Guidelines for Analysis of Investments in Bicycle Facilities (2006)

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21 CHAPTER 2 MEASURING AND FORECASTING THE DEMAND FOR BICYCLING INTRODUCTION This chapter describes strategies to estimate the demand for different types of cycling facilities. Such estimates form the basis for user travel time and cost savings as well as esti- mates of reduced traffic congestion, energy consumption, and air pollution. Several relatively comprehensive reviews exist that estimate the demand for non-motorized travel. These re- ports range from adapting traditional transportation modeling applications to devising specific applications and tools. Rather than simply review these existing reports, this chapter focuses on supplementing the knowledge gained from these reports with new perspective and original research. The team presents a way to understand the actual amount of cycling based on different settings (as opposed to how it is modeled or predicted). While several surveys and datasets describe the amount of bicycling in the United States and various smaller areas within it, no single effort has previously reconciled the results of these different surveys and data sources to develop a general overview of the amount of bicy- cling. Supplementing its own data analysis with these previ- ous efforts, the team reconciles several seemingly conflicting survey results and sets bounds on the amount of bicycling that occurs in various geographic areas. The team uses this as a basis for a simple sketch planning model for bicycle planners to estimate demand in local areas. The team also presents a detailed model to predict the amount of cycling relative to cycling facilities in the cities of Minneapolis and St. Paul, Minnesota. This analysis helps advance the state of the art beyond simply describing the techniques for demand modeling to evaluating how these techniques can be reasonably applied by a planner seeking reasonable results with limited resources. A principal utility of this exercise was to present many of the difficulties asso- ciated with practices of predicting demand. Such difficulties illustrate the limitations of applying traditional demand mod- eling applications. The detailed bicycling demand analysis led to conclu- sions regarding the most practical ways of measuring and predicting demand from the standpoint of a planner work- ing with limited resources and data. Based on these conclu- sions, the team developed a draft sketch planning method for measuring and predicting bicycling demand.i The method develops ranges of estimates from limited and easily avail- able datasets. A model easy to understand, use, and explain has more value even if it is necessarily limited in its detail and precision. This method has been incorporated into the guidelines. The literature review is described as follows: first is the lit- erature on measuring demand, including some original con- tributions; second is the literature on modeling demand, that is, relating demand to bicycle facilities (the team makes orig- inal contributions by developing a demand model for the Twin Cities area); and third is the demand literature discus- sion, which evaluates many of the problems with the tradi- tional approach of predicting demand by relating demand to underlying explanatory factors. LITERATURE REVIEW Simple and reliable tools to estimate and predict the amount of bicycling in a given area, and how this amount depends on the availability of bicycle facilities and other conditions, would be useful for a variety of investment and policy deci- sions. However, while the desirability of such tools is gener- ally recognized, and there have been a number of efforts to model demand either specifically or generally, no modeling technique or set of parameter values or even rules of thumb have emerged as definitive. Measuring the amount of bicycling occurring is an inexact science. A good first step in thinking about how to model bicycling demand is to understand the types of questions that the model might be used to answer. Porter, Suhrbier, and Schwartz (41) list three major questions, paraphrased as follows: • How many people will use a new facility? • How much will total demand increase given an improved facility or network? • How does bicycling affect public objectives such as reduced congestion and better air quality? This question could be added: What are the total benefits that bicycling creates, including the benefits to cyclists them- selves, such as improved health and recreational opportuni- ties? The answer to this and the previous questions could be useful in justifying public spending on bicycle-related pro- jects. The answers to the first two questions are likely to be

more useful for technical analyses to prioritize projects given limited resources. Another way of approaching the problem is to note that there are three different demand prediction objectives: • Predicting the total amount of bicycling in an area or on a facility, • Predicting the marginal amount that total demand will change given a change in facilities or policy, and • Identifying areas where inadequate facilities appear to be holding the level of bicycling below its potential, as in the “Latent Demand” approach (42). In principle, a model that explains the total amount of bicycling as a function of “basic” factors including demo- graphic, policy, and facility variables would answer all of these questions at the same time. Most past work has taken this kind of approach. The FHWA (43) and Texas Transportation Institute (44) completed major surveys of non-motorized mod- eling techniques in the late 1990s; the majority of the efforts they describe focused on predicting either commute shares or total bicycle travel by referring to characteristics of the