.40% 2.79%
Undergraduate 15.31% 28.31% 40.52% 5.07% 3.69% 0.67% 6.42%
Graduate 23.16% 6.60% 54.47% 4.86% 3.76% 1.32% 5.84%
Employee 55.79% 3.24% 23.57% 2.15% 10.03% 1.06% 4.16%
Campus- wide
Overall
27.76% 18.32% 37.64% 4.22% 5.48% 0.87% 5.70%
13 Students often return home several times a day.
10
In terms of the number of trips, bicycling continues to be the most popular form
of transportation at UC Davis, followed by the automobile, the bus, multimodal travelers,
carpooling, and walking. Students bike the most, with over 40% of them bicycling,
almost 30% taking the bus, 15% driving, 5% walking, and 4% carpooling. The student
biking contingent is led by freshmen at 74%, followed by a wide margin by PhD students
at almost 60%, master’s students at almost 50%, and the rest of the undergraduates are
relatively close to one another at just above 30%.
Out of the employees, faculty bike the most at nearly 40% ( more than most
undergraduates), followed by staff at 20%, and the administration at around 18%. As far
as driving alone, the administration tops out at over 60%, followed closely by staff with
just under 60%, while less than 45% of faculty drive alone. Carpooling, however, is led
by the administration ( 11.1%), holding a slight lead over staff ( 10.6%), and faculty
( 7.3%). We discuss the mode split in greater detail in the conclusion ( p. 42).
3.3 Peak Periods vs Non- Peak Period Travel
The majority of travelers come to UC Davis during the hours of 6 and 10am ( over 60%
for all roles), with the administration and staff being the most regular. Monitoring peak
vs non- peak travel is mostly of concern for mitigating traffic congestion and parking
congestion.
Table 3- 2 Timing of First Trip to Campus Based on Weekly Trips
Monday through Friday
Role
Not scheduled
this day
Between 6am and
10am
Before 6am and
after 10am
Freshman 4.8% 63.4% 31.8%
Sophomore 3.2% 67.9% 28.9%
Junior 7.2% 70.4% 22.4%
Senior 7.4% 60.7% 31.9%
Master's student 16.8% 60.6% 22.6%
PhD student 10.9% 71.4% 17.8%
Faculty 9.1% 80.9% 10.1%
Staff 5.4% 88.7% 6.0%
Administration 3.1% 93.0% 3.9%
Total 6.9% 71.7% 21.4%
3.4 Where are travelers coming from?
3.4.1 Which travelers are coming from within Davis and outside of Davis?
For the following analyses, we distinguish between travelers on campus, within Davis,
and outside of Davis. Around 75% of the campus population lives on campus or within
Davis, with almost 15% on campus and around 60% within Davis. The 25% commuting
from outside of Davis are mostly staff. Over half of staff and administrative commuters
live outside of Davis, while the majority of all other commuters live predominantly in
11
Davis. Over 80% of undergraduate students live within Davis ( including on- campus),
over 60% of graduate students and faculty, and over 40% of staff and administration.
Table 3- 3 Number of Commuters Living in Davis, On Campus, and Outside of Davis
Live in Davis Live on Campus Live Outside of Davis
Role Number
Percent of
Total
Population
Number
Percent of
Total
Population
Number
Percent of
Total
Population
Freshman 239 5.3% 3760 83.1% 529 11.69%
Sophomore 4324 88.4% 310 6.3% 259 5.30%
Junior 4428 77.6% 550 9.6% 725 12.71%
Senior 6578 77.0% 559 6.5% 1410 16.50%
Master's student 1162 62.0% 145 7.7% 566 30.24%
PhD student 2402 65.6% 661 18.1% 596 16.30%
Faculty 1402 67.6% 0 0.0% 672 32.42%
Staff 4009 45.1% 0 0.0% 4880 54.91%
Administration 206 47.9% 0 0.0% 224 52.11%
Total 14 24750 61.0% 5985 14.7% 9866 24.31%
Figure 3- 4 Percent of Travelers Living on Campus, with Davis, and Outside of Davis
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Freshman
Sophomore
Junior
Senior
Masters
PhD
Faculty
Staff
Administration
outside
davis
campus
14 These counts slightly underestimate the real total, as these are counts of only those cases which could be
geocoded ( about 90% of all cases).
12
3.4.2 How are within Davis travelers traveling compared to their out of town
counterparts?
The bicycle is the most used form of travel for all roles of Davis residents except
sophomores and the administration ( Table ; Figure 3-). It is interesting to note that
bicycling rates go up from sophomores to seniors after the initial drop- off in the rate from
freshmen moving off campus. Future surveys will show whether this is a cohort effect,
due to younger people cycling less on average, or perhaps reveal that students that stay in
Davis tend to bike more every year they stay on past sophomore year.
Table 3- 4 Number of Commuters by Mode within Davis ( Includes On- Campus)
Role Drive Bus Bike Walk Carpool Other
Multi-modal
Total
Freshmen 52 249 2963 331 41 41 145 3905
Sophomores 321 1852 1359 118 171 21 225 4078
Juniors 564 1610 1758 242 94 27 295 4603
Seniors 940 1969 2573 380 201 45 403 6511
Master's students 157 90 789 90 24 24 36 1210
PhD students 339 202 1919 161 73 16 64 2814
Faculty 359 26 766 52 65 9 9 1290
Staff 1306 221 1626 130 260 31 76 3665
Administration 97 0 79 0 12 6 0 194
Total 4135 6219 13832 1504 941 220 1253 28270
Table 3- 5 Mode Split of Residents within Davis ( Includes On- Campus Housing)
Role
Did Not
Travel
Drive Bus Bike Walk Carpool Other
Multi-modal
Freshman 2.1% 1.3% 6.4% 75.9% 8.5% 1.0% 1.0% 3.7%
Sophomore 0.3% 7.9% 45.4% 33.3% 2.9% 4.2% 0.5% 5.5%
Junior 0.3% 12.3% 35.0% 38.2% 5.3% 2.0% 0.6% 6.4%
Senior 0.0% 14.4% 30.2% 39.5% 5.8% 3.1% 0.7% 6.2%
Master's student 0.0% 13.0% 7.4% 65.2% 7.4% 2.0% 2.0% 3.0%
PhD student 1.4% 12.0% 7.2% 68.2% 5.7% 2.6% 0.6% 2.3%
Faculty 0.3% 27.8% 2.0% 59.4% 4.0% 5.0% 0.7% 0.7%
Staff 0.4% 35.6% 6.0% 44.4% 3.5% 7.1% 0.8% 2.1%
Administration 0.0% 50.0% 0.0% 40.7% 0.0% 6.2% 3.1% 0.0%
Undergrads 0.6% 9.8% 29.7% 45.3% 5.6% 2.7% 0.7% 5.6%
Grads 1.0% 12.3% 7.3% 67.3% 6.2% 2.4% 1.0% 2.5%
Employees 0.4% 34.2% 4.8% 48.0% 3.5% 6.5% 0.9% 1.7%
Overall 0.6% 14.6% 22.0% 48.9% 5.3% 3.3% 0.8% 4.4%
13
Figure 3- 5 Mode Split of Residents within Davis ( Includes On- Campus Housing)
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Freshman
Sophomore
Junior
Senior
Master's student
PhD student
Faculty
Staff
Administration
Drive
Bus
Bike
Walk
Carpool
Other
Multimodal
The uniqueness of Davis can be seen in Figure 3- 5 below with over 15,000 people biking
to work or classes on campus, over 6500 taking the bus, and only around 4500 driving.
Within Davis, over 40% of commuters primarily use a bicycle to get to campus and over
25% use the bus ( Figure 3- 6).
Figure 3- 6 Number of Commuters by Primary Mode and Location
0
2000
4000
6000
8000
10000
12000
On- Campus In Davis W/ Out Campus Outside of Davis
Drive
Bus
Bike
Walk
Carpool
Other
Multimodal
14
Figure 3- 7 Mode Split by Location
0%
10%
20%
30%
40%
50%
60%
70%
80%
On- Campus In Davis W/ Out Campus Outside of Davis
Drive
Bus
Bike
Walk
Carpool
Other
Multimodal
3.5 What is the relationship of distance to mode choice?
Distance estimates come from those respondents in our sample who selected their home
location on a map, provided an address or cross street, or lived on campus. For those off-campus,
we calculated the geocode- based network distance for each, whereas those on
campus were simplified to a distance of zero. When they are weighted to represent the
whole population they sum to about 90% of the total. As a result, while the following
graphs of distances are representative, the total number of travelers is slightly
underestimated on the graphs. 15
As shown in Figure 3- 8, the majority of travelers within Davis who live off campus
commute less than four miles. Cyclists and bus riders average around two miles away
from the campus. Walkers live just over a mile on average from campus, with no walkers
beyond three miles. Most carpoolers and those who drive alone to campus within Davis
live between two and three miles away.
15 We double checked the cases missing geocoding by using their own estimates of how far from campus
they live, and role and modesplit distribution patterns closely resemble the geocoded cases.
15
Figure 3- 8 Number of Travelers by Mode by Mile – Within 6 Miles of Campus16
0
1000
2000
3000
4000
5000
0 1 2 3 4 5 6
Drive
Bus
Bike
Walk
Carpool
Other
Multimodal
Figure 3- 9 Average Miles from Campus by Mode and Role ( in Davis)
0.00 0.50 1.00 1.50 2.00 2.50 3.00
Drive
Bus
Bike
Walk
Carpool
staff
faculty
grads
undergrads
16 The trend lines in the following charts are for illustrative purposes only, the area under the curves are not
equal to the total number of travelers by each mode – this would be found by summing the measurements
from each mile marker.
16
Table 3- 6 Average Miles from Campus by Mode and Role ( in Davis, All Travelers)
Other Multimodal Drive Carpool Bus Bike Walk
Distance ( miles) 18.47 18.13 15.63 14.72 3.18 1.48 0.79
Figure 3- 10 Number of Travelers by Mode by Mile: Outside of Davis ( 5- 35 miles from Campus)
0
50
100
150
200
250
300
350
400
450
500
5 10 15 20 25 30 35
Drive
Bus
Bike
Walk
Carpool
Other
Multimodal
Most travelers outside of Davis live within 10- 20 miles away, in nearby cities. However,
there are also a non- significant number traveling around 100 miles ( see gray segments in
Figure 3- 11), most commuting from the Bay area; a large number of these commuters use
multiple modes such as BART and Amtrak.
