shared wheels in the auto city: evaluating los angeles’ bicycle sharing program

8/12/2019 Shared Wheels in the Auto City: Evaluating Los Angeles Bicycle Sharing Program

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Shared Wheels in the Auto City:

Evaluating Los Angeles Bicycle Sharing Program

A draft final report

to the John Randolph Haynes and Dora Haynes Foundation

Principal Investigator:Dr. Michael Smart

Post-Doctoral Researcher and LecturerComplete Streets Initiative, Lewis Center for Regional Policy Studies

UCLA School of Public Affairs

3250 Public Affairs Building, Box 951656Los Angeles, CA 90095-1656Tel: 917-292-9703Fax: 310-206-5566

Email: [email protected]

and

Carlos HernandezMasters Candidate

UCLA Department of Urban Planning

3250 Public Affairs Building, Box 951656Los Angeles, CA 90095-1656Tel: 818-935-9528

Email: [email protected]

August 2013


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1 Background.........................................................................................................................................................2

1.1 Why the research could not be executed as proposed ................................................................................2

1.2 What this report covers ...............................................................................................................................2

2 Introduction ........................................................................................................................................................4

2.1 What is Bicycle Sharing? ...........................................................................................................................4

2.2 Goals of Bike Sharing ................................................................................................................................4

2.3 Dramatic Increase in Bike Sharing Programs Globally ..............................................................................5

3 Data Collection ...................................................................................................................................................7

4 Analysis ............................................................................................................................................................11

4.1 Demographic Findings .............................................................................................................................12

4.2 Policy-Related Findings ...........................................................................................................................16

5 Statistical Models .............................................................................................................................................20

6 Conclusion ........................................................................................................................................................24

7 Works Cited ......................................................................................................................................................25


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1 BackgroundThis section presents an overview of the research project. First, we explain how the proposed bicycle-

sharing program in Los Angeles (the object of our study) has been delayed, and how this has required us

to refocus the research project for the time being, with hopes of continuing the original work at a later

date once the bicycle-sharing program is implemented.

1.1 Why the research could not be executed as proposedThis final report presents the initial wave of a proposed two-wave travel behavior study in downtown

Los Angeles. The study was designed to test whether individuals living or working in downtown Los

Angeles would exhibit different travel behavior (particularly, more bicycling) after the introduction of a

bicycle-sharing system. The system was slated for roll-out in stages in late 2012 and early 2013, withsubsequent expansions expected. However, the city of Los Angeles and the bike-sharing vendor (Bike

Nation, Inc.) delayed implementation for unknown reasons, and then scaled back the initial system to a

beta testing program of just nine stations (down from four hundred initially proposed) for exclusive

use by city employees (Newton, 2012). Slated for operation in April 2013, this beta-testing program

never opened. Later reports suggested that the bike-sharing agreement between the city of Los Angeles

and Bike Nation was in jeopardy due to existing contractual restrictions on advertising at the bike kiosks

that would severely limit the vendors ability to operate profitably (Christensen, 2013, Newton, 2013).

Because the research could not be executed as proposed, the Haynes Foundation chose to terminate the

grant early. This report presents the findings of the first wave of survey work. The research team

involved in this project hopes to obtain additional extramural funding when the bike-sharing system is

finally implemented. By using the same questionnaire andwhere possiblecontacting the same

participants, we hope to be able to conduct rigorous statistical tests for changes in individuals travel

behavior resulting from the bike-sharing program.

1.2 What this report coversThe original design of this study was a before-and-after test. As the after phase has not yet occurred,

this report presents only the data we obtained from the initial wave of the survey. Because we cannot

test the effect of the introduction of bike-sharing in Los Angeles, we instead focus on the demographics


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of current cyclists in downtown, as well as on the determinants of increased cycling among current

residents. In particular, we focus on demographic influences (such as age, race, and sex) and a small

number of policy-relevant questions, such as automobile parking and the availability of showers at the

workplace.


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2 IntroductionThis section provides an overview of bicycle-sharing in the global and national context. The first and

second sections define bike sharing and explain the goals of bicycle-sharing organizations. The third

section highlights the dramatic rise in bike-sharing programs and subscriptions in recent years and puts

the Los Angeles proposal in perspective.