pop- ulation and land use of the area being considered and to some measure of the bicycling environment. A second, less common type of demand prediction method uses census commute-to-work shares, often combined with other data, to provide an area-specific baseline of bicycle usage. This can substitute for some of the unmeasured fac- tors, attitudes toward bicycling in community and accessi- bility of neighborhoods to employment centers, that typically are found to have a large and often unpredictable impact on demand in the more “traditional” models. At the extreme, these represent two completely different ways of approaching the problem. Use of the traditional approach relies on an (often unstated) assumption that rela- tionships between demand and underlying explanatory factors will be stable over time and transferable from one place to another. The second approach relies more directly on what is already known to be true about demand in a given area. This method in principle is more limited because it does not directly relate demand to changes in the underlying environ- ment. However, within the short to medium time frames that most bicycling forecasts are concerned with, it is more accu- rate to base predictions on known facts rather than on theoret- ical and possibly unproven relationships. Section 2.21 describes the results of several surveys and other measurements of general bicycling demand that have been done during the last decade. The central aim is to describe the results of these many different measurements in one place to show that they are all roughly consistent when differing methodologies are considered and to place general bounds on the numbers that are likely to be observed. This information forms the basis for the demand prediction guide- lines. Section 2.22 describes the existing demand prediction literature as well as efforts to develop a demand model for 22 the Twin Cities area. The team does not describe existing literature in depth. It has already been well covered in recent reports. It does address applying these models to practical demand estimation, especially in situations where a planner is constrained by limited data, time, or technical expertise. These issues are explored in depth in section 2.23, which develops an argument that the common demand modeling objective to develop relationships between facilities and usage by comparing different geographic areas is unlikely to pro- vide useful results for a variety of reasons. These reasons are derived from the analysis of bicycling rates and from some findings from the team’s attempt to develop a demand model for the Twin Cities area. Measuring Bicycling Demand This section describes the results of several surveys and other measurements of general bicycling demand that have been conducted during the last decade. The first objective here is to combine the results of many different measurements to show that they are in fact all roughly consistent and to place general bounds on the numbers that are likely to be observed. The second objective is to demonstrate that the various mea- sures of bicycling demand can be reconciled by a conceptual framework in which there is a distribution of bicycle riding frequencies over the population (see Appendix A). Most of the information about the amount of bicycling addresses the number of people who ride bikes, as opposed to the number of trips or miles of riding. Because of the amount of information that is available about riding frequency, the team uses this as the measure of bicycling demand. At the end of this section, the team addresses how this can be converted into trips or distance calculations. The surveys and other sources that address the frequency of bicycling produce a wide variety of results. Each source asks about a different time frame; the number of people who ride a bike in a week will be larger than the number who ride in a day. A key distinction that has to be tracked is that adults are considerably less likely to ride a bike than are children, regardless of the time frame being considered. These two groups must be studied separately to avoid confusion or ambi- guity. This is generally not an issue with most bicycling sur- veys, which tend to focus on adults. It is, however, a factor in deriving numbers from general travel data collection surveys. In the ensuing discussion and tables, the numbers refer to adults 18 years old and older. In each case, the survey sam- ple was randomly selected households, regardless of their propensity or use of cycling. A summary of the findings of all these sources is shown in Table 6. Davis (50) takes a different approach by actually counting the number of bikes on a fairly large sample of roads and bike facilities in the Twin Cities and calculating the total amount of biking in the region. In this approach, there is no informa- tion on who is biking or why, but only an estimate of the total

amount being done (and where it is being done, to some extent). Davis’s approach provides a very powerful and objec- tive alternative to the biases that are always inherent in survey-based data (for example, the number of people who say they would consider commuting by bike exceeds by a factor of 20 the number who ever actually do). It is also a method that could be exactly duplicated in other cities and towns, providing an objective baseline of how much cycling actually takes place, and how it might vary by location, facil- ity, and even weather conditions. Finally, this method has the advantage over surveys of being a relatively inexpensive method for the amount of information that is generated. Some users cycle almost every day; others may only ride once per year. The longer the time frame being considered, the more people will have ridden at least once. It is possible to divide the population