Figure 3- 11 Distribution of Residential Distance in Miles by Role 17
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Freshman
Sophomore
Junior
Senior
Master's student
PhD student
Faculty
Staff
Administration
Total
101
100
50
40
30
20
15
10
5
4
3
2
1
0
17 In the following two graphs, 0 indicates on campus, 1 indicates 0 to 1, 2 is 1 to 2, etc. up to 100.
However, 101 indicates 101 up to 156.
17
Figure 3- 12 Distribution of Residential Distance in Miles by Role ( Condensed)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Undergrads Grads Faculty Staff
101
100
50
40
30
20
15
10
5
4
3
2
1
0
Figure 3- 13 Number of Travelers within 5 Miles of Campus by Role
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Undergrads
Grads
Faculty
Staff
Figure 3- 14 Number of Travelers Greater than 5 miles from Campus by Role
0
200
400
600
800
1000
1200
5 25 45 65 85 105
Undergrads
Grads
Faculty
Staff
18
3.5.1 Parking Demand
Parking on Campus
Figure 3- 15 suggests that more than 500 additional undergraduates choose to purchase a
C- Permit every year starting after their freshman year, however this may partly be an
artifact of cohort size. Adjusting for this, we see that 1 out of every 36 freshmen has a
permit, 1 out of every 6.4 sophomores, 1 out of 4.5 juniors, and 1 out of every 4 seniors
has a permit ( Table 3- 7). These ratios are not far off from the AVR measures calculated
in Section 3.1.
Figure 3- 15 Type of Long- Term Parking Permit Purchased by Each Role
0
250
500
750
1000
1250
1500
1750
2000
2250
A C CP2A CP2C DSA L
Freshman
Sophomore
Junior
Senior
Master's
PhD
Faculty
Staff
Administration
Permit Types
A ( Faculty and staff) C ( Faculty, staff, and students)
CP2A ( Two person carpool, A permit) CP2C ( Two person carpool, C
permit)
CP3A ( Three or more person carpool, A permit) CP3C ( Three or more person
carpool, C permit)
DSA ( Disabled) GP ( Vanpool)
K ( Cuarto resident exception) L ( Remote lot)
M ( Motorcycle) N ( Night)
RT ( Retiree) V ( Vendor)
Visitor
Table 3- 7 Odds Against Purchasing a Permit by Role
Frosh Soph Junior Senior Master's PhD Faculty Staff Admin
35.9 6.4 4.5 3.9 4.0 4.9 1.8 1.7 1.4
19
There were over 13,000 long- term parking permits being used during Fall 2007,
according to our survey. Staff purchased the most long- term permits, followed by
undergraduates, then faculty, grad students, and admin ( Figure 3- 16).
Figure 3- 16 Number of Parking Permits Purchased by Each Role
Freshman
Sophomore
Junior
Senior
Master's
PhD
Faculty
Staff
Admin
0
1000
2000
3000
4000
5000
6000
Table 3- 8 Number of Long- Term Parking Permits Purchased by Each Role
Frosh Soph Juniors Seniors Master's PhDs Faculty Staff Admin Total
A 0 0 0 0 0 0 699 2175 200 3074
C 126 719 1241 2084 424 602 221 2082 49 7549
CP2A 0 0 0 23 6 16 131 483 43 702
CP2C 0 0 0 0 0 0 35 195 6 236
DSA 0 21 0 0 6 25 0 0 0 52
L 0 21 27 92 31 107 39 234 0 552
Total 126 761 1269 2199 467 750 1125 5169 299 12166
( Plus 845 additional respondents who purchased permits but didn’t specify which kind.)
20
Figure 3- 17 How often have you bought a single- use parking permit ($ 6) in the last 6 months?
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Freshman
Sophomore
Junior
Senior
Master's student
PhD student
Faculty
Staff
Administration
Never
Once a month or less
Two to three times a month
Once a week
A few times every week
For undergraduates, purchase of single- use parking permits increases with year in school.
Master’s students purchase the most single- use permits per capita. What a beautifully
symmetric bar chart! We leave it an exercise for the reader to divine the reasons for the
mysterious symmetry.
Parking Location
The most popular parking zones for all commuters are zones 3, 4, 5, and 6 ( Figures 3- 19
and 3- 20). For those from outside of Davis is zone 4, followed by zone 6, zone 3, and
zone 5. For commuters in Davis, parking zones 5 and 6 are both largely utilized, zones 3
and 4 slightly less.
Figure 3- 18 Parking On- Campus vs Off- Campus
0
1000
2000
3000
4000
5000
Freshman
Sophomore
Junior
Senior
MS Student
PhD Student
Faculty
Staff
Admin
On the UCD campus
Within Davis, but not on
campus
Other
21
Figure 3- 19 On- Campus Parking Location
Figure 3- 20 On- Campus Parking Location Breakdown by Commute Location
0
200
400
600
800
1000
1200
1400
1600
1800
Outside of Davis In Davis
Zone 4
Zone 6
Zone 3
Zone 5
Zone 2
Zone 1
Zone 7
Zone 9
Zone 8
22
4 What are the main barriers for people to walk, bike, bus,
and carpool more to get to campus?
In this year’s survey, we focused a section of the survey towards biking in Davis – those
respondents who lived within Davis were prompted with additional questions about their
opinions regarding bicycling. In this section, we focus on travelers who live within
Davis, and the differences between those who use lower impact modes and those who
drive alone. We compare the means of different roles’ answers to questions in the survey
to uncover differences that may prove important to their commute choices.
Figure 4- 1 Total Number of Travelers by Mode by Mile ( including On- Campus Residents): Davis
0
2000
4000
6000
8000
10000
12000
0 1 2 3 4 5 6
Other
Carpool
Walk
Bike
Bus
Drive
Recalling our analysis from Section 3, around 75% of campus travelers live within Davis,
with around half of these bicycling and over 20% taking the bus, with a large number of
these living close to two miles from campus.
Table 4- 1 Number of working bicycles available in household
( 1,2,3, 4= 4 or
more) Walk Bike Bus Carpool Drive Other
Employee 1.8 2.7 1.5 1.7 1.7 2.0
Student 1.4 2.0 1.6 1.6 1.3 1.8
LIM users have more working bicycles than drivers, with cyclists not surprisingly
topping the list. However, it is interesting that employees have more bicycles per
household in general than students.
Table 4- 2 Number of working motor vehicles available to household
( 1,2,3, 4= 4 or
more) Walk Bike Bus Carpool Drive Other
Employee 1.4 1.5 0.9 1.8 1.9 1.5
Student 1.1 1.0 1.0 1.6 1.7 1.3
23
Likewise, drivers have more motor vehicles than LIM users, with students have less
vehicles in general.
Table 4- 3 Distance between home and campus ( in miles)
Walk Bike Bus Carpool Drive Other
Employee 1.31 2.24 2.53 2.70 2.98 7.92
Student .72 1.26 2.64 2.17 2.79 2.51
Table 4- 3 is the geocode- based network distance between people’s homes and the
campus ( not the subjective estimates from question 2.4 of the survey). Drivers travel
about half a mile further to campus than LIM users on average, an expected difference
since most of the on- campus population does not drive.
Table 4- 4 Average commute time ( in minutes)
Walk Bike Bus Carpool Drive Other
Employee 18.04 12.53 18.58 10.08 9.59 18.50
Student 12.85 9.38 13.09 12.02 8.90 11.46
While drivers live a half mile further than LIM users on average, they arrive on campus
several minutes sooner than their counterparts do. It is unknown whether this includes
time spent looking for parking, so further research could be done to verify this.
Table 4- 5 I am very skilled at riding a bike
{ 1= Strongly Disagree, 5= Strongly Agree} Walk Bike Bus Carpool Drive Other
Employee 3.9 4.5 3.9 3.9 3.9 4.5
Student 3.7 4.4 4.0 4.0 3.9 4.1
Most travelers believe they are skilled bike riders ( around 80%), with drivers feeling less
skilled on average than their lower impact mode using counterparts. Comparing
perceived cycling skill more closely across roles, we find the following:
Figure 4- 2 Cycling Skill
3.00
3.20
3.40
3.60
3.80
4.00
4.20
4.40
4.60
Drive Bus Bike Walk Carpool
undergrads
grads
faculty
staff
24
Cyclists feel they are the most skilled at bike riding, with faculty who take the bus and
walk as well as grad students who walk think they are the least skilled.
Table 4- 6 I live too far from campus to ride a bike
{ 1= Strongly Disagree, 5= Strongly Agree} Walk Bike Bus Carpool Drive Other
Employee 1.2 1.2 1.6 2.5 2.4 1.6
Student 1.2 1.3 2.5 2.6 2.7 1.8
Most travelers that live within Davis don’t believe they live too far from campus to ride a
bike. Some drivers do think that distance inhibits them from choosing to ride a bike,
which we explore more in detail in Figure 4- 3.
Figure 4- 3 Number of Drivers within Davis Who Live Too Far Away to Bike by Distance from
Campus
0%
10%
20%
30%
40%
50%
60%
70%
0 1 2 3 4 5
0
50
100
150
200
250
300
350
400
450
500
Percent that agree or strongly agree
Number that agree or strongly agree
The above graphs shows that of the 3,586 people who drive three miles or less to campus,
774 believe it is too far to bike. If they shifted to other modes besides driving alone, this
would represent a two percent reduction in drive alone trips overall for the campus. If all
drivers within 3 miles of campus shifted to other modes, this would result in an eight
percent reduction in drive alone trips overall.
Table 4- 7 I regularly need more cargo capacity than a bike can provide
{ 1= Strongly Disagree, 5= Strongly Agree} Walk Bike Bus Carpool Drive Other
Employee 1.9 1.8 2.6 3.3 3.4 2.5
Student 2.0 2.2 2.7 3.0 3.5 2.5
Table 4- 7 illustrates that LIM users generally disagree with this statement, while drivers
are more likely to agree slightly.