2.1 What is Bicycle Sharing?Bicycle-sharing programs typically consist of a dense network of bicycle-sharing kiosks in dense urban

areas, from which users can pick up and drop of bicycles using an electronic fob or their credit card.

These kiosks offer short-term, one-way rentals from kiosk to kiosk, and in most cities, short trips (under

30 minutes) are free for members. This arrangement encourages members to use bicycles for short, oftenutilitarian trips within the urban core, rather than for long recreational rides. Figure 1 shows a typical

bike-sharing kiosk. Bicycles themselves typically have amenities such as lighting, baskets, and

adjustable seats, making them a comfortable and useful mode of travel.

2.2 Goals of Bike SharingBicycle-sharing organizations aim to increase the attractiveness of urban cycling by removing one of the

principal barriers to cycling: having a bike handy when you want it. By making bicycles alwaysavailablein the urban core, the Los Angeles bike-sharing program could transform the transportation

profile of the Los Angeles region. Mayor Villaraigosa has touted the program as a way to improve urban

mobility while reducing the citys environmental footprint (Bloomekatz, 2012). Additionally,

proponents see bike sharing as a way to increase the usefulness of the regions public transportation

system by providing connectivity to and from transit stops. Indeed, the dispersed land-use patterns of the

Los Angeles region make this last-mile connectivity particularly troublesome for transportation

planners, and low-cost, convenient bicycle rentals have the potential to increase the reach of transit

greatly (Midgley, 2009). Finally, proponents of bicycle sharing programs see it as a way to promote

public health by encouraging an active form of travel; indeed, the Minneapolis and Denver bike-sharing

programs were even sponsored by health insurers Blue Cross/Blue Shield and Kaiser Permanente

(Newmarker, 2010,Walker, 2010).


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F igure 1. Bicycle Shari ng Kiosk (image: Bike Nation USA)

Scholars are just beginning to assess the impacts of bike-sharing programs, and the results suggest that

the impacts are substantial. In Barcelona, the introduction of a bicycle sharing program more than

doubled the number of bicycle trips in the urban core in the first year, while in Paris, cycling nearly

tripled within one year (Romero, 2008,cited in DeMaio, 2009). Such an increase in cycling benefits not

only the environment, but also public health (Dill, 2009, Pucher et al., 2010)and even traffic safety, as

cycling safety typically increases as the number of cyclists in an area increases (Jacobsen, 2003).

However, these previous studies have only examined the effects of bike sharing programs in dense urban

environments in older cities where automobile use is much lower than in the Southern California

context. Further, previous studies have not used rigorous research methods, and can be considered

anecdotaland often self-reported by the bicycle-sharing agency itself. This study will examine the

impacts of bicycle sharing in Los Angeles, a context that has wide-reaching implications for the

effectiveness of bicycle sharing in auto-dominated urban environments throughout the world.

2.3 Dramatic Increase in Bike Sharing Programs GloballyIn recent years, cities around the globe have introduced bicycle-sharing programs. Bicycle sharing

programs offer short-term, one-way rentals of bicycles, typically blanketing dense urban areas with

bicycle sharing kiosks. While the concept has been around since the mid-1960s (see, for instance,

Constant and Schimmelpenninck, 1970), only since the early 2000s have bike-sharing programs been


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adopted widely, with over 120 programs founded worldwide between 2000 and 2009 (DeMaio, 2009,

Shaheen et al., 2010). While European cities were the first to adopt these systemsParis Vlib system

was an early success (Dell, 2008)cities in the U.S. have more recently begun embracing bicycle

sharing in a big way. Washington, D.C.s Capitol Bikeshare, for instance, has nearly 2,000 bicycles at

over 175 bicycle kiosks in the city and is considered a major success. Each year, the list of cities with

bike sharing systems grows, with cities such as Boston, Denver, Minneapolis, and Kansas City

introducing programs in recent years.