into different frequencies of riding in a way that is consistent with these numbers derived from differ- ent time frames. Table 7 shows an example of what such a breakdown might look like, based on trial and error. These riding probabilities and population frequencies are mathematically consistent with about 1% of adults riding in a given day, 5.3% in a week, 16% in a month, 29% in a sum- mer, 40% in a year, and 50% “sometimes” riding, although 23 not necessarily in a given year. Mathematically consistent here means that the fraction of each population frequency group who will ride during a given time span can be calculated using a simple probability formula, and the groups summed to arrive at a population total. Evidence from the TBI and NHTS, although not exactly consistent, can be interpreted to imply that the average person- day of cycling for an adult generates about 40 minutes and 7 to 8 mi of riding, although there is a great deal of variation around these averages. Modeling and Predicting Bicycling Demand The objectives in this literature review are to outline the general types of models and methodologies that have been used and to evaluate their potential usefulness to planners seeking demand estimates with practical value. The team con- siders three criteria that are likely to be important to planners: accuracy, data requirements, and ease of use. The focus is on the FHWA report (51) of 1999 because it is the most recent comprehensive survey. The earlier TTI report (52) provides more detail on specific models and methods but does not add to the breadth of the FHWA report. The major FHWA report documenting non-motorized travel estimation methods identifies five major methods. The first two of these, comparison studies and aggregate behavior models, are criticized by FHWA for their low accuracy. The low accuracy is derived from the difficulty of comparing one location with another (or transferring parameter values esti- mated in one location to another) because of the large impact of unobservable factors such as attitudes. While these meth- ods can be easy to use, and could require limited data, the dif- ficulty of not knowing the area(s) from which the demand numbers were generated severely limits its applicability. Source and Area Measure Average Range Travel Behavior Inventory, Twin Cities MSA National Household Travel Survey, U.S. Total (45) 0.9% - NHTS, U.S. MSAs NHTS, U.S. States NHTS, U.S. Total NHTS, U.S. MSAs NHTS, U.S. States Parkwood Research Associates (46) %/month Bureau of Transportation Statistics (45, 47) %/summer 27% Parkwood Research Associates (46) %/year 37%-46% National Sporting Goods Association (48) %/6 times per year 10.7% - Minnesota DOT (49) %/ever ride 50% - %/week 6.7% - %/day 1.4% - - 0.2%-2.4% - 0.0%-2.2% - 4.5%-12.7% - 3.5%-12.4% - 16.6%-21.2% TABLE 6 Measures of adult bicycling frequency Frequency of cycling % of adults 3 of every 4 days 0.1% 1 of every 2 days 0.2% 1 of every 4 days 0.5% 1 of every 10 days 1.2% 1 of every 20 days 3% 1 of every 50 days 10% 1 of every 100 days 15% 1 of every 200 days 20% Never 50% TABLE 7 Cycling frequency

Comparison studies attempt to predict bike use in one area or facility by measuring use in a similar area. However, it is difficult to know whether the two locations are similar. Areas identical in demographics and land use can generate bicycling rates that differ by a factor of 10 or more. Similarly, for aggre- gate behavior models, the fact that a certain relationship exists in one area between the amount of bicycling and certain explanatory variables generally does not mean that the same relationship will exist in other similar areas. Even if relation- ships were not consistent across places, these types of models could still be useful if the range of likely error is known and is relatively small. But this does not appear to be the case. The third method, sketch planning, is described as relying on data that already exist or can be collected easily, such as census data. This is the sort of model that is described later in this report. Sketch planning methods use readily available data such as commute to work shares from the census as a tool for estimating behaviors of interest, rather than estimat- ing these behaviors directly from underlying conditions. The FHWA report rates these methods as not being very accurate, although this assessment seems to be derived from the fact that these methods are simple and rely on limited data. It is not clear that the relative accuracy of sketch planning meth- ods compared with others has ever been formally evaluated. This method has been criticized for being difficult to apply accurately to other geographical areas. The team is not con- vinced that this is the case. While the relationship between commute shares and other measures of bicycling may not be perfectly consistent from one place to another, it does seem from the analysis to fall within a fairly limited and predictable range. The team believes that such methods could be quite accurate, especially when supplemented with local knowl- edge and judgment. Perhaps even more important, the degree of accuracy can be known with some precision. This can make the forecasts more useful to planners hoping to do a risk analysis. And, these methods can have other advantages of being very easy to use (and explain to policymakers) and requiring limited and easily accessible data. The last two methods described in the FHWA report, dis- crete choice models and regional travel models, are widely respected in the transportation profession because of their longstanding application to the forecasting of auto and transit travel. However, it is not clear that they are appropriate for understanding bicycle travel, in part because of the significant amount of data and technical expertise