Table 4- 8 I don't ride a bike when it's raining
{ 1= Strongly Disagree, 5= Strongly Agree} Walk Bike Bus Carpool Drive Other
Employee 3.4 2.6 4.1 4.2 4.4 3.8
Student 3.7 2.7 4.2 4.0 4.3 3.8
25
Table 4- 9 I don't ride a bike when it's hot outside
{ 1= Strongly Disagree, 5= Strongly Agree} Walk Bike Bus Carpool Drive Other
Employee 2.1 1.5 3.2 3.2 3.1 2.4
Student 2.4 1.7 3.1 2.9 3.2 2.5
Table 4- 10 I don't ride a bike when it's cold outside
{ 1= Strongly Disagree, 5= Strongly Agree} Walk Bike Bus Carpool Drive Other
Employee 2.1 1.6 3.1 3.3 3.3 2.5
Student 2.8 1.8 3.2 3.0 3.3 2.7
Table 4- 11 I don't want to arrive on campus sweaty
{ 1= Strongly Disagree, 5= Strongly Agree} Walk Bike Bus Carpool Drive Other
Employee 3.4 2.6 3.8 3.3 3.9 2.8
Student 3.6 3.0 3.8 3.6 3.8 3.6
Both LIM users and drivers answered as expected in response to these statements. The
two groups have more differences regarding temperature ( hot/ cold).
Table 4- 12 My job requires that I wear professional clothing
{ 1= No Impact, 6= Very Large Impact} Walk Bike Bus Carpool Drive Other
Employee 2.6 2.3 3.3 2.9 3.6 2.8
Student 2.2 1.9 2.4 2.3 2.4 2.2
Table 4- 13 The style of clothing I prefer is inconvenient for biking
{ 1= Strongly Disagree, 5= Strongly Agree} Walk Bike Bus Carpool Drive Other
Employee 2.2 1.8 2.8 2.7 3.1 2.5
Student 2.2 2.0 2.4 2.5 2.8 2.4
LIM users have less impact on biking from clothing preferences, with employee drivers
being the most impacted from professionalism – less than 20% of most campus roles are
concerned about biking in their preferred attire – the only exception is that around 40% of
the administration find it hard to bike in their preferred attire.
We also looked at perceived safety of biking on various types of infrastructure: on-campus,
on streets with no bike lane, on streets with a bike lane, and on grade- separated
bike paths. These results are found in Table 4- 14 to Table 4- 17.
Table 4- 14 Riding a bike on campus
{ 1= Very Unsafe, 5= Very Safe} Walk Bike Bus Carpool Drive Other
Employee 3.3 3.9 3.6 3.8 3.7 4.0
Student 3.7 4.0 3.8 3.9 3.9 3.8
Table 4- 15 Riding on a road with no bike lane
{ 1= Very Unsafe, 5= Very Safe} Walk Bike Bus Carpool Drive Other
Employee 2.3 2.5 2.2 2.0 2.3 2.4
Student 2.2 2.5 2.3 2.4 2.4 2.5
Table 4- 16 Riding on a road with a bike lane
{ 1= Very Unsafe, 5= Very Safe} Walk Bike Bus Carpool Drive Other
Employee 3.6 3.9 3.8 3.6 3.7 4.2
Student 3.6 3.9 3.7 3.7 3.6 3.7
26
Table 4- 17 Biking on a bike path
{ 1= Very Unsafe, 5= Very Safe} Walk Bike Bus Carpool Drive Other
Employee 4.1 4.6 4.3 4.4 4.4 4.6
Student 4.4 4.5 4.3 4.4 4.3 4.3
Non- cyclists feel consistently less safe biking across all types of infrastructure. Only
around 10% of drivers found it safe or very safe on roads without bike lanes. Around 60-
70% of drivers found it safe or very safe to ride on a street with a bike lane as well as
riding on campus. Lastly, bike paths were seen as safe or very safe by the most people –
85- 90% of each group.
We looked at a number of changes to the biking program at UC Davis to gauge what
impact such programs might have on travelers’ decisions to ride a bike. The results are
shown in Table 4- 18 to Table 4- 22.
Table 4- 18 More bicycle- friendly dress code
{ 1= No Impact, 6= Very Large Impact} Walk Bike Bus Carpool Drive Other
Employee 1.9 1.9 2.2 2.6 2.3 2.1
Student 1.7 1.9 1.9 2.1 1.9 1.9
Table 4- 19 Locked box on campus in which I can store my bike
{ 1= No Impact, 6= Very Large Impact} Walk Bike Bus Carpool Drive Other
Employee 2.1 2.0 2.8 2.3 2.2 2.0
Student 2.3 2.3 2.6 2.4 2.3 2.4
Table 4- 20 Low cost emergency rides home
{ 1= No Impact, 6= Very Large Impact} Walk Bike Bus Carpool Drive Other
Employee 2.1 2.4 3.0 2.8 2.7 2.9
Student 2.8 2.8 3.1 3.2 2.7 2.7
Table 4- 21 After hours emergency flat tire repair
{ 1= No Impact, 6= Very Large Impact} Walk Bike Bus Carpool Drive Other
Employee 2.0 2.7 3.1 2.6 2.7 3.2
Student 2.8 3.1 3.1 3.3 2.8 2.9
Table 4- 22 Bike racks on Unitrans
{ 1= No Impact, 6= Very Large Impact} Walk Bike Bus Carpool Drive Other
Employee 1.9 2.4 3.6 2.7 2.2 2.4
Student 3.2 3.4 4.1 3.4 3.2 3.4
It is clear that certain programs particularly encourage drivers to bike more while others
particularly encourage LIM users to bike more. Bus users seem the most encouraged by
various interventions to get them to use bikes. It also looks as if students may be
interested in flat tire repair for their bicycles on campus during non- business hours. This
could be explored more accurately in future surveys.
Lastly in this comparison of different modes related to bicycling within Davis, we look at
some household traveler characteristics that may be important to different modes: the
27
frequency of errands, household lifecycle characteristics, and number of years at UC
Davis. People were prompted with a list of errands which included work- related business,
dropping off/ picking up other family members, meals, social activities, exercise/ working
out, grocery shopping, visiting/ caring for family members, and medical/ dental
appointments.
Table 4- 23 Average Frequency of Errands
{ 0= Not at all, 1= Once a week or less, 2=
Once every few days, 3= Once a day, 4= A
few times a day} Walk Bike Bus Carpool Drive Other
Employee 1.4 1.4 1.3 1.8 1.8 1.2
Student 3.4 3.1 1.1 1.6 1.8 2.1
Drivers run errands more often during their commutes than LIM users on average, yet
student walkers and bikers as a group run more errands on their way to classes than other
groups.
Figure 4- 4 Average Frequency of Commute Errands per Person per Week by Mode
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Drive Carpool Other Multimodal Bike Bus
In Davis
Outside of Davis
Travelers who live in Davis run more errands on average during their commute, led by
drivers and carpoolers.
Different lifecycle stages are likely to have an effect people's mode choice, so
demographic variables related to lifecycle were included in the survey.
8.2: Number of people of each category below are in your household?
Table 4- 24 Children, age under 6
{ 0, 1, 2, 3, 4, 5= 5 or
more} Walk Bike Bus Carpool Drive Other
Employee .10 .24 .09 .33 .13 .22
Student .04 .03 .01 .03 .02 .04
28
Table 4- 25 Children, age 6- 15
{ 0, 1, 2, 3, 4, 5= 5 or
more} Walk Bike Bus Carpool Drive Other
Employee .11 .28 .22 .30 .43 .55
Student .03 .03 .02 .02 .07 .03
Table 4- 26 Youth, age 16- 17
{ 0, 1, 2, 3, 4, 5= 5 or
more} Walk Bike Bus Carpool Drive Other
Employee .00 .06 .00 .03 .10 .16
Student .09 .07 .04 .01 .01 .11
Table 4- 27 Total Adults, age 18- 65
Walk Bike Bus Carpool Drive Other
Employee 1.7 1.9 2.0 2.0 1.7 1.8
Student 2.7 2.8 3.2 3.0 2.7 2.9
Table 4- 28 Elderly, age 65 or older
{ 0, 1, 2, 3, 4, 5= 5 or
more} Walk Bike Bus Carpool Drive Other
Employee .00 .06 .00 .08 .07 .11
Student .02 .01 .00 .02 .01 .02
LIM users’ households mainly consist of adults. In comparison, drivers’ households have
more non- adults, particularly children 6- 17 years old. There also seems to be some
preliminary evidence that people who have children under 6 may be more likely to bike
or carpool than drive alone. This relationship could be looked at in more detail in future
analyses.
The last variable of interest is the number of years employed or enrolled at UC Davis.
In general, drivers have been employed or enrolled at UC Davis for a slightly longer time,
as seen in Table 4- 29.
Table 4- 29 Number of Years at UCD
Walk Bike Bus Carpool Drive Other
Employee 9.6 9.4 6.0 10.5 11.0 9.4
Student 2.2 2.1 2.3 2.4 2.6 2.3
Within this section, we have compared different commute attributes, attitudes, lifestyle
patterns, and some demographic characteristics and how they related to travelers mode
choices within Davis. We found that travel time, distance, weather, perceived safety of
cycling, chauffeuring children, and the duration of time at UC Davis all had a relationship
to which mode travelers chose. Below, we summarize some of the main findings and
questions.
Drivers within Davis tend to live half a mile further away than their lower- impact mode
counterparts yet for employees their commute time is three minutes faster than biking,
eight minutes faster than walking, and nine minutes faster than taking the bus on average.
For students driving is also the fastest way to get campus - although only a minute faster
than biking, four minutes shorter than walking and taking the bus.
29
Somewhere around 80% of the population would say that they are skilled bike rider.
Those who think they are the least skilled at riding bikes are graduate students who walk
to campus, faculty who walk to campus, and faculty who take the bus.
Around 3,500 people drive 3 miles or less to get campus, and almost 800 perceived that
this is too far to bicycle. People who generally drive or take the bus don't ride their bikes
when it's raining, when it's hot outside, or when it's cold outside. People who generally
walk are less averse to inclement weather, however. Almost everyone doesn't want to
arrive on campus sweaty, though cyclists are the least averse to this of all the groups.