The three largest cities in the countryNew York City, Los Angeles, and Chicagoare currently in the

planning stages for their own bicycle-sharing programs, with the first bicycle kiosks planned to arrive in

those cities in the coming year (Flegenheimer, 2012, Bloomekatz, 2012). Los Angeles bicycle sharing

program is slated to be phased in late 2012 and early 2013 (Flegenheimer, 2012). In this study, I propose

to investigate the effects of Los Angeles bicycle sharing program on travel and activity patterns of those

who live and work near bicycle kiosks. But how exactly will the introduction of bicycle sharing into our

large, auto-oriented metropolitan area influence peoples activity and travel patterns? Will people switch

from driving to cycling and transit, or will bicycling only replace walking trips? Can bike-sharing

programs increase the usage of bicycles and make the region greener and healthier?

May 2013 witnessed the launch of Citi Bike, the New York City bike share program sponsored by Citi

Group. The program, with 6,000 bikes at 330 stations earns it the honor of being the (currently) largest

bike sharing program in the United States (Fried, 2013c). The implementation of this program

experienced considerable delays due to various operational and other issues, including the devastating

hurricane Sandy (Zimmer, 2012). Nevertheless, the bike share program has been a tremendous success,

achieving over 2 million shared-bicycle trips just weeks after its launch, an achievement comparable to

those of other highly successful programs such as those in London and Paris (Fried, 2013a).

Furthermore, Citi Bike has maintained only one injury per 176,000 trips (Fried, 2013b). The Citi Bike

website also provides data on any given 24-hour period since its inception listing the number of trips,

miles traveled, and membership purchases. New Yorkers overwhelmingly approve of bike share and

planned bike infrastructure in the city (Miller, 2013). Los Angeles planned bicycle sharing program will

(one day) launch in a remarkably different urban environment; we hope to then assess the impacts of the

program on Southern Californias travel patterns, health, traffic safety, and economic development.


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3 Data CollectionData collection for the first phase of research was conducted in the spring and summer of 2013. Figures

2 and 3 show the 20-item questionnaire along with a travel diary that asked participants to detail their

trips from the prior day. This paper survey was duplicated in an online environment using the online

data collection instrument Lime Survey. The survey was distributed to participants via postcards sent to

2,500 households in the downtown Los Angeles region, defined as all locations within one mile of the

intersection of Seventh Street and Broadway. Both print and online surveys were made available in

Spanish. Several weeks after the first postcard was distributed, we sent a reminder postcard (Figure 4) to

encourage a larger sample size. Additionally, we worked with media outlets to publicize the study, with

a radio segment on KPCC public radio and postings in Blogdowntown, a downtown LA-oriented

weblog.

Figure 4. Postcard

Participants submitted self-reported data for various questions ranging from transportation choice,

demographics, and daily commuting patterns. We recieved a total of 478 responses, with 329 full and149 incomplete responses. Because some survey participants were likely recruited via our media

outreach as well as through word-of-mouth, it is difficult to ascertain the response rate for the study. The

maximum response rate possible would be 19 per cent if all respondents received a postcard.


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Figure 2. Page 1 of the English-Language Survey Instrument (Print Version)


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Figure 3. Page 2 of the English-Language Survey Instrument (Print Version)


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The survey responses likely suffer from some amount of bias, though it is difficult to determine in which

ways the responses might be biased. A principal concern of the research team was to avoid hypothesis

guessing on the part of the respondents, as this can introduce considerable bias. If it were clear that the

survey was princiapally aimed at understanding bicyclcing in downtown Los Angeles, then we might

expect the responses to be biases considerably toward those who bicycle already, as these people might

be more motivated to particiapte in the survey. Additionally, highly motivated participants and activists

often will provide inaccurate information in order to skew the results in a way that they feel will be

beneficial; here, we were concerned with overinflation of self-reported bicycle use as a strategic method

to convince public officials to provide more and better bicycle infrastructure in downtown Los Angeles.

The online collection method also provides a likely bias in terms of who has internet access but it is

uncertain how much this effects our study and in what way.


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4 AnalysisThis section presents an overview of the data collected in the first wave of the survey. We focus

principally on the relationship between demographic and geographic variables with our variable of

principal interest: the frequency with which one bicycles.1We derive the variable of interest from

question three of the survey, shown in figure 5. We treat this variable as an ordered categorical variable

throughout the analysis.