that is needed to exe- cute them and in part because “unobservable” factors play a greater role in determining the amount of bicycle travel than they do in either auto or transit.ii Both these types of models are based on the assumption that bicycle trips are made after considering a decision among a number of alternate modes. However, the evidence strongly suggests that a majority of bicycle trips are recre- ational in nature—a person going for a bike ride for fun probably did not consider whether to go for a drive or a bus ride instead. Recreational bike trips are probably made in 24 addition to, rather than instead of, auto trips; they would not be captured by a model that starts out by assuming that a given household will make a certain number of trips (as these models do). Ignoring recreational trips could be justi- fied if it were assumed that they had no value to society (or at least a very small value compared with a “utilitarian” trip) but the team is not willing to make this assumption, and indeed the benefits analysis indicates that it is probably not true. A good model of bicycling demand should capture all types of bike trips. To these criticisms could be added that the accuracy of these models is unproven in the context of bicycle forecast- ing. FHWA rates these types of models as highly accurate, but it is not clear whether this rating is based on actual com- parisons with other models or on their complexity and high data needs. To supplement the research associated with this task, the team conducted original research based in the Twin Cities metropolitan area. This application develops a disaggregate model relating bicycle facilities to the probability of an indi- vidual riding a bicycle in a given day. The work is described in detail in Appendix B. The following text provides an overview of the methods and results of this analysis. The primary aim of the investigation was to understand the effect of proximity to a bike facility on the odds of cycling. In other words, does living closer to a bike facility increase the likelihood of traveling by bike? The hypothesis is that subjects living in closer proximity to a bike facility will be more likely to travel by bike compared with those who live more than 1 mi from the nearest bike facility. The outcome of interest (any bicycle use in the preceding 24 hours) was ascertained from standard travel data fur- nished by the Twin Cities Travel Behavior Inventory. The explanatory variable (or exposure) of interest is the proxim- ity of bicycle facilities in the form of on- and off-road bicy- cle lanes and trails. Three continuous distance measures were calculated using global information system (GIS) layers fur- nished by the Minnesota DOT, with separate map layers for on-street and off-street trails. Using household locations (x-y coordinates) and the GIS map layers, the distance in meters was calculated to the nearest on-street bicycle lane, the nearest off-road trail, and the nearest bike facility of either type. Distance variables were used to classify subjects into one of four categories. The four categories represent the dis- tance from home to the nearest bike facility as less than 400 meters (one-quarter mi), 400 to 799 m, 800 to 1,599 m, and 1,600 m or greater (greater than 1 mi). Given that dis- tance cut-points with relatively simple interpretation were used, it provides a compelling way to grasp the reported findings in terms of comparing individuals who live within 400 m of a bike trail and those who live more that 1,600 m from a bike trail. Attributes of the built environment are theorized to influence the likelihood of cycling—namely, having destinations to which individuals can bicycle matters. Three spatial attributes were measured that are indicative of

one’s home location—open space, regional accessibility, and neighborhood retail. For the sample of central city residents studied, 86 subjects (4.8%) had at least one documented bike trip. This rate is higher than both the larger TBI sample and national averages, which tend to hover around 1 to 2% of the population (53). As expected, the proportion of bikers varied with proximity to bike facilities, with more bikers living closer to bike trails and fewer bikers living further from bike trails. Of interest, these distributions differed depending on which measure of bicycle facility proximity was used. A priori, it was assumed that the type of bicycle facility matters, that is, the type of bike facility may have different effects on the likelihood of bicycle use. Therefore, the team used separate models to estimate the effect of proximity to off-road facilities on the odds of bike use. Examining the simple logistic regression model to the fully adjusted model for off-road bicycle facilities, the odds of bike use did not dif- fer significantly by proximity to a trail. No effect of proxim- ity to off-road bike facilities on bicycle use was detected (see Appendix B for details). Finally, the effect of proximity to on-road bike facilities on the odds of bike use was examined. Using a series of logis- tic regression models, it was found that subjects living within 400 m of an on-road bike facility had significantly increased odds of bike use compared with subjects living more than 1,600 m from an on-road bike facility. As expected, those that lived within 400 to 799 m of an on-road bike facility also had significantly increased odds of bike use compared with subjects living more than 1,600 m from an on-road bike facil- ity, although the odds of bike use were slightly lower than for those living closest to an on-road facility. After adjusting for individual, household, and neighbor- hood characteristics, the effects were somewhat attenuated. Subjects living near an on-road facility (less than 400 m) still had statistically