Less than 20% of the campus has trouble bicycling in their preferred attire, though 40%
of the administration does.
Regardless of the mode chosen, campus travelers agree that they perceive the grade-separated
bike path as the safest form of infrastructure, followed by roads with a bike
lane, and regular roads. For those who bike, riding on campus is about as safe as riding
on the road with a bike lane. The fact that people who don't regularly cycle perceived all
types of cycling infrastructure as less safe than those who regularly bike suggests that
perceived safety, and possibly a lack of experience with cycling is a major barrier.
Student cyclists, bus users, and student carpoolers may be interested in emergency flat
tire repair after regular business hours. This suggests a few things. First, it suggests that
bicycle maintenance and repair is a barrier to students riding bikes. Secondly, it suggests
a need for bicycle maintenance facilities available on campus at all hours.
It appears that employees who have children tend to drive more when their children are
between the ages of six and fifteen, likely due to chauffeuring children for school and
other activities on the way to and from work. The fact that people who drive to UC Davis
tend to have worked longer than those who use lower impact modes is curious. Could it
just be a factor of age? It is a cohort effect, where younger generations are more likely to
use LIMs? Or is it that the longer one works or goes to school at UC Davis, the more
likely one is to drive?
5 Are people trying to make a difference through their
transportation choices?
Mode choice isn’t just a decision about travel time and associated travel costs; it is also
understood as a lifestyle choice for some people. For example, bicycling for many isn’t
just a form of travel, it also enables and articulates an alternative lifestyle and vision of a
more sustainable culture ( Horton 2006). Standard economic theory currently used for
transportation planning and most policy- making begins with the assumption that people
are “ selfish robots”, and proceeds from there to form prescriptive policies and predictions.
This has worked well enough for looking at human behavior in market settings in the last
few hundred years, but it isn’t an accurate or appropriate model of human behavior for
many other settings ( Henrich, Boyd et al. 2005). When looking to solve community-scale
collective problems in transportation, planners may not only need to call upon their
30
fellow community members to rise above myopic self- interest; we may also need to call
upon our models to account for the same possibility ( Congleton 2008).
Theories of collective action have been used in sociology and political science to explain
protest behavior and social movement participation, including the environmental
movement, efforts to improve air quality, and efforts to reduce global warming
( Klandermans 1984; Finkel 1989; Gibson 1997; Muller 1998; Lubell 2002; Lubell,
Vedlitz et al. 2006; Lubell, Zahran et al. 2007). We have adapted these models to study
collective action in the mode choice setting. 18
This section compares this fall’s survey responses to the Spring Quarter 2007 survey
responses about collective problems related to transportation choices at UC Davis.
Problems addressed in the fall survey include local air pollution, local traffic congestion,
global warming, and national dependence on non- renewable energy. We measure level
of concern for these problems, belief in one’s ability to affect them through personal
action, and belief in the UC Davis community’s ability to affect them through collective
action.
There is the potential for a prosocial bias in people’s answers to survey questions about
collective issues, which can reduce the variance of the answers ( Sjostrom and Holst
2002). Survey questions themselves can generate a norm simply by querying about
norms, so we changed the format of the fall survey relative to the spring survey to
minimize this effect, following the work of Sterngold, et al. ( Sterngold, Warland et al.
1994).
We provided introductory questions to each collective interest- related query, as follows:
7.3.1: Are you concerned about any of the following transportation related issues in your
community or do you feel that they are not really a problem?
( Air pollution, Traffic congestion, Global warming, Dependence on non- renewable energy)
No, they are not really issues to me.
Yes, I am concerned about one or more of these issues.
7.3.3: Do you think your personal actions could improve any of the following transportation issues
by driving less?
No, my personal action cannot improve any of these issues by driving less.
Yes, my personal action can improve one or more of these issues by driving less.
7.3.4: Do you think UC Davis travelers can improve the following transportation issues by driving
less?
No, UC Davis travelers cannot improve any of the issues by driving less.
Yes, UC Davis travelers can improve one or more of these issues by driving less.
If respondents chose “ Yes” for these questions, they were queried further about the extent
to which they were concerned, thought their personal action mattered, or believed that the
group could make a difference. For further background we recommend reviewing
Section 7 of the spring 2007 report 19 .
18 See Chris Congleton’s dissertation for discrete choice model using the survey questions in this section:
Congleton, C. ( 2008). The Collective Calculus of Mode Choice: Are Drivers Free- Riding on Lower Impact
Modes? Davis.
19 found at http:// taps. ucdavis. edu/ surveys/ results/ Spring_ 07_ Travel Assessment_ UCD. pdf
31
5.1 Level of Concern
The community is generally very concerned about collective problems related to
transportation. Over 70 percent of all travelers are concerned or very concerned about
local air pollution, local traffic congestion, global warming, and national dependence on
non- renewable energy.
Table 5- 1 Concern about Air pollution
{ 0= Not at all Concerned,
3= Very Concerned} Walk Bike Bus Carpool Drive Other
Employees 2.19 2.45 2.33 2.45 2.24 2.35
Students 1.99 1.96 1.88 1.94 1.94 1.90
Table 5- 2 Concern about Traffic congestion
{ 0= Not at all Concerned,
3= Very Concerned} Walk Bike Bus Carpool Drive Other
Employees 2.09 2.15 2.17 2.25 2.18 2.32
Students 1.73 1.70 1.67 1.97 1.87 1.68
Table 5- 3 Concern about Global warming
{ 0= Not at all Concerned,
3= Very Concerned} Walk Bike Bus Carpool Drive Other
Employees 2.11 2.47 2.39 2.30 2.08 2.23
Students 1.97 2.00 1.96 1.99 1.95 1.91
Table 5- 4 Concern about Dependence on non- renewable energy
{ 0= Not at all Concerned,
3= Very Concerned} Walk Bike Bus Carpool Drive Other
Employees 2.32 2.55 2.46 2.41 2.25 2.39
Students 1.94 2.04 1.89 1.99 1.99 1.86
Employees are more concerned in general than students, with LIM users more concerned
than drivers. However, employee LIM users and drivers are almost equally concerned
about congestion. All groups have a relatively high level of concern on all issues.
5.2 Personal Actions
Perceptions of one’s ability to affect community- level policies and problems may be
important in determining one’s mode choice. For example, concern and ability to affect
changes in local air pollution or local congestion may motivate people to choose
alternatives rather than drive alone.
Table 5- 5 Personal Efficacy to Reduce Air pollution
{ 0= No Improvement,
3= Large Improvement} Walk Bike Bus Carpool Drive Other
Employees 1.54 1.93 2.36 1.89 1.67 1.93
Students 1.71 1.68 1.45 1.50 1.41 1.34
Table 5- 6 Personal Efficacy to Reduce Traffic congestion
{ 0= No Improvement,
3= Large Improvement} Walk Bike Bus Carpool Drive Other
Employees 1.68 1.92 2.34 1.89 1.62 1.98
Students 1.85 1.79 1.55 1.46 1.45 1.35
32
Table 5- 7 Personal Efficacy to Reduce Global warming
{ 0= No Improvement,
3= Large Improvement} Walk Bike Bus Carpool Drive Other
Employees 1.25 1.72 2.31 1.73 1.46 1.74
Students 1.49 1.49 1.39 1.36 1.32 1.16
Table 5- 8 Personal Efficacy to Reduce Dependence on non- renewable energy
{ 0= No Improvement,
3= Large Improvement} Walk Bike Bus Carpool Drive Other
Employees 1.34 1.81 2.27 1.80 1.59 1.87
Students 1.33 1.50 1.32 1.28 1.31 1.09
While most travelers are concerned about collective problems, few think they can
personally do something about it. Employees are more likely to believe they can make a
difference than students, while LIM users are more optimistic than drivers.
5.3 Actions of UC Davis Travelers as a Group
Perceptions of group efficacy may play a role in shaping people’s mode choice. The
following question measures this concept. Questions are focused on what extent UC
Davis travelers would improve the following transportation issues by driving less than
they do now:
Table 5- 9 Group Efficacy to Reduce Air pollution
{ 0= No Improvement,
3= Large Improvement} Walk Bike Bus Carpool Drive Other
Employees 2.23 2.55 2.81 2.38 2.16 2.48
Students 2.41 2.39 2.21 2.25 2.03 2.05
Table 5- 10 Group Efficacy to Reduce Traffic congestion
{ 0= No Improvement,
3= Large Improvement} Walk Bike Bus Carpool Drive Other
Employees 2.70 2.81 2.96 2.55 2.28 2.60
Students 2.53 2.72 2.53 2.36 2.28 2.41
Table 5- 11 Group Efficacy to Reduce Global warming
{ 0= No Improvement,
3= Large Improvement} Walk Bike Bus Carpool Drive Other
Employees 1.77 2.22 2.67 2.05 1.87 2.18
Students 1.96 1.91 1.84 1.98 1.71 1.59
Table 5- 12 Group Efficacy to Reduce Dependence on non- renewable energy
{ 0= No Improvement,
3= Large Improvement} Walk Bike Bus Carpool Drive Other
Employees 2.13 2.37 2.67 2.07 2.02 2.33
Students 1.98 1.99 1.88 2.03 1.74 1.59
The pattern here is similar to personal action, but there is more optimism for
improvement across all segments. LIM users are more confident in the group’s impact,
and employees are more confident in the group’s impact except for air pollution and
traffic congestion, where students and employees answers are more similar.
33
Figure 5- 1 Fall 2007 Summary Statistics for Collective Action on Transportation Related Problems
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
Student Drivers Student LIMs Employee
Drivers
Employee LIMs Entire
Community
Concerned
Personal efficacy
Group efficacy
71.4% of the entire community is moderately or very concerned about collective
problems posed by transportation. Translating this concern into personal action, only
25.6% believe a medium or large improvement is possible to problems by their individual
choice to drive less. However, almost half ( 45.8%) of the community believes that all
UC Davis travelers working together to drive less can make a medium or large
improvement to collective transportation problems.