Figure 5. Survey Question 3, Primary Variable of Interest

Figure 6 shows the breakdown of responses to our question of primary interest. Over half of the

respondents (218 of 390) reported that they rarely or never bicycle. While this represents a large share

of respondents, this likely reflects a considerably greater rate of cycling in downtown Los Angeles than

in the U.S. as a whole. The nationally-representative travel diary survey, the 2009 National Household

Travel Survey (NHTS) asked respondents a similar question about the frequency of cycling of the past

week. In that survey, 87% of all respondents (and just over 87% of respondents in urban areas) reported

that they had not cycled in the past week. While the difference in reporting frames (one week versus one

month) likely makes up for some of the difference, it is probable that the rate of cycling in downtown

Los Angeles is greater than the national average. Nine percent of survey respondents reported riding

their bicycles nearly daily. The most recent Census data on commuting by bicycle suggest that fewer

than 5% of downtown Los Angeles residents commute by bicycle (though the margin of error on recent

Census data are quite large). This suggests, however, that a many of the frequent cyclists in downtown

Los Angeles may not be those who are using their bicycles for the journey to work.

1We use the self-reported monthly frequency of cycling rather than the actual occurrence of cycling on the travel diary day,as the month-long variable provides greater variation; the records for actual bicycle trips on the survey day include aconsiderable number individuals who made no bicycle trips on that day but who reported cycling a few times monthly. Wehope to obtain a larger sample in the future, including in a (yet unfunded) second wave. With this larger sample we hope tobe able to analyze trip-level records for actual bicycle trips.


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Figure 6. Self-reported Bicycling Frequency

4.1 Demographic FindingsA number of demographic and other variables are strongly associated with the frequency of bicycling.

For instance, age appears to be strongly associated with bicycling. Figure 7 shows this trend. Younger

residents of downtown Los Angeles are considerably more likely to report that they cycle a couple of

times a week or more than are older residents of the neighborhood. This finding is generally supportive

of existing research on cycling, which on the whole finds a negative association between cycling and

age (Pucher and Renne, 2003). Interestingly, those in their 30s are somewhat more likely to cycle

frequently than are those in their mid-20s, though this difference is not statistically significant.

218(56%)82

(21%)

55(14%)

35

(9%)

Rarely or Never Once or Twice a Month

A Couple Times/Week Nearly Daily


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Figure 7. Percent Bicycling "A Couple Times a Week" or More by Age

Similarly, income and bicycling show a strong relationship (Figure 8). Middle-income residents of

downtown Los Angeles are more likely to cycle frequently than are lower- and upper-income residents.

Of course, income is strongly related to a number of other important determinants of cycling behavior

most notably, age. This underscores the need to conduct multivariate regression to disentangle the

independent effects of each of these variables. We conduct such a regression in the following section of

this report.

Figure 8. Percent Bicycling "A Couple Times a Week" or More by Income

Group

35%

24%28%

19%

9%

0%

20%

40%

18 to 25(N=23)

26 to 30(N=79)

31 to 40(N=110)

41 to 50(N=43)

51 or older(N=47)

17%

33%

26%

16%19%

25%

0%

20%

40%

0-40k(N=41)

40k-70k(N=67)

70k-90k(N=43)

90k-120k(N=57)

120k +(N=77)

Refused(N=105)


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There is a significant relationship between sex and bicycling (Figure 9). In our survey, males report

riding bicycles more frequently than do females. Just over half of the male respondents report that they

rarely or never ride bicycles, whereas the figure for females was 63 percent. These numbers are quite

significant and may have some implications on the culture of bicycling, which has a stronger male

presence in general (Emond et al., 2009, Garrard et al., 2008). Still, the majority of both sexes described

riding rarely or never.

Figure 9. Self-Reported Bicycling Frequency by Sex

We observe large differences in cycling between racial/ethnic groups, though these differences are likely

due to other characteristics (principally, the age of respondents) and not to race itself. For Hispanics, 30

percent report bicycling a couple times a week or more. At the other end of the spectrum, only 14

percent of those who identify as Asian/Pacific Islander report cycling that frequently (Figure 10). But as

previous analysis found that most respondents who ride a couple of times a week are younger (age 18

to 25), does that suggest that most Hispanic respondents are younger? We first examined the mean ageof each race group in Figure 11; here the main finding is that our sub-sample of Black respondents is

considerably older than the rest of our sample. The differences between the average ages of other

racial/ethnic groups are less stark. Looking separately at the proportion of younger individuals in each

race/ethnicity category (Figure 12), we found larger differences. Over half of the white sub-sample is

under the age of 35, compared to only 5 of 16 (3%) of our black respondents. The quite large differences

