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A-1 APPENDIX A ESTIMATING BICYCLING DEMAND INTRODUCTION by differences in the bicycling environments. Unmeasured factors, perhaps cultural or historical, appear to play an extremely large role Transportation investment decisions often require estimates or in determining the level of cycling in an area. predictions of the amount of bicycling in a given area, as well as A second, less common type of demand prediction method uses how this amount depends on facilities and other conditions. Despite census commute-to-work shares, often combined with other data, to a variety of publications describing efforts to model bicycle demand, provide an area-specific baseline of bicycle usage; this can help to no modeling technique or set of parameter values or even rules of neutralize or perhaps proxy for some of the unmeasured factors that thumb has emerged as definitive. can have such a large impact on demand. Epperson (138) in Miami A first step in thinking about how to model bicycling demand is used census data combined with demographic factors for estimating to understand the types of questions that the model might be used bicycling demand generally. Goldsmith (139) in Seattle used census to answer. Porter, Suhrbier, and Schwartz (41) list three major ques- data combined with local information to predict likely changes in tions, paraphrased here: bicycle commuting due to facility improvements. This appendix approaches the demand prediction problem more How many people will use a new facility? from this second philosophical perspective; that is, to use known infor- How much will total demand increase given an improved facil- mation about commuter bicycling to develop estimates of total bicy- ity or network? cling levels in an area. These estimates would provide an area-specific How does bicycling affect public objectives such as congestion baseline that could then be supplemented with other information to and air quality? predict how the number might change under various conditions. There are three major steps in developing a tool based on this approach. The last of these could be expanded to include the benefits to The first part of the appendix describes the results of several sur- cyclists themselves, such as improved health and recreational oppor- veys and other measurements of general bicycling demand completed tunities. The answer to this question could be useful politically, in over roughly the last decade. The aim is to bring together the results justifying public spending on bicycle-related projects. The answers of these many different measurements, to show that the statistics are to the first two questions are likely to be more useful for technical all roughly consistent when their differing time frames are consid- analyses, in prioritizing projects given limited resources. ered, and place general bounds on the sizes of numbers that are likely Another way of approaching the problem is to note that there are to be observed. three different demand prediction objectives: The second part of the appendix argues that, for a variety of rea- sons, the common demand modeling objective to develop relation- Predicting the total amount of bicycling in an area or on a facility, ships between facilities and use by comparing different geographic Predicting the marginal amount that total demand will change areas is not likely to provide models that are consistently success- given a change in facilities or policy, and ful. The reasons are derived in large part from some problematic Identifying areas where inadequate facilities appear to be hold- findings from our own attempt to develop a demand model for the ing the level of bicycling below its potential, as in the "Latent Twin Cities area. Demand" approach (42). The third part of the appendix discusses a simple model relating current total bicycling rates to census commute to work shares. We In principle, a model that explains the total amount of bicycling describe estimates of this relationship across several geographic as a function of "basic" factors including demographic, policy, and scales. This method is advantageous because it is simple to estimate, facility variables would answer all of these questions at the same understand, and explain to policymakers, and has a known range of time. Most past work has taken this kind of approach. Federal High- accuracy. way Administration (43) and Texas Transportation Institute (44) completed major surveys of non-motorized modeling techniques in the late 1990s; the majority of the efforts they describe focused on THE AMOUNT OF BICYCLING IN THE U.S. predicting either commute shares or total bicycle travel by reference to these types of basic factors. More recent work such as Dill and This section describes the results of several surveys measuring Carr (55) has also used this methodology. general bicycling demand that have been completed over roughly the Results of these efforts have been mixed. While certain demo- last decade. The primary objective here is to bring together the results graphic and geographic variables routinely emerge as important, of many different measurements, to show that they are roughly con- evidence linking bicycle facilities and policies to levels of cycling sistent when their differing time frames are considered, and to place has proven hard to come by; Dill and Carr note that there is some- general bounds on the sizes of numbers that are generally likely to be what of a consensus that such evidence has not been established. In observed. A secondary objective is to demonstrate how a concep- general it has been hard to find strong relationships because the dif- tual framework in which there is a distribution of bicycle riding fre- ferences in levels of bicycling across different areas can be very quencies over the population can reconcile the various measures of large in relative terms, much larger than can reasonably be explained bicycling demand.