Figure 5- 2 Summary of Changes between the Spring ‘ 07 Survey and the Fall ‘ 07 Survey
- 10.0%
- 8.0%
- 6.0%
- 4.0%
- 2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
average concern average personal
efficacy
average group efficacy
Employee LIMs
Student LIMs
Entire Community
Employee Drivers
Student Drivers
Compared to the spring ’ 07 survey, answers changed slightly. The percentage of people
concerned or very concerned dropped by almost 6%, possibly in part due to the changes
we made to the survey to reduce bias. Interestingly, more employees believe they can
make a difference personally than spring ‘ 07, with a 4.3% increase for LIM users and an
8.8% increase for employees who drive alone. Proportionately less student LIM users are
optimistic about their personal ability to make changes – a 4.6% decrease from spring ‘ 07.
34
6 Evaluation of Existing Programs
Overall Knowledge and Usage of Various TAPS Programs
Almost 80% of all undergraduates have never heard of many of TAPS alternative
transportation programs. Almost half of all administration, staff, and faculty are also
relatively unaware. However, knowledge and usage of TAPS programs does increase the
longer one stays at UCD. It is mainly non- students who utilize the programs. Below, we
analyze the awareness level ( percentage of people who have heard of each program), the
success rates for those who have heard of the program ( of those who have heard of the
program, what percentage use it currently?), and lastly, the success rate for those who
have tried it ( of those who have previously used the program, what percentage are still
using it?).
Figure 6- 1 TAPS Carpooling Program: Awareness, Usage, and Experience
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Student Driver Student LIM Employee Driver Employee LIM Total
awareness level
success rate for those who've heard of it
success rate for those who've tried it
Figure 6- 2 Discounted transit passes ( transit pool program)
0%
10%
20%
30%
40%
50%
60%
70%
Student Driver Student LIM Employee Driver Employee LIM Total
awareness level
success rate for those
who've heard of it
success rate for those
who've tried it
35
Figure 6- 3 Emergency ride home service for carpool and transit/ train users
0%
10%
20%
30%
40%
50%
60%
70%
Student Driver Student LIM Employee Driver Employee LIM Total
awareness level
success rate for those who've heard of it
success rate for those who've tried it
Figure 6- 4 24 free daily parking days ( per year) for carpoolers, trainpoolers, and transitpoolers
0%
10%
20%
30%
40%
50%
60%
70%
80%
Student Driver Student LIM Employee Driver Employee LIM Total
awareness level
success rate for those who've heard of it
success rate for those who've tried it
Figure 6- 5 Online Ridematching ( find a carpool partner) Service
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Student Driver Student LIM Employee Driver Employee LIM
awareness level
success rate for those who've heard of it
success rate for those who've tried it
36
Figure 6- 6 Trainpool
0%
10%
20%
30%
40%
50%
60%
70%
80%
Student Driver Student LIM Employee Driver Employee LIM
awareness level
success rate for those who've heard of it
success rate for those who've tried it
Figure 6- 7 Transitpool
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Student Driver Student LIM Employee Driver Employee LIM
awareness level
success rate for those who've heard of it
success rate for those who've tried it
Figure 6- 8 Yolo TMA Commuter Club
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Student Driver Student LIM Employee Driver Employee LIM
awareness level
success rate for those who've heard of it
success rate for those who've tried it
37
Figure 6- 9 www. sacregion511. org
0%
10%
20%
30%
40%
50%
60%
Student LIM Employee Driver Employee LIM Total
awareness level
success rate for those who've heard of it
success rate for those who've tried it
We also queried respondents about their interest- level for in- vehicle parking meters that
charge by the minute, as well as their interest- level for automobile rental by the hour.
Figure 6- 10 Interest in In- Vehicle Parking Meters that charge by the minute
0%
10%
20%
30%
40%
50%
60%
No Yes I'm not sure
Undergraduate
Graduate
Employee
Few campus travelers are interested in trying in- vehicle parking meters ( less than 15%),
but over 40% are not sure if they are interested in the service. The details of the service
would probably need to be stated to gauge interest more accurately.
38
7.4b Hourly car rental
Figure 6- 11 Interest in Hourly Car Rental
0%
10%
20%
30%
40%
50%
60%
70%
No Yes I'm not sure
Undergraduate
Graduate
Employee
Automobile rental by the hour was of interest to more travelers – over 20% of students
and over 10% of employees. An additionally 25% were not sure, probably also because
the details of the service would be important to their interest level.
39
7 Greenhouse Gas Emissions from the Daily Commute
We provide a rough comparative estimate of greenhouse gas emissions generated by
different campus roles. We use the distance each commuter travels from home to campus
and their primary mode 20 to make an estimate of their daily commute emissions.
Figure 7- 1 Average Commute Distance for Single Occupancy Vehicle ( SOV) Users by Role
0.0
5.0
10.0
15.0
20.0
25.0
Freshmen
Sophomores
Juniors
Seniors
Master's Students
PhD's Students
Faculty
Staff
Administration
In the following figure we report the total sum of commute miles by single occupancy
vehicle drivers for each role.
Figure 7- 2 Total Daily Commute Miles for Single Occupancy Vehicle ( SOV) Users by Role
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
Freshmen
Sophomores
Juniors
Seniors
Master's Students
PhD's Students
Faculty
Staff
Administration
20 This introduces a small amount of error for commuters who use different modes of transport on different
days of the week, but was done in this fashion in the interest of time. Mode split calculated using primary
mode vs the actual number of trips per week closely approximated one another, so we expect that the error
with this substitution should also not be too significant.
40
In order to estimate the greenhouse gas ( GHG) emissions from different roles, we must
use estimates of emissions per mile for different modes. For single occupancy vehicles,
we use 1.3 Lbs of CO2 equivalent per mile, for carpools we use the SOV estimate divided
by our average carpool size ( 2.56 people per car) to get 0.51 Lbs of CO2 equivalent per
mile, and for the bus we use an estimate for a relatively full transit bus around 0.3 Lbs of
CO2 equivalent per mile 21 .
Figure 7- 3 Total Daily CO2 Equivalent Emissions for SOV, Carpool ( CP), and Bus Users by Role
0
25000
50000
75000
100000
125000
150000
175000
200000
Freshmen
Sophomores
Juniors
Seniors
Master's Students
PhD's Students
Faculty
Staff
Administration
SOV Lbs of CO2 Equivalent per Day
CP Lbs of CO2 Equivalent per Day
BUS Lbs of CO2 Equivalent per Day
Staff commuters provide the largest share of emissions by far ( over 47%), followed by
senior students ( at 15%). This is partly due to the large number of staff and the fact that
over half of them live outside of Davis.
If we compare roles by emissions per capita ( see Figure 7- 4), we find that Administrators
have the highest emissions per person, followed closely by Staff, Faculty, and then
Master’s students.
21 All GHG emission per mile estimates are adapted from calculations made by the Sightline Institute:
http:// www. sightline. org/ maps/ charts/ climate- CO2byMode
41
Figure 7- 4 Per Capita Daily CO2 Equivalent Emissions for SOV, Carpool ( CP), and Bus Users by
Role
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Freshmen
Sophomores
Juniors
Seniors
Master's Students
PhD's Students
Faculty
Staff
Administration
SOV Miles per Capita
CP Miles per Capita
BUS Miles per Capita
Overall, we estimate that campus commuters to the main campus emit a total of 488,321
lbs ( 222 metric tons) equivalent of CO2 per travel day, with over 90% of this coming
from single occupancy vehicle drivers traveling to the campus from outside of Davis.
We estimate that the total annual emissions from campus commuters is on the order of
50,000 metric tons per year.
Lastly, we note that air travel is a large and unaccounted for contributor to the campus
carbon footprint, including faculty and student travel to conferences and research sites, as
well as administrative travel associated with the university. While not part of the daily
commute to campus, these travels are university- related and as such deserve to be
measured and accounted for in the campus carbon footprint.
42
8 Target Areas for Reducing Carbon Emissions and
Increasing AVR at UC Davis
This concluding section begins with a forecast of campus growth over the next 7 years
and its effect on the carbon footprint of the campus. We introduce commute miles
rather than trips as the appropriate metric for measuring the success of travel
demand management programs. With this metric in mind, we list three broad target
areas to reduce the carbon footprint of UCD travelers, outlined in Table 8- 1:
1. increase the proportion of travelers living in Davis or on campus,
2. increase the use of alternatives to driving outside of Davis, and
3. increase the use of lower impact modes within Davis.
We suggest potential strategies within each target area, based on relevant data from our
survey to determine existing barriers and levers for change.
8.1 Campus Growth Will Likely Lead to Increased Carbon
Emissions Without Intervention
In this report, we have examined the travel choices and some of the opinions of current
UC Davis affiliates. While a majority of those coming to the main campus currently
commute from within Davis or on campus, the university anticipates more growth in the
coming years as mandated by the state. Additionally, the Sacramento Area is home to
about a million people and is expected to double in population by 2035, which amounts
to another 34,000 people a year ( SACOG 2008).
Given the current planning scenario, we estimate that over 64% ( 9,700) of the additional
15,200 travelers to UC facilities in Davis by 2016 will live off- campus ( Mohr 2008) and
probably outside of Davis as well ( see Figure 8- 1 below). If they do, the resulting growth
would change the overall mode split of the campus to increase driving alone by 4%,
reduce bus use by 4%, and bicycling by 2% ( assuming mode splits remain the same as
they are now on campus, within Davis, and outside of Davis).
43
Figure 8- 1 Additional Commuters to UC Davis between 2001 and 2016 22
97%
13%
0%
36%
3%
88%
100%
64%
0%
20%
40%
60%
80%
100%
Students Employees Non- UC Employed
Visitors
Total Growth
on campus
off- campus
While we do not calculate the estimated carbon emissions from this growth, it is clear
that increases in driving alone and reductions in biking and busing will also increase the
carbon emitted. Unless the new growth is on or near campus, unless the proportion of
driving trips decreases, the collective carbon emissions from campus commuters will
only increase.
8.2 The UC Davis Community is Concerned about Collective
Transportation Problems
The vast majority of people are concerned about global warming and other collective
problems related to personal travel. Over 71% of the entire community is moderately or
very concerned about collective problems posed by their transportation choices.