63%

19%

11% 7%

51%

22%16%

11%0%

20%

40%

60%

80%

Rarely or Never Once or Twice aMonth

A CoupleTimes/Week

Nearly Daily

Female Male


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in age distribution by race/ethnicity likely explain some of the differences in cycling by members of

different racial/ethnic groups; we thus urge caution in interpreting these differences. In the following

section, we include race/ethnic variables in our modeling of cycling frequency and find no effect after

controlling for age and other variables.

Figure 10. Percent Bicycling "A Couple Times a Week" or More by Race

Figure 11. Mean Age by Race/Ethnicity

21%

30%

14%

24%

21%

23%

0%

20%

40%

Black (N=14) Hispanic(N=44)

Asian/Pac.Isl. (N=42)

White(N=173)

Other/Multi(N=29)

Refused(N=87)

43

33 3437

31

0

15

30

45


API (N=42) White (N=173) Other/Multi(N=20)


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Figure 12. Percent of Respondents 35 Years or Younger by Race

4.2 Policy-Related FindingsThis section discusses findings from our first-wave survey that relate directly to housing and

transportation policy. These findings are suggestive of several policy levers that might help to increase

the rate of cycling in Los Angeles.

Those who have free parking available at home are considerably less likely to bicycle frequently than

are those who have paid parking or those who have no arranged parking. Interestingly, there is no

difference in cycling rates between those who have free parking on-site and those who have free parking

off-site. However, having parking is also strongly associated with having a car (and the causality likely

runs primarily from the decision not to own a car to the decision not to purchase parking or rent from a

building with parking included, though certainly it may run in the opposite direction as well). While just

28 percent of those with parking available have no car (quite high for Los Angeles), 40 percent of those

with no arranged parking have no access to a car.

2.7%

17.9%13.6%

54.3%

0.5%0%

20%

40%

60%


API (N=42) White (N=173) Other/Multi(N=20)


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Figure 13. Percent Bicycling "A Couple Times a Week" or More by

Availability of Car Parking at Home

Interestingly, the lack of free parking at ones worksite is strongly associated with greater rates of

cycling. Those with no free parking at work (a slim majority of respondents) are more than twice as

likely to cycle nearly daily compared with those who have free parking provided, and over half again as

likely to report cycling a couple times a week.

Figure 14. Self-Reported Bicycling Frequency by Availability of Free

Parking at Work

20% 20%

24%

29%

0%

20%

40%

Free On-Site(N=176)

Free Off-Site(N=146)

Paid Parking(N=55)

No Parking(N=66)

52%

20% 18%10%

54%

31%

11% 4%0%

20%

40%

60%

80%

Rarely or Never Once or Twicea Month

A CoupleTimes/Week

Nearly Daily

No Free Work Parking (N=136) Free Work Parking (N=119)


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Availability of bicycle parking at home is similarly associated with the frequency of bicycling, though

here the story is less clear. Respondents with available bike parking are more likely to ride from once

or twice a month to a couple of times a week and conversely, a majority of respondents that rarely

or never ride a bicycle have no bike parking at home. Given the convenience of bike sharing

eliminating the need for bicycle storage, we are particularly interested in examining how bike sharing

may change the cycling habits of those with no bicycle storage at the home.

Figure 15. Self-Reported Bicycling Frequency by Availability of Bicycle

Parking at Home

Showers at work have a positive relationship with bike frequency as respondents with available showers

have a tendency to ride more often than those who do not. Figure 16 shows that those with showers at

work (a small proportion of all respondents) are considerably more likely to cycle than are those with no

showers available. It is possible that some of this difference stems from existing cyclists requesting

showers at work, though we suspect that the causality runs primarily in the other direction; showers at

work clearly make it more convenient to cycle to work.