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A-2 Measurements of Bicycling Frequency populations. The NHTS also asks about whether the individual com- pleted bicycle trips during the last week; again it is possible to cal- Most of the available information about the amount of bicycling culate this at the level of specific MSAs and states. addresses the number of cyclists, as opposed to number of trips or There are several national bicycling-specific surveys addressing miles of cycling. Because of the amount of information that is avail- longer time periods than a week. Rodale (46) reports on U.S. sur- able about riding frequency, we use this as our measure of bicy- veys done in 1992 and 1995. They report the percent of adults bicy- cling demand. The end of this section briefly addresses some other cling in the last year, and it is possible to calculate the percent bicy- measures. cling in the last month. The Bureau of Transportation Statistics (47) The surveys and other sources that address the frequency of bicy- conducted a U.S. survey asking about riding done during the sum- cling produce a wide variety of results. Each source asks about a dif- mer of 2002, defined as a three-month period. A more general Min- ferent time frame; the number of people who cycle in a week will nesota Department of Transportation (Mn/DOT) survey (141) from be larger than the number who ride in a day. A key distinction to 2003 asks whether respondents bicycle for exercise, but does not keep in mind is that (empirically) adults are considerably less likely ask about frequency. The 2002 National Sporting Goods Association to ride a bike than are children, regardless of the time frame being survey (48), asks about participation in a variety of recreational activ- considered. These two groups must therefore be studied indepen- ities; here the standard is riding a bike at least 6 times in the year. dently to avoid confusion or ambiguity. This is generally not an Finally, the U.S. Census asks detailed questions, including mode issue with most bicycling surveys, which tend to focus on adults, choice, about the commute to work of about 10% of the residents of but it is a factor in deriving numbers from general travel data col- the U.S. These are summarized for use by transportation planners lection surveys. In the ensuing discussion and tables, the data refer in the Census Transportation Planning Package (142). These data to adults 18 years and older. have the advantage of being by far the largest and most geographi- We derived measures of the number of people who ride a bicycle cally comprehensive bicycle-related data sample available. The dis- in a given day from two sources. The Twin Cities Travel Behavior advantage is that they capture only commute to work trips, which Inventory (TBI) from 2001 (140) was a daily diary survey of about are a small minority of all bicycling trips (47). Table 10 summa- 5,000 households in the Minneapolis-St. Paul metropolitan statisti- rizes the results from the sources described here and in the preced- cal area (MSA). This was done primarily during the spring and sum- ing paragraphs. mer. The National Household Travel Survey (NHTS) of 2001 (45) Some people ride almost every day; others may only ride once a was a similar survey done over the entire United States; roughly year. The longer the time frame being considered, the more people 25,000 households were sampled in the general survey that we exam- will have ridden at least once. It is possible to divide the population ined for this study. This survey was done over an entire year, which into different frequencies of riding in a manner consistent with the makes it possible to measure seasonal variations. Both of these above numbers derived from different time frames. If each member surveys involved households keeping travel diaries on a randomly of a group of people has a probability p of riding a bicycle in a given assigned day; these days were spread throughout the week, and day, then the expected fraction n of that group that will ride at least throughout the year for each geographic area. once in a span of x days is given by the formula: The NHTS also identifies households in about 20 Metropolitan Statistical Areas (MSAs) and 34 states, allowing us to calculate aver- n = 1 - px (1) ages for these areas. It should be noted that samples for many of these were fairly small, so the number for a specific area could be well off the true value. However, this probably gives a reasonable estimate Groups with different riding probabilities, p, will generate dif- of the range of values that might be observed over areas with large ferent expected numbers of riders over a given time frame, and the TABLE 10 Measures of adult bicycling frequencies Source and Area Measure Average Range TBI, Twin Cities MSA % per day 1.4% - NHTS, U.S. Total 0.9% .56% winter .88% spring-fall 1.1% summer NHTS, U.S. MSAs - 0.2% - 2.4% NHTS, U.S. States - 0.0% - 2.2% NHTS, U.S. Total % per week 6.7% - NHTS, U.S. MSAs - 4.5% - 12.7% NHTS, U.S. States - 3.5% - 12.4% Rodale % per month - 16.6% - 21.2% BTS % per summer 27% Rodale % per year 37% - 46% NSGA % 6 times per year 10.7% - Mn/DOT % that ever ride 50% - U.S. Census Commute to work % 0.4% U.S. Census, MSAs 0.1 - 1.4% U.S. Census, states 0.1 - 1.1%