However, most do not think they can personally or collectively have a significant effect
on these problems, but some do: over 25% believe a medium or large improvement to
problems is possible by their individual choice to drive less, and almost half ( 46%) of the
community believes that all UC Davis travelers working together to drive less can make a
medium or large improvement to collective transportation problems. Why not more than
this? In transportation, there is a gap between exercising a personal choice, largely based
on personal convenience ( travel times, travel costs, etc.), and communities exercising a
collective choice, based on social costs and benefits. This gap is determined by the
ability of social institutions or groups to incentivize and coordinate collective action.
Our survey provides evidence that most travelers do not believe that their personal
decisions have an effect on other peoples’ decisions when it comes to commuting; i. e.
they do not perceive that their choice of mode is a strategic decision. Yet as
transportation analysts we know that it is – the costs of transportation, both private and
social ( such as parking costs, transit fare, congestion delays, bicycle and pedestrian safety,
etc.) are all affected by the proportion of people choosing each mode. Coordinating these
22 These numbers include West Village.
44
choices on a collective basis has a have huge effect on the convenience of each choice.
But how does this coordination happen?
Social institutions are the bridge between the individual and the collective, and rational
collective choices leading to socially optimal outcomes are possible when social
institutions are able to coordinate individual behaviors in such a way that individual
choices can make a difference to collective outcomes. When the private costs and
benefits of personal choices are equal to the social costs and benefits of those choices, an
economically efficient and socially optimal outcome will occur. Our social institutions
are the arbiters of these private costs and benefits in transportation, and it is up to these
institutions to determine what these costs should be. In the context of campus travel, the
university is the primary social institution to make changes in the private costs and
benefits that travelers see, although governance from the federal government down can
affect these costs and benefits also.
8.3 LIM convenience is the gold standard of goals, across
jurisdictions
According to modeling using our survey data ( Congleton 2008), the overall convenience
of different choices largely determines their use by people, while collective
considerations represent a significant yet much smaller fraction of determinants of the
choice. Thus, the relative convenience of mode choices is incredibly important for
achieving transportation policy goals. For this reason, we recommend all relevant
jurisdictions cooperate in a coordinated fashion to achieve a single long term goal:
make the convenience of lower impact modes equal or greater to that of
driving. The collective public vision at each jurisdiction will determine how this can be
accomplished, with the SACOG BluePrint Project being a principle example of how this
vision can be constructed on a community- level within local jurisdictions. These
jurisdictions include but are not limited to the federal government, the State of California
( and the California Department of Transportation), regional Metropolitan Planning
Organizations ( the Sacramento Area Council of Governments is our local MPO), the
University of California, Yolo and adjacent counties, the City of Davis, and UC Davis
respectively.
8.4 The Future of the UCD Commute Carbon Footprint
Right now, near 50,000 metric tons of CO2 per year, UC Davis probably has a smaller per
capita carbon footprint than many other universities of its size in the US. This is in large
part due to its high share of bicycle commuters ( 38% of total trips) and transit users
( 18%). The car- free campus, the bike infrastructure of the city, the incredible success of
the student- run Unitrans local transit system, all are likely to contribute greatly to making
the UC Davis mode split what it is. But as we saw in the last chapter on greenhouse gas
emissions, the campus’s commute carbon is a function of the distance people live from
campus and the emissions per mile of their commute choice.
If we look at how commute miles are distributed by mode ( Figure 8- 2), rather than just
commute trips, we find that 56% of the total commute miles to UC Davis are miles
driven alone, with only 16% of total miles being traveled by bus or bicycle.
45
Figure 8- 2 Mode Split – in terms of miles traveled by mode at UC Davis
drive
56%
multimodal
14%
carpool
11%
walk
bus 1%
8%
bike
8%
other
2%
If we really want to reduce carbon emissions of the campus commute in the long term,
we would do better to pay attention to increases in the percentage of lower impact mode
miles, not just the percentage of lower impact mode trips, as a metric of our success.
This provides a more accurate measurement of our success in reducing our collective
carbon emissions from commuting.
Currently, over half of the commute miles to UC Davis are driven alone, 14% are
multimodal, meaning that people use a combination of modes to get to campus 23 , 11% are
in carpools, and bike and bus are tied around 8% of overall miles, other and walking
together account for the remaining 3%. What if taking the bus, riding the bike, walking
the foot, and carpooling together accounted for 50% of total commute miles? What
would that look like for the City of Davis and the campus? We leave it to the reader to
imagine such a world. Right now, we live in a world where lower impact modes total to
28% of commute miles to UC Davis.
If we are to increase the share of lower impact mode miles, how are we to get from this
world of 28%, to that potential world of 50% or more in the future? That question must
be answered by the UC Davis community. We suggest the three different target areas in
Table 8- 1 as a starting point, and provide some beginning discussion points on these
target areas below.
23 In this survey, this includes train riders and UCD Medical Center and UCD- UCB shuttle riders, but it
also includes people who drive a pickup truck to Davis and pull a bike out of the back and ride it onto
campus. Further post- hoc research will distinguish between these different users in our survey, but we
don’t perform this above.
46
Table 8- 1 Target Areas for Reducing Green House Gas Emissions and Potential Strategies
Targets Possible Strategies
Get more people living within Davis,
preferably close to or on- campus
• West Village project ( UC Davis)
• Incentives for residential infill projects
downtown and in Central Davis
( Davis)
• Mileage- based Employee Relocation
Incentives ( UCD, SACOG, CA, Fed)
Get more people who live outside of Davis
to use transit/ carpool/ vanpool instead of
driving alone
• TAPS Individualized Marketing
Programs ( TAPS)
• Increasing Partnership with Yolo Bus,
SacRT, Amtrak, etc. ( TAPS)
• Aggressive Travel Demand
Management ( TAPS, Davis, SACOG)
• Regional Lower Impact Mode
Network Development ( Yolo County,
SACOG, DOT, Davis, Woodland)
• Educational Programs on Travel Costs
and Land- Use/ Transportation
• Increase walking and cycling access to
transit ( MPOs)
• Bike Stations at Transit Centers
( Davis, MPOs)
• Increasing Regional Park and Ride
Lots and Express Bus Service targeting
UCD Travel Clusters ( TAPS)
• Increase Train Incentives to reduce
train cost to that of auto ( UC, TAPS)
• Increase( Begin?) Funding of
Educational and Promotional Programs
( Federal government, State of
California, UCD)
Get more people within Davis to
walk/ bike / bus
• Education/ Training Programs ( UCD,
Davis, SACOG)
• Cultural/ Promotional Programs ( UCD,
Davis, SACOG)
• Financial incentives ( e. g. unlimited bus
passes for employees and grad
students) ( UCD)
• Reduced or complimentary parking
passes for occasional drivers who
bicycle ( TAPS)
47
8.5 Get more people living within Davis, preferably close to or
on- campus
Increasing the proportion of people who work and study at UC Davis and live in Davis or
on- campus is an effective long- term strategy for reducing the University’s commute
carbon. Why? The competitiveness of LIMs increases as the distance between a
traveler’s residence and the main campus decreases. Since commute times within Davis
are relatively similar for most modes ( Remember from fig x that the commute times for
driving, busing, biking, and walking, only vary from 12 to 18 minutes, respectively),
other cost/ benefit considerations can come into play besides time. Conversely, few
considerations are as important as time when looking at options when travelers live
further away from campus. Recalling Figure 3- 6 ( Duplicated in Figure 8- 3, below), we
can see the difference in the practical choices available to people based on where they
live. This underscores the importance of distance from campus and the relative travel
times of different choices available to community members.
Figure 8- 3 Mode Split by Location
0%
10%
20%
30%
40%
50%
60%
70%
80%
On- Campus In Davis W/ Out Campus Outside of Davis
Drive
Bus
Bike
Walk
Carpool
Other
Multimodal
For most people who live outside of Davis, walking and cycling are not an option,
although a surprising number of people still bike from outside of Davis to the campus
( we estimate at least 40 people currently bike 7 miles or more from outside of Davis,
including faculty, staff, graduate, and undergraduate students) 24 . What this means is that
getting a greater proportion of UCD affiliates to reside on or near campus will translate
directly into reducing the carbon footprint of the campus.
24 See http:// www. davisbicycles. org/ for information about a locally made film about bicycle commuting
between Davis and Sacramento called “ Zen and the art of Bicycle Commuting”.
48
The UC's sustainable transportation policy pledges that UC Campuses will “ continue
their strong commitment to provide affordable on- campus housing, in order to reduce the
volume of commutes to and from campus. These housing goals are detailed in the
campuses' Long Range Development Plans". The West Village Project is a prime
example of dense housing development right on campus, and along with additional dorm
infill, will result in accommodating 97% of the anticipated student growth at UC Davis to
2016 right on campus. However, West Village will only accommodate 12.5% of
anticipated staff growth at UC Davis as noted in Figure 8- 1 above. Unless additional
housing is provided on- campus or within Davis somehow, UC Davis affiliates will likely
contribute to significant growth in transportation- related problems. This housing
development could be driven by policy, whether incentives for residential infill projects
downtown and in Central Davis, or whether UC Davis follows its sustainable
transportation policy, not just with its growing student population, but also with their
associated staff and research affiliates. Another strategy for the campus might be to
create a mileage- based employee relocation incentive package. This would provide
employees who commute the furthest to get to campus an incentive to move closer to
campus, thus significantly reducing their commute distance. This program could also be
done through SACOG or at state level in cooperation with other large employers in the
region.
8.6 Get more people who live outside of Davis to use
transit/ carpool/ vanpool instead of driving alone
Another strategy for reducing the carbon created by campus commuters is to get a greater
proportion of people who live outside of Davis to carpool, vanpool, and take transit,
including the train where applicable.
While 95% of those who live in Davis report they live near a bus stop, only 44% of those
who live outside of Davis do. Unitrans only provides local service, so bus usage drops
severely for affiliates who reside outside city limits. Besides the lack of availability for
many people, another one of the reasons for this seems fairly straightforward. According
to our survey, those who are able to take the bus to get to campus from outside of Davis
have to travel times 1.7 times longer on average than if they had driven the same distance.
This time cost likely prohibits most people from choosing anything but driving when they
live far away.