significantly increased odds of bike use com- pared with subjects living more than 1,600 m from an on- road bike facility. Subjects within 400 to 799 m still tended toward increased odds of bike use, but this failed to reach the level of statistical significance. While not the focus of this analysis, this part of the study reaffirmed that many of the socio-demographic and economic variables used in other studies are important. Bicyclists are more likely to be male, to be college educated, to come from households with children, and to have higher income. Discussion of Prediction Methods Traditional approaches to modeling bicycle demand derive at some level from the standard methods used for forecasting auto travel (i.e., they start from basic information about the people and the transportation environment in an area and use this in some way to predict an amount of bicycle travel, either directly, or as the solution to a mode choice problem in a larger travel model). 25 This section discusses some issues with using this approach to model bicycling demand. The arguments are based in part on some of the facts about bicycling discussed in the previ- ous section, and in part on some preliminary findings from an original attempt to estimate a demand model for the Twin Cities area. While this model is not described here, in part due to the lack of useful results, it is used to illustrate some of bicycle demand modeling issues more generally. There are several reasons why a bicycling demand model derived from basic information is not likely to be accurate or useful. These can be illustrated in part by the team’s own attempt at developing a demand model, in which was found a statistically significant result that off-road paths were associ- ated with lower rates of bicycling. This result is counter- intuitive. Davis (50) found that off-road facilities in the Twin Cities are more intensively used than other options. The team’s result was not due to an obviously underspecified model; a wide variety of demographic and land use variables were included in the regressions. There are several reasons for this outcome. One is a possible shortcoming related to measurement; the manner in which facilities were defined did not correspond to how people perceive them. For example, many of the sub- urban “off-road” facilities run next to busy highways, with all the associated crossing of driveways and roads. They are off- road in the sense that there is a barrier separating them from the road, but they are not off-road in the sense of eliminating potential conflicts or of being appealing facilities on which to cycle. For example, it is conceivable that elaborate bicycle modeling efforts would incorporate traffic volumes on major streets, travel times by bicycle (given traffic signals and other sources of delays), crash locations, or number of street cross- ings by off-road paths. Such data are available in many met- ropolitan planning organizations. Other issues and factors include lane width, pavement quality, and the presence of on- street parking. These measures were not captured because they are considerably more difficult to obtain. Proximity to high traffic corridors along a route also has important implications. It would be useful to have information about impedance fac- tors along a specific route, difficulties with the transit/cycling interface, or other issues. These factors are important and the fact that they were absent from the data might limit the broader applicability of the results. This problem is only compounded when trying to develop a model based on results in different locations because cities may have different ways of defining and measuring their own facilities. The second issue with this sort of model is that there are very large and seemingly random differences from one place to another. In one area in Minneapolis, 16% of the adults made bike trips on the day they were surveyed, while the rate in many other areas was 0%. Even across entire metropolitan areas the differences can be large; the metropolitan areas and states with the most bicycling can have rates that are 10 times that of the places with the least. While there are some well- documented population and land use characteristics that are associated with higher levels of bicycling, the impact of these

factors can account for only a small fraction of the total vari- ations that are observed. Other, seemingly unobservable atti- tudinal and possibly historical factors seem to dwarf the effect of the factors that planners and policymakers can control. The report contains findings for residents of Minneapolis and St. Paul. The team is not convinced that these findings are applicable to other locations. Twin Cities’ residents may dif- fer from those of other places with respect to lifestyles and preferences for bike use. Even within the Twin Cities area, the same regressions yielded different results depending on the geographical scope of the study area, that is, including sub- urbs in the analysis changed the results. The options, avail- ability, and manner in which bicycle facilities are valued likely differ substantially between populations, especially urban versus suburban (54). Because the impact of the unobservable variables are large relative to the variables of interest, it is likely that what is being observed, both in this model and in others, is the effect of atti- tudinal variables acting on policy variables through spurious correlations. What seems to have been observed in this model are geographic spikes in bicycling. These spikes happened to be positively correlated with some facility measures and neg- atively with others, but that in a causal sense had little or noth- ing to do with any of them. While other work of this type in the literature has typically not had to deal