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A-3 numbers from each group can then be summed to arrive at a popu- MODELING BICYCLING DEMAND lation total. Table 11 shows an example of how the population can be allocated to groups with different probabilities of riding in a Traditional approaches to modeling bicycle demand are derived given day, in order to match known overall population bicycling from the standard methods used for forecasting auto travel. That is, rates over different time frames. These riding probabilities and pop- they start from basic information about the people and the transporta- ulation frequencies are mathematically consistent with about 1% of tion environment in an area and use this in some way to predict an adults riding in a given day, 5.3% in a week, 16% in a month, 29% amount of bicycle travel, either directly, or as the solution to a mode in a summer, and 40% in a year, and with 50% "sometimes" riding, choice problem in a larger travel model. although not necessarily in a given year. This section discusses some problems with using this approach The numbers deriving from the population frequencies do not to model bicycling demand, some of which appear intractable. The exactly correspond to the national averages over the medium time arguments are based in part on some of the facts about bicycling dis- frames. This is probably because the national averages may be cussed in the previous section, and in part on some preliminary find- slightly overestimated in these cases. Intermediate time frames such ings from our own attempt to estimate a demand model for the Twin as "this week" or "the last month" contain some room for personal Cities area. While this model is not described here, in part due to the interpretation; a person who rode ten days ago might consider that to lack of useful results, it is used to illustrate some of bicycle demand be close enough to count as part of the last week. Evidence that this modeling more generally. is happening can be seen in the fact that the fraction of adults in the There are several reasons a bicycling demand model derived NHTS who report riding in the last week is more than seven times from basic information about land use, demographics, and the trans- the number that rode on their survey day. Given that survey days portation system is likely to be of limited utility. These can be illus- covered all days of the week, and that every day will not be a com- trated in part by our own attempt at developing a demand model, in pletely new set of people, this result should be logically impossible. which we found a statistically significant result that off-road paths If this frequency table is roughly right, there are some interesting were associated with lower per person levels of bicycling for nearby implications. The top four lines are the people who ride at least once residents. This result makes no sense intuitively; at worst residents every ten days. They are 2% of the adult population, or 5% of the should ignore the paths. Empirically, Davis (50 ) found that off-road adults who cycle in a given year. But they constitute 42% of the rid- facilities in the Twin Cities were in fact much more intensively used ers on any given day. That is, the 5% most active cyclists generate in all parts of the city than other options such as streets and on-street about half the riding days, the other 95% generate the other half. bike lanes. Our result was not due to an obviously underspecified Because so many of the trips are generated by such a small number model; a wide variety of demographic and land use variables were of people, a relatively small part of the population can have a big included in the regressions. There are several possible reasons for impact on the total amount of cycling. If 4% of the public were in this problematic outcome. the "frequent" category, rather than the 2% that probably are now, One is a possible shortcoming in the analysis; the way facilities that could conceivably lead to a 40% increase in the total amount of were defined did not correspond to how people perceive them. For biking. Something like this may be what is happening in areas that example, many of the suburban "off-road" facilities run next to busy generate very high levels of bicycling. highways, with all the associated crossing of driveways and roads. Evidence from the TBI and NHTS, although not exactly consis- They are off-road in the sense that there is a barrier separating them tent, shows that on the average day when an adult rides a bicycle, from the road, but they are not off road in the sense of eliminating he or she rides about 40 minutes. The NHTS also reports distances, potential conflicts, or of being appealing to ride on. However, the however, these seem extremely unreliable. Considering the total development of a more general measure of the bicycling environ- daily ride durations in these data, assuming plausible average speeds, ment, going beyond simple number of miles of facilities, is a diffi- and assuming that those people who ride longer times will also go cult problem for many reasons. faster, gives a likely daily average distance of perhaps 7 to 10 miles. Another reason is that a large fraction of bicycle riding is recre- Those people riding more than 60 minutes in a day, while they are ational. Intuitively, the sorts of land use and transportation facilities only one-quarter to one-third of all cyclists in a given day, ride that would be ideal for utilitarian riding (dense development, a grid about two-thirds of the total miles. network, etc.) seem very different from what would be ideal for re- creational riding (infrequent intersections, density of little impor- tance). That is, the value of a facility may depend on the use to TABLE 11 Possible population which it is being put. As a related point, the skill level of the rider distribution of bicycling frequencies likely also influences perceptions of the riding environment. These are significant conceptual difficulties, since it would seem that there Frequency of cycling % of adults is no single land use-transportation type that is ideal for all bicycling 3 of every 4 days 0.1% activities or people, and hence no unambiguous way of defining the 1 of every 2 days 0.2% "quality" of the environment. 1 of every 4 days 0.5% The second problem with this sort of model is that there are large 1 of every 10 days 1.2% and seemingly random differences from one place to another. In one 1 of every 20 days 3% 1 of every 50 days 10% area we analyzed in Minneapolis, 16% of the adults made bike trips 1 of every 100 days 15% on the day they were surveyed, while the rate in many other areas 1 of every 200 days 20% was 0%. Even across entire metropolitan areas or states, differences Never 50% of a factor of ten can be seen.