The UC’s official sustainable transportation policy states,
" By January 2009, each campus will implement a pre- tax transit pass program to facilitate the
purchase of transit passes by University employees, or will establish a universal access transit pass
program for employees.", and campuses must “ engage in advocacy efforts with local transit districts
to improve routes in order to better serve student and staff ridership."
-- Policy Statement and Guidelines for Implementation
Just like within Davis, the competitiveness of regional lower impact mode options
increases as the difference in travel time between a LIM and driving decreases. In the
case of regional travel, this could be accomplished by transit with express service to UC
Davis from common employee residence clusters. Using the geocoded data from this
49
survey, TAPS can identify clusters of campus commuters that could be served by a
vanpool, express bus to campus, and/ or regional park and ride lots for Amtrak and other
transit. This information could be communicated with Yolo Bus, SacRT, Amtrak,
SACOG, etc. to improve the coordination of services offered by these groups.
Another innovation that could be attempted is to initiate a morning carpool/ vanpool
“ parade” where all carpools and vanpools could follow designated one- way routes
through the central campus between 7am and 7: 30am to drop off their members close to
their place of work before the driver parks the vehicle. This makes sense in the morning
because there is significantly less bicycle and foot traffic at this time, and the “ door to
door” service could make carpooling more competitive.
8.6.1 Aggressive Travel Demand Management
Another part of the UC's sustainable transportation policy’s purpose is to " Incorporate
alternative means of transportation to/ from and within the campus to improve the quality
of life on campus and in the surrounding community.” Likewise, SACOG plans to invest
in educational and promotional programs for Travel Demand Management to reduce the
region’s vehicle miles traveled by 10% ( SACOG 2008). Travel Demand Management
( TDM) is a huge field of strategies and techniques with various degrees of
implementation in practice. UC Davis, the City of Davis, SACOG, and cities with UC
Davis affiliates reside must determine what TDM measures are appropriate in their
jurisdiction and how they are to be applied. At the same time, it is likely that
coordination across/ through these jurisdictions could play a key role in determining how
successful any of them are. With these two ideas in mind, we present a number of TDM
strategies that could be discussed within stake- holder communities for each jurisdiction.
Price commute options correctly
As a first step, get pricing information to travelers about social costs and benefits of
different commute choices, as a second, the community can work towards moving
policies so that actual prices of different commute choices reflect their true costs. While
this means adjusting revenue streams for different modes and therefore different agencies
responsible for their conveyance to campus ( Unitrans~ bus, TAPS~ autos), these
adjustments can be made incrementally over time so that large unpredicted changes can
be avoided.
Increase Train Incentives to reduce train cost to that of auto
Train costs could be further reduced for UC affiliates and eventually included as part of a
more general and integrated transit pass, just as UCD undergraduates can use their
student IDs to take Yolobus. This makes sense for a number of UC, California State
University, and Community College schools, as well as other large employers seeking to
reduce their carbon footprint.
Fund Educational and Promotional Programs
For regions and cities, there has been a trend towards federal and state funding of “ shovel
ready” infrastructure projects rather than educational/ cultural/ market based approaches
( Conversation with Tara Goddard, Nov. 2008). However, both the City of Davis and
50
SACOG have educational programs as goals, and UC Davis has several educational
programs in nascent stages of development, some of which are in cooperation with Yolo
County and the local police. These types of programs could have financial support at the
state and federal level through the same process as infrastructure projects. These
programs could educate the community about community- level problems in
transportation, the external costs and benefits of their private travel choices, how to move
to more sustainable lifestyles, etc. Programs could support trainings, events, and creative
mass media such as films, theatre, and art more generally.
Individualized Marketing Programs
When people come to TAPS to purchase a long- term parking pass and provide their
starting commute location and regular commute times ( where applicable), TAPS can
identify the nearest open carpools, vanpools, and transit routes to their home, thus
beginning an individualized marketing process to enroll them in alternatives, monitor
their satisfaction and use of these programs. This type of program allows for all parkers
to be informed about programs, allows those disinterested to opt out with little cost or
hassle, and those interested to learn more about and participate in programs. This type of
program could also be performed at any workplace or location where parking is sold by a
human representative.
8.6.2 Development of Lower Impact Mode Networks
SACOG, Yolo county, Woodland, Davis, and UC Davis are all working together to
research the feasibility of a Neighborhood Electric Vehicle ( NEV) path alongside the
already planned bicycle path between Woodland and Davis. For Woodland commuters
to UC Davis, NEVs are currently not an option, there is simply not a legal route to take a
NEV between Woodland and Davis. In this respect, the proposed NEV route creates a
new commute option, it provides people a new choice which they do not currently have.
For most Woodland commuters, the average travel distance is around 12.8 miles. Based
on self- reported travel times, average travel time for Woodland commuters is 18 minutes,
and the average travel speed would then be 44mph. If we assume that car drivers end up
taking a NEV, and we then assume their average travel speed would drop to say, 20mph-
24mph, and their average travel time to 32- 39 minutes. While this is around double the
commute time, it is still a fairly normal commute time for many people and still faster
than taking the bus. It is not implausible that some will find the benefits of taking a NEV
between Woodland and Davis to outweigh the saved travel time of driving on the freeway.
Separating regional and local travel
Without separation from fast heavy vehicles, walking, biking, and using NEVs will
always be more dangerous on roads that prioritize the movement of cars and allow them
to travel at speeds much higher than walking or biking speeds. The Lower Impact Mode
Network ( LIMnet) in existing cities is simply the collection of roads where legal travel
speeds are 25 mph or less. Paying specific attention to such roads and the network they
create between homes and activity centers, working to separate trunks of these networks
from high speed arterials, and prioritizing LIM traffic where possible on these roads
improves the local travel of LIM users. Most of Davis is a LIMnet already, although
51
there are notable exceptions, such as the 5 th St. corridor. This corridor is perceived as
dangerous and unpleasant to cross by many pedestrians and cyclists in Davis.
Without prioritizing and improving the safety and convenience of Lower Impact Vehicles,
they will continue to present less private utility to the majority of travelers than their
higher impact counterparts. We therefore recommend developing separate networks or
even just designating a strategic subset of existing roads to prioritize LIMs, especially
between residential streets and activity centers.
8.6.3 Implement Bike Stations at Transit Centers ( Davis, MPOs)
The City of Davis has been discussing having a bike station, similar to stations in several
cities across the U. S. 25 , and has been actively researching this option but to date no bike
station exists in Davis or on- campus. This station could be a place for secure overnight
storage of bicycles, a place to purchase bicycle parts and accessories, receive and/ or
perform repairs, and could even teach bicycle repair to the public. It is likely to
encourage more travelers to commute by bike and train rather than drive. Other cities
along the Capitol Corridor route could consider doing the same, including Sacramento,
Fairfield/ Suisun, Martinez, Richmond, and Berkeley.
8.7 Get more people within Davis to walk/ bike/ bus
We saw that living within Davis significantly increases the competitive edge of lower
impact modes. Yet even within Davis, distance has a strong role to play.
Walking
A five minute walk for most people is about a quarter of a mile. UC Davis has many
pedestrians that walk much further than a quarter of a mile to get to work or classes on
campus, but none indicated that they walk more than 3 miles from campus. The walking
modesplit is highest on campus, at almost 12%, and remain relatively high right off
campus, then quickly drops off above a mile away.
Figure 8- 4 Percentage of Travelers Walking by Distance from Campus
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
0 1 2 3 4 5 6
Miles
25 the nearest one being at the Downtown Berkeley BART station.
52
This means that the more people who live within a mile of campus, the greater the share
of trips by walking will be overall. Denser housing close to campus would accomplish
this.
8.7.1 Education/ Training Programs
Bicycling
For cyclists, we see from Figure 8- 5 that the share of biking trips is highest on campus at
over 75%, and the percentage drops about 12% for every mile away from campus, almost
linearly.
Figure 8- 5 Percentage of Travelers Biking by Distance from Campus
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
0 1 2 3 4 5 6
Miles
In our introduction, we noted that bicycling has declined in the city in recent years. The
Bicycle Plan for the City of Davis has recently been updated with a new goal of
increasing bicycle trips within the City of Davis to 25% of all trips by 2012 ( Davis 2008).
In addition, education has been identified as an important goal of the city: “ It is apparent
that the City of Davis must still continue to work hard, particularly by education and
encouragement to increase the level of bike ridership if it is to remain ‘ America’s Best
Cycling City.’” ( Bicycle Advisory Commission and Public Works Department 2006)
[ p3]. Another goal includes maintaining “ an education program to promote bicycle use
and safety,” and investigating “ development and promotion of a monthly ‘ riding tips’
clinic aimed at new riders.” ( Bicycle Advisory Commission and Public Works
Department 2006) [ p6]. Most of the new riders coming into Davis every year appear in
September, about 5,000 of them on average. Most of them have bikes, and no formal
training on how to use or maintain one, and most of them will leave UC Davis within
four to five years, still with no training on how to safely use or maintain a bicycle.
Our survey showed that freshmen have the highest cycling rate at almost 75%, and it
appears that this may decline by over 40% by the time they begin the new school
year as sophomores. If so, this is a huge attrition rate! This phenomenon is worth
investigating more in the future and points very clearly towards the need for a cycling
training and maintenance program during the freshmen year, perhaps even a UC Davis
53
“ core class” in the first quarter of enrollment about safe cycling and basic bike
maintenance. Of those students that stop riding their bicycles before or during their
sophomore year, common reasons the author has heard include having their bike stolen or
having their bike “ stop working”. Hundreds of bikes are abandoned annually on campus,
in city bicycle parking, or stashed behind a bush somewhere in Davis.
Additionally, as seen in Figure 4- 2, drivers, bus users, and walkers feel less skilled at
riding bikes than those who bike regularly to campus do. This raises the question, if there
were classes offered on how to safely ride a bicycle on- campus and within Davis, would
more people use bicycles to get to campus? Further, would the 75% of freshmen who are
riding between classes remain cyclists for the duration of their stay at UC Davis if they
were provided training on how to cycle effectively on regular roads off- campus, provided
with ASUCD- subsidized rain gear, a rear bike rack, and waterproof bike bags that could
carry their books, laptop, and other gear? A small research study answering this question
would be very useful.