60%

20%11% 10%

47%

24% 22%

7%0%

20%

40%

60%

80%


A CoupleTimes/Week

Nearly Daily

No Bike Parking (N=266) Has Bike Parking (N=124)


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Figure 16. Self-Reported Bicycling Frequency by Availability of Showers at

Work

59%

19%13% 8%

35% 33%

20%13%

0%

20%

40%

60%

80%


A CoupleTimes/Week

Nearly Daily

No Shower at Work (N=344) Has Shower at Work (N=46)


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5 Statistical ModelsMany of the variables explored above are correlated with one another. For instance, not having paid

parking at home and not having a car are moderately positively correlated in our dataset. This makes

sense, intuitively. Not all of those who are car-free will live in buildings that have no pre-arranged free

parking, but on the margin we expect that people sort into residential settings that suit their needs.

Providing free parking costs developers money, and they pass these costs on to renters or buyers; thus,

all else equal, no-parking housing units will rent or sell for less than those with parking, and so those

who have no need for parking will be enticed to purchase or rent there. Thus, simply by looking at

descriptive statistics (as above), we may obtain a skewed understanding of the underlying processes that

influence bicycle use; is it in fact the lack of parking that has a strong primary association bicycle use, or

the lack of a car?

In order to disentangle the various influences on cycling behavior, we therefore employ an ordered

logistic regression model. This type of statistical model is appropriate estimating the independent

associations of variables on an outcome that is ordinal and discrete, such as self-reporting levels of

cycling in four ordered categories. The model is a maximum-likelihood technique that assumes an

underlying continuous process (in this case, frequency of cycling trips per unit of time) and then

estimates cutoff points along a continuum of outcomes for each of the (latent) ordered response

categories. Thus, if the underlying process can be described as:

Eq. 1where cycling*is an unobserved, continuous count of cycling trips per unit time, 1nare coefficients

andx1xnare variables expected to influence rates of cycling (for instance, age, parking availability,

and the like) and is an error term. Because we expected it to be far too tedious (and highly error-prone)

for individuals to recollect the actual number of cycling trips they had taken in the past month (cycling*),

we instead used a series of four ordered categories, as shown in Figure 4. The model estimates cutoff

parameters delineating these categories:


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{ Eq. 2

Table 1 shows the results of three models. We discuss each of these in turn. Due to the sample size of

our study and the cross-sectional nature of this preliminary study (prior to a second wave of surveys), as

well as the high level of correlation between some of the variables included in our survey, we restrict

our initial analysis to those variables that are highly policy-relevant and/or relevant from a social justice

perspective.

Model 1 presents the output from a model that includes only the individuals age, sex, and income in

predicting cycling frequency. Positive values indicate more cycling, while negative values indicate less.

Stars indicate the level of statistical significance, and coefficients that have no stars are not statistically

significant from zero, indicating no effect. This model serves as a reasonableness check of our data. The

estimated effect of each of the three variables conforms to our expectations; older people and women are

less likely to cycle than are younger people and men, and all else equal those with higher incomes are

more likely to cycle. We also include a series of dummy variables for those respondents who did not

report age, sex, or income. This statistical method (not without its problems; see Cohen et al., 2003)

allows the researcher to include the records of individuals who (for reasons of privacy or response

fatigue, for instance) have chosen not to respond to one or more questions in the survey. Here, we see

that those who chose not to report their income (over one third of all respondents) are considerably more

likely to cycle than are those who did report income, and those who did not report their sex (just under

one quarter of respondents) are considerably less likely to cycle frequently. We have no ready

interpretation for these findings; this method helps, however, to increase the sample size of our models.

Model 2 adds dummy variables for race. Here we find that the estimated effects of income, age, and sex

remain nearly unchanged, while the variables for race are not statistically significant at the p


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Table 1. Ordered Logistic Regression Results for Self-Reported Cycling Frequency, Downtown Los Angeles

Residents 2013

Model 1 Model 2 Model 3

Income in Thousands 0.004 ** (2.18) 0.004 ** (2.15) 0.004 ** (2.28)

Age -0.054 *** (-4.40) -0.054 *** (-4.29) -0.055 *** (-4.23)

Female -0.566 ** (-2.30) -0.544 ** (-2.19) -0.606 ** (-2.39)

Race/Ethnicity (Base: Non-Hispanic White)

Non-Hispanic Black -0.399 (-0.55)

Hispanic, any Race 0.202 (0.62)

Asian/Pacific Islander -0.266 (-0.78)