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A-4 There are some well-documented population and land use char- of bicyclists in an area could be used for general political purposes, acteristics that are associated with higher levels of bicycling. For justifying expenditures by reference to the number of bicyclists and example, people with college educations are more likely to bicycle, the benefits that they receive from cycling. However, the more inter- but the difference is on the order of a factor of two compared with esting problems for planners are predicting how the number of cyclists less educated people. A similar difference exists for factors such as will change as a result of a facility or other improvement, and know- income, development density, and gender. None of the known fac- ing how many cyclists use or will use a specific facility. tors, alone or together, can come close to explaining why people in While this model does not directly address these questions, we some places are ten or more times as likely to ride bikes as people in believe that it is still useful because the answers to these questions other places. Other attitudinal and possibly historical factors seem will in general need to be conditioned on the number of current bicy- to dwarf the effect of the factors that planners and policymakers can clists. This is not to say that the number of cyclists in an area can- control. not grow; the examples of many high-cycling cities show what is Because the impact of the unobservable variables are so big rel- possible. However, in general any growth will probably be gradual ative to the variables of interest, it seems highly likely that what is rather than abrupt, and will likely depend on continued improvement being observed, both in our model and in others, is the effect of atti- of the cycling environment. Thus the rules of thumb developed here tudinal variables acting on policy variables through spurious corre- are not intended to represent permanent bounds on possible cycling lations. Our model seems to have been driven by geographic spikes levels, but only to provide a range of likely short-term changes. in riding that happened to be positively correlated with some facil- Similarly, while use of a given facility will probably depend on ity measures and negatively with others, but that in a causal sense a host of site-specific factors, in most cases it will also be limited in had little or nothing to do with any of them. It seems possible that the short term by existing bicycling habits among the surrounding these types of spurious correlations might also be driving the results population. A thousand daily users may be realistic in an area where of other work of this type in the literature, given the typically low 2,000 people a day currently ride bikes; it is probably not realistic explanatory power of these models. in an area where 200 do. This is not to say that facilities are only A third problem is that low levels of bicycling cause the range of justifiable in areas that already have a lot of cyclists, or that a facil- sampling error to be many times larger than the sample mean for ity cannot increase the number of cyclists. The point, again, is only any realistic sample size. The effect is that the regression is trying to provide an empirical basis for developing realistic expectations to match measured variable values that could be off by a factor of regarding short-term results. five or more from their true values. A sample of 1,000 people would The basic assumptions motivating this analysis are that a large frac- yield 9 cyclists on a given day at the national average level; the 95% tion of total bicycling is done by a small fraction of cyclists who ride confidence interval for this sample ranges from 3 to 15 cyclists. This frequently and that many of these frequent riders are bicycle com- is a large difference in relative terms, and observed extremes could muters observed in the census commute to work data. The hypoth- easily just be sampling aberrations. Yet these inaccurate measure- esis that we test in this section is that the basic riding frequency table ments could strongly influence the estimated parameter values. described in the previous section will hold more or less across dif- Finally, there is always the problem, noted by Dill and Carr (55) ferent areas. Thus an area with many commuter cyclists will also and others, that even a positive correlation between riding and facil- have more total cycling, and an area with few commuters will have ities could be causation in the other direction, that is, the large num- little total riding. In other words commuting by bike, while it is a ber of cyclists creating the political climate to build the facilities, small fraction of the total bicycling in a given area, can still be used rather than the facilities encouraging more riding. For example, bike as a "leading indicator" of what might be happening with other types lanes in some cases may be a response to existing situations such as of cycling. bikes interfering with traffic. In these cases retrofitted lanes will Three different geographical divisions are examined to study this often be associated with high levels of cycling after they are built. issue. First is a set of 15 MSAs for which we could match CTPP com- By contrast, lanes in some newer cities in California do not seem to mute to work shares with NHTS (45) daily bicyclist counts. Next are have high riding levels (55), possibly because they were designed states; there are 34 for which both census and NHTS data were avail- into new roads, that is, built in anticipation of riding rather than in able. Last is an analysis of 66 "zones" of the Minneapolis-St. Paul response to it. MSA using data from the TBI (140), showing that the basic princi- Seemingly the only way around these problems