Also, while our survey did not ask about bicycle maintenance issues, anecdotal evidence
over eight years points towards the hypothesis that a lack of training in both the use and
maintenance of the bicycle is partly responsible for the attrition rate between the
freshman and sophomore year. Many students stop riding their bikes at the first major
maintenance issue, whether a flat tire, a stuck brake, or a warped wheel, either as
freshmen, or sophomores. It also seems that in this process their attitude towards biking
may change negatively also. This could be studied and understood more clearly in the
future, but perhaps more expediently, it could be avoided through education and training.
The scope of teaching around 5000 new people how to safely ride a bicycle is well within
the abilities of an educational institution like UC Davis. The American League of
Bicyclists Road I training course takes about 9 hours, and the recommended number of
students in a class is 10, so that means the teaching load for instructors is about 1 hour
and 7 minutes of training per student, and for 5000 students, that’s 5556 hours. This
translates to about 14 full- time cycling instructors during the fall quarter in order to meet
the demand. This could easily be translated into 28 part- time student jobs( at $ 10/ hr), all
of whom would become certified League Instructors, at an estimated annual cost well
under $ 60,000, and possibly as low as $ 30,000 ( If class sizes were doubled; the American
League of Bicyclists is flexible on class size for institutions), costs could be cut up to
half.. If funded through ASUCD, this program, educating all freshman about how to
ride a bike safely and make basic roadside repairs, would be an additional annual
cost of around $ 1.25-$ 2.50 per student, and seems likely have a tremendous impact on
campus modesplit.
This discussion points towards the need for an on- campus learning center, possibly
student- run, training students and community members how to safely operate their
bicycles, and additionally how to repair their bicycles. This center could also organize
and oversee a large training program for incoming freshmen every fall. Why does the
bicycle campus in the “ bicycle city” not have such a program already?
54
Unitrans Bus Ridership
For bus, the peak usage is at around two miles, and another peak occurs at 5 miles from
campus via the road network. Geographic analysis needs to be done to see more clearly
where these trips originate from, but it is clear that the bus is only a minor threat to
walking and bicycling within a mile from campus.
Figure 8- 6 Percentage of Taking the Bus by Distance from Campus
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
0 1 2 3 4 5 6
Miles
The UC’s official sustainable transportation policy states,
" By January 2009, each campus will implement a pre- tax transit pass program to facilitate the
purchase of transit passes by University employees, or will establish a universal access transit pass
program for employees." -- Policy Statement and Guidelines for implementation
It is unclear what impact this policy will have at UC Davis. It will be interesting to
monitor staff bus ridership after the policy change.
8.7.2 Cultural/ Promotional Programs
The campus and the city already have a number of successful events both annually and
more intermittently that celebrate, encourage, or otherwise help people to use lower
impact modes, especially the bicycle. However, few of these existing programs
specifically target people who currently drive. The campus could provide bike buddy
programs where experienced bike commuters were partnered with a neighbor less skilled
and/ or less seasoned who wanted to commute by bike, simply by providing an online
matching system similar to the current rideshare program provided by TAPS. This
“ bikeshare” program could be advertised and assembled effectively during the
Sacramento Region’s Bike Commute Month ( during May), in addition to simply being
available year- round. Additionally, the alternative transportation coordinator at TAPS,
the bicycle coordinator, and student environmental and transportation groups can
cooperate more closely on specific campaigns. One example in which this has worked
well in the past was to have student volunteers table in front of parking structures to earn
pledges of drivers to not drive at least one day during the month of May. This type of
program can focus on voluntary changes by those who can have the greatest impact
on transportation problems, single occupancy drivers, especially those living outside of
Davis. It also puts interested drivers directly in touch with those who have the
information and support they need to experiment with not driving, whether for a single
day, or as a more permanent lifestyle goal.
55
9 Appendix II: Differences between the Spring ‘ 07 and Fall ’ 07
Surveys
Section
Question
number Content
Version
Applied ( e/ s,
blank= both) Type
Section
Question
number Content
Version
Applied ( ne/ ns,
blank= both) Type Other Changes/ Comments
2.0 Regular Travel to Campus? s Y/ N 2.0 Regular Travel to Campus? Y/ N
2.0.1 Boilerplate ( No need to complete survey if N on 2.0) - 2.0.1 Boilerplate ( No need to complete survey if N on 2.0) -
2.1 Number of working motor vehicles MC 2.1 Number of working motor vehicles MC
2.2 Live on campus? s Y/ N 2.2 Live on campus? ns Y/ N
2.3 Bus service nearby? MC 2.3 Bus service nearby? MC
2.4 Distance of commute Num 2.4 Distance of commute MC
2.4.1 Name of on- campus residence s Num 2.4.1 Name of on- campus residence ns Num
2.5.0 Screener for 2.5.0a Y/ N
2.5 Special conditions MC 2.5.0a Special conditions MC
2.5.3 Accommodation suggestions Text
3.0.0 Boilerplate for Section 3.0 - 3.0.0 Boilerplate for Section 3.0 -
3.0.1
Form( s) of travel used last week [ switch board for
conditional questions]
MC ( Multiple
Answer) 3.0.1
Form( s) of travel used last week [ switch board for
conditional questions]
MC ( Multiple
Answer) Eliminates “ motorcycle” and “ train”
3.0.1.1 Time of Travel ( peak or non- peak?) MC array 3.0.1.1 Time of Travel ( peak or non- peak?) MC array Eliminates “ Saturday” and “ Sunday”
3.1.1.2 Multi- modal? Y/ N Now condition for 3.0.1.4 to show up
3.0.1.2 Form of travel by day MC array 3.0.1.2 Form of travel by day MC array Eliminates “ Saturday” and “ Sunday”
3.0.1.3 Reason for not traveling to work e MC array 3.0.1.3 Reason for not traveling to work ne MC array Eliminates “ Saturday” and “ Sunday”
3.0.1.4 Details of multi- modal travel Text 3.0.1.4 Details of multi- modal travel Text
3.0.1.5 Number of trips avoided through telecommuting e Num 3.0.1.5 Number of trips avoided through telecommuting e MC
3.0.2.4 Length of commute time Num 3.0.2.4 Length of commute time MC
3.0.5 Primary work/ first class location Text 3.0.5 Primary work/ first class location Text
3.0.6.0-
3.0.6.4 Commute time estimation for various forms of travel MC
3.1.2.1 Carpool Size Num 3.1.2.1 Carpool Size MC
3.1.3 Type of motor vehicle MC
3.1.3.1 Alternative vehicle MC 3.1.3.1 Alternative vehicle MC Eliminates “ Other”
3.1.4 Gas mileage Num
3.1.5 Drop- off location MC 3.1.5 Drop- off location MC
3.1.6
Specific location ( Parking lot Number/ Street& Cross-
Street) Text 3.1.6 Specific location ( Parking zone, with zone map) MC
Shows up only when answered “ on-campus”
on 3.1.5
3.1.7- 3.1.8 Maps of Parking Location on Campus -
3.2.3 Type of bike MC 3.2.3 Agree/ Disagree Statement on biking constraints 5- pt scale array
3.2.4 Brand of bike Text 3.2.4 Level of Impact of dress code on biking 6- pt scale
3.2.5 Bike gear( s) used
MC ( Multiple
Answer) 3.2.5 Bike gear( s) used
MC ( Multiple
Answer)
3.2.6 Incidents of bike stolen Num 3.2.6 Level of safety on various bike facilities 5- pt scale array
3.2.7 Incidents of bike accidents MC
3.2.9 Level of Impact of various programs on Biking 6- pt scale array
3.3.4 Bus system( s) used
MC ( Multiple
Answer) 3.3.4 Bus system( s) used
MC ( Multiple
Answer) Eliminates “ Other”
3.3.5 Bus route( s) used Text
3.5 The Train 3.5.2 Train station where commute begins Text
3.7.1 Type( s) of routine errands
MC ( Multiple
Answer)
3.7 Errands
3.7.1 Screener for 3.7.1.1 Y/ N
3.7.1.1 Frequency of running errands 5- pt scale array 3.7.1.1 Frequency of running errands 5- pt scale No distinction on errand types
6.0 Frequency of travel during workday/ on campus 5- pt scale 6.0 Frequency of travel during workday/ on campus 5- pt scale
Now a condition for 6.1 to show up
( except answering “ Not at all”)
6.1 Form( s) of travel used during workday/ on campus
MC ( Multiple
Answer) 6.1 Form( s) of travel used during workday/ on campus
MC ( Multiple
Answer)
6.3.3 Keep bike at work/ on campus? MC 6.3.3 Keep bike at work/ on campus? Y/ N Eliminates “ I don't know”
6.6 Frequency of purchasing single- use parking permits 5- pt scale 6.6 Frequency of purchasing single- use parking permits 5- pt scale
6.7 Purchased long- term parking permit this year? MC 6.7 Purchased long- term parking permit this year? Y/ N Eliminates “ I don't know”
6.7.1 Type of parking permit purchased MC 6.7.1 Type of parking permit purchased MC
7.4.0 Knowledge of TAPS related programs MC array
7.4a – 7.4b Opinion on two specific TAPS programs MC
7.4c Other TAPS programs used, but not listed Text
3.8.1 Type( s) of people who know about your travel pattern
MC/ MC
( Multiple
Answers) Mixed- up between ns and ne
3.8.2 Number of known person who travel with you Num
3.8.3 Household members who travel with you Num
7.1.1 Level of convenience in using various forms of travel 6- pt scale array
7.1.2 Level of safety in using various forms of travel 5- pt scale array
7.1.4.1.1 Level of stress in using various forms of travel 5- pt scale array
7.1.4.1.2 Level of excitement in using various forms of travel 5- pt scale array
7.1.4.2 Reasons of inconvenience in alternative forms of travel Text
7.1.5.0 Boilerplate for 7.1.5.1- 7.1.9 -
7.1.5.1 Would try bus service if available 5- pt scale
7.1.6- 7.1.9 Level of pride in using various forms of travel 5- pt scale
7.2.1 Boilerplate for 7.2.1b- 7.2.1j -
7.2.1b –