Other/Multi-Ethnic -0.010 (-0.02)

Free Parking at Home -0.355 * (-1.73)

Free Parking at Work -0.143 (-0.78)

Showers Available at Work 0.658 ** (2.32)

Missingness Indicators

Did not Report Income (N=105) 1.380 *** (2.81) 1.525 *** (3.03) 1.526 ** (3.02)

Did not Report Age (N=88) -1.407 (-1.62) -1.973 * (-1.87) -1.544 * (-1.69)

Did not Report Sex (N=88) -1.743 ** (-2.38) -2.059 ** (-2.45) -1.766 ** (-2.42)

Did not Report Race (N=87) 0.747 (0.84)

Cut-Off Values

Rarely/Never to Once or Twice/Month -1.450 *** (-3.22) -1.462 *** (-3.02) -1.693 *** (-3.17)

Once or Twice/Month to CoupleTimes/Week -0.418 (-0.93) -0.423 (-0.88) -0.652 (-1.01)

Couple Times/Week to Nearly Daily 0.731 (1.59) 0.730 (1.48) 0.503 (1.37)

N (responses) 390 390 390

Cox-Snell R2 0.085 0.090 0.105

AIC (Lower indicates better fit) 877.0 884.7 874.3

Note: Source: authors' data; t-statistics in parentheses; stars indicate statistical significance: * p


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Finally, in Model 3 we remove the controls for race and ethnicity and add in our three primary policy-

oriented variables: parking availability at home and at ones workplace, and the availability of showers

at the workplace. Here, we find that free parking at home is associated with lower rates of cycling, while

showers at work are associated with considerably more cycling. The estimated effects of our control

variables (race, income, and sex) do not change meaningfully, suggesting that the policy-related

variables effects are independent of these controls.

In Figure 16, we focus on the predicted effect of increasing the number of worksites in Los Angeles that

have showers available on cycling behavior, controlling for other variables in the model. Our model

suggests that a doubling of the number of worksites with showers (from 12% to 24% of worksites)

would have moderate influence on cycling frequency; the share of those who do not cycle at all is

predicted to decrease from 56 percent to 52 percent, with a two percentage point increase in the

frequency of those who bike a once or twice a month or a couple times per week.

Figure 16. Predicted Change in Cycling Frequency by Saturation of

Showers at Work

56%

21%

14%

9%

52%

23%

16%

9%

43%

25%

19%

13%

0%

20%

40%

60%

Rarely or Never Once or Twice aMonth

A Couple Times/Week Nearly Daily

12% Showers (Current) 24% Showers (Doubling) 100% Showers


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6 ConclusionThe impact of a bicycle-sharing system on travel behavior in downtown Los Angeles remains unknown,

as the proposed system remains unimplemented. However, the existing cycling behavior of downtownresidents is suggestive. On the whole, downtown residents are more likely to cycle than are other

Angelenos. Our analysis provides several insights into cycling in downtown Los Angeles, though the

results are preliminary due to the incomplete nature of the study at this time.

In particular, we note the strong gendered patterns of cycling, which have been documented elsewhere.

In downtown Los Angeles as elsewhere, bicycling is a heavily male activity. Other researchers have

suggested that this may be due to perceived safety and security issues while cycling; we encourage Los

Angeles cycling advocates and transportation officials to reach out to women (particularly, those who

are not cycling but otherwise fit the profile of a cyclist) to better ascertain the barriers to cycling.

Our study suggests that mandating parking in residences may decrease cycling rates. Indeed, downtown

Los Angeles is the sole location in the city of Los Angeles where a large share of housing is available

without parking; in other parts of the city, renters who wish to live a car-free lifestyle typically must pay

for parking (in the form of higher rents) regardless of their preference. Reducing or eliminating

minimum parking requirements at residential developments may therefore increase cycling rates.

Finally, we find that the provision of showers at the workplace has significant impact on cycling rates,

though the effect is modest. A doubling of the number of workplaces with showers would, our model

suggests, reduce the share of workers who never cycle from 56% to 52%. Certainly, this effect is

meaningful, and in conjunction with other pro-cycling policies, policies in support of workplace showers

may help to create a more bikeable Los Angeles.


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shared wheels in the auto city: evaluating los angeles’ bicycle sharing program

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