would be to study ple still works at this very different geographic scale. the same geographic area over a period of time as facilities change. The TBI and NHTS, like most travel diary data, are limited by The relevant question for policy is not comparing people living at small sample sizes for specific geographic areas. Because of this and location A with different people living at location B, but rather com- the low level of cycling, the expected number of cyclists in the sam- paring the people at A with themselves as the provision of facilities ple for a given area could vary by a factor of 10 or more from the low changes over time. This would be an expensive prospect using sur- to high end of the range. Ordinary measures of goodness of fit have veys; development of a low cost method of counting bikes over a little meaning in this sort of environment; we focus instead on more large number of different streets and bike facilities, such as is out- heuristic measures such as the number of observations that fit within lined in Davis (50), would be of great value for this purpose. the predicted confidence interval. A MODEL OF TOTAL BICYCLING DEMAND Metropolitan Statistical Areas This section outlines a simple "sketch planning" method for esti- Combining census data with our NHTS analysis produced 15 MSAs mating the number of daily bicyclists in an area using easily avail- for which we had both commute to work shares by bike and total able data from the CTPP (142). An estimate of the current number percent of adults biking on their survey day. The commute shares

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A-5 ranged from 0.1% (Cincinnati and Dallas) to 1.4% (Sacramento). States The daily adult biking shares ranged from 0.18% (Houston--although this is probably a sampling problem as 4.2% rode during the previ- There were 34 states with data from both the census and the ous week), to 2.45% (Portland, OR, with Sacramento close behind NHTS. Alabama had the lowest bicycle commute share at 0.07%, at 2.25%). We estimated parameter values as shown in equation 2; Oregon the highest at 1.07%. Arkansas has the lowest rate of total the R squared for this equation was about 0.7. bicycling at 0% (again, a sampling problem as 3.4% rode during the preceding week), and Florida the highest at 2.21%. Equation 3 shows the estimated parameter values, which are slightly different A = 0.3% + 1.5 * C (2) from that observed at the MSA level. The R squared of this model where A = % of adult populatio on who bicycle in a day was about 0.3. C = bicycle commute share % A = 0.4% + 1.1 * C (3) This equation can be used to generate a predicted total riding share for each city. Given this predicted share and the NHTS sample size, Using either these parameter values or those derived from the a 95% confidence interval of expected number of adult bicyclists MSA level, the same predictive results emerge. Of the 34 states, in the sample can be calculated assuming a binomial function. For 30 have actual counts within a 95% confidence interval of their pre- 14 of the 15 cities, the actual number of bicyclists fell within this dicted values; the exceptions are all underpredicted. Of the states confidence interval. The one exception was Chicago, which gener- with good sample sizes (over 1,000) about half were predicted with ated 19 actual cyclists compared with a predicted level of 9. good accuracy (less than one standard deviation), the other half were The performance of this model at predicting the observed number farther off the mark, with predictions both too high and too low. of cyclists for the cities with the biggest samples (and presumably the most reliable numbers) is quite good, again with the exception of Chicago: New York had 20 predicted, 23 actual; Los Angeles had Twin Cities Zones 23 predicted, 22 actual; San Francisco had 21 predicted, 19 actual; and Boston had 9 predicted, 7 actual. At the low and high ends The final level of analysis considered variations within the Min- of the commuter cyclist ranges, Cincinnati had 1 predicted and neapolis-St. Paul MSA, using 65 "zones" that had been defined for 1 observed, Dallas was 3 predicted, 3 observed; Portland was 6 pre- a different project. These were largely based on political bound- dicted, 10 observed; and Sacramento 9 predicted, 8 observed. Port- aries, with the two central cities divided into a number of zones land was among the worst-predicted cities, but was still within a based on natural and artificial divisions and neighborhood character- 95% confidence interval. Overall, as Figure 8 shows, the hypothe- istics. Populations of the zones ranged from about 10,000 to 30,000. sis that overall bicycling rates will correlate with bicycle commut- While this analysis was based on the TBI, which was a large local ing rates seems to be supported; indeed the correlation seems survey, there were still only 139 adults in the survey who made bike quite strong at this geographic level. Figure 8 shows performance trips (whose home location could be mapped to a zone in this area), of the model, the points represent actual commute rates for cities and one-third of these were in four zones in Minneapolis. Thus the and the lines represent levels that the model predicted. estimated bicycling rates for most of the zones are extremely unreli- The equation is also exactly consistent with the U.S. as a whole able. The results of this regression are shown in Equation 4. (0.4% commute share, 0.9% total daily cyclists), and with a divi- sion into larger and smaller cities, in which the same figures are observed. A = 0.6% + 2.5 * C (4) 3.0 0.5% + 3(C) 0.3% + 1.5(C) Daily Adult Bicyclists, % 2.5 2.0 1.5 0% + 1 (C) 1.0 0.5 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Bicycle Commute Share, % Figure 8. Daily bicyclists and commute share, combined MSAs and states.

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A-6 The relatively high slope and intercepts of this equation are likely 1.5 times the commute share; this was the best fit at the MSA level, a reflection of the "outlier" nature of the Twin Cities compared with and also describes the U.S. as a whole. Figure 8 shows lines repre- the areas on which the previous two regressions were based. That senting rough boundaries on the observed values for daily cyclists, is, depending on the measurement, the Twin Cities have an overall as they relate to bicycle commute shares at the MSA and state level. adult bicycling rate of 1.6% to 2%, which is quite high compared These lines appear in fact to represent three distinct relationships with their bike commute share of 0.4%. Thus overall bicycling here between these two variables that are observed in the data, but at this is about twice as high as would be predicted by the earlier regres- point this must be considered a sampling coincidence. sions; given this information, perhaps it is logical that the estimated The model described here has important practical advantages. It is parameter values with data drawn from this region would be about simple enough to be understandable to makers of funding decisions, twice as high as well. and provides a known range of possible outcomes derived from a In most of the zones the sample size was too small to present an wide variety of locations and different geographic scales. How- interesting prediction problem; that is, for these zones both the pre- ever, it does fall short of the modeling ideal of directly describing diction and the actual count were either one or zero. For those 15 zones a relationship between the provision of bicycling facilities and the where the predicted number was two or more, 12 were predicted amount of bicycling that will take place. The formulas we derive sim- within a 95% confidence interval, while three had actual values in ply describe the amount of bicycling that is currently taking place; excess of the predicted range. Although the high bicycling zones they do not relate this amount in a causal way to explanatory factors, were not predicted accurately in absolute terms, the general rela- or explain how it might change. We believe that this compromise is tionship between commuting and total bicycling held. The six zones necessary because of the findings described in the first two sections where commuting by bike exceeded 2% generated six of the seven of this appendix. highest rates of overall daily bicycling. By helping the planner to estimate a range of the number of bicy- clists currently riding in a given geographic area, the model estab- lishes a baseline that can be used to develop more informed estimates CONCLUSION about how this number might change given a change to the facili- ties or cycling environment. Such a baseline is necessary for any more On any given day, roughly 1% of the adults in the United States detailed estimates or predictions because there is such a high degree ride a bicycle. Over large geographic areas such as metropolitan areas of variation in bicycling demand levels in different locations. This or states, this number could range roughly between about 0.3% and model represents a first step in such a methodology; the question of 2.5%. Over smaller areas such as specific parts of metropolitan areas, how to get from a general estimate of current bicycling levels to pre- the range could go as high as 15%. These variations are far larger dictions about general or facility-specific future levels is left to later than can be reasonably explained by differences in formal policies research. and facilities. It seems that local or even "subcultural" attitudes and More qualitative research to better understand the "outliers" could perhaps history play a very substantial role in the perception of bicy- also be useful. Some MSAs and states have very high or low levels cling as an appealing or even "normal" thing for an adult to do, of bicycle commute shares and/or daily adult bicyclists. Over such although without further study it is difficult to imagine how these large populations, this seems unlikely to be due to demographic dif- factors might exert their influence. ferences. More detailed case studies of places that generate these When the actual percentage of cyclists in an area is not known, it very high or low rates of bicycling could be enlightening, especially can be estimated with reasonable accuracy by considering the bicycle if "soft" factors such as culture and attitudes can be probed in some commute to work share. A "most likely" value would be 0.3% plus systematic way.