with quite such a wide range of bicycling levels, it seems probable that these types of correlations might be driving their results to a large extent, too, given the typically low explanatory power of these models. A third issue is that because the level of bicycling is so low, the range of sampling error can be many times larger than the sample mean for any realistic sample size. The effect is that the regression is trying to predict (measured) variable values that could be off by a factor of five or more from their true values. A sample of 1,000 people would yield 9 cyclists on a given day at the national average level; the 95% confidence interval for this sample ranges from 3 to 15 cyclists. This is a very big dif- ference in relative terms. Seemingly very high or low levels could easily just be sampling aberrations. Yet these inaccurate measurements could strongly influence the estimated parame- ter values. Obviously this will be a problem with any model of bicycling behavior, but it seems likely to be compounded by the need in traditional models to incorporate a large number of explanatory variables. Finally, there is always the problem that even a positive correlation between riding and facilities could be causation in the other direction—that is, the large number of cyclists creates the political climate to build the facilities in the first place. Given limited funds, agencies are unlikely to spend them on striping bike lanes unless there is a problem such as crashes, or bikes and motor vehicle conflicts. Thus, retrofit- ted lanes are probably a response to existing cycling to a large extent, and thus will naturally be associated with high levels of cycling after they are built. Indeed, the other main “result” of the Twin Cities model was that on-street bike lanes were very strongly and positively associated with increased riding. By contrast, lanes in some newer cities in California do 26 not seem to have high riding levels (55), possibly because they were designed into new roads, that is, built in anticipation of riding rather than in response to it. Seemingly, the only way around these problems would be to study the same geographic area over a period of time as facil- ities change. The relevant comparison is not comparing people living at location A to different people living at location B, but rather comparing the people at A with themselves as the provi- sion of facilities changes over time. This would be an expen- sive prospect using surveys; development of a low cost method of counting bikes over a large number of different streets and bike facilities would be of great value for this purpose. A SKETCH PLANNING METHODOLOGY This section outlines a simple sketch planning method to estimate the number of daily bicyclists in an area using readily available data. This method could be used for gen- eral political purposes, justifying expenditures by reference to the number of bicyclists and the benefits that they receive from cycling. It could be used to estimate demand on new facilities by assuming that it will be some fraction of the total amount of riding in the surrounding area. Finally, this method could be used indirectly to estimate changes to the amount of cycling resulting from facility improvements, assuming that changes will be some (probably small) fraction of the existing total. Discussion The basic assumption that motivates the model described here is the idea that a large portion of total bicycling is done by a small fraction of cyclists who ride frequently, and that many of these frequent riders are bike commuters who will be observed as such in the census commute to work data. The assumption is that the basic riding frequency table described in the previous section will hold more or less across different areas, so that an area with a lot of commuter cyclists will also have a lot of total cycling, and an area with few commuters will have little total riding. In other words, commuting by bike, while it is a small fraction of the total bicycling in a given area, can still be used as a “leading indicator” of what might be happening with other types of cycling. The team used three different geographical divisions to study the relationship among commute shares, total daily bicycling rates, and total weekly bicycling rates. These divi- sions were metropolitan statistical areas, states, and zones of 10,000 to 30,000 people within the Minneapolis-St. Paul area. The results of these analyses are described in more detail in Appendix A. The primary results are summarized in the following subsections, along with some key facts from the demand measurement analysis. On any given day, roughly 1% of the adults in the United States ride a bicycle. Over large geographic areas such as met-

ropolitan areas or states, this number could range roughly between about 0.3% and 2.5%. Over smaller areas such as spe- cific parts of metropolitan areas, the range could go as high as 15%. The possible range can be reduced somewhat for a given area by considering the bicycle commute to work share. The best fit for a regression relating percentage of adults who bicy- cle in a given day to the census bike commute to work share varies considerably across different sized geographic units. However, based on this analysis, it appears that bounds can be placed on the range of values that are likely to be observed. The observed lower bound for the number of daily adult bicy- clists is equal to the commute share (even though in this case there are more total bicyclists than commuters, since they are calculated from different denominators). A “most likely” value would be 0.4% plus 1.2 times the commute share; this was the best fit at the MSA level, and also describes the United States as a whole. An upper bound would be about 0.6% plus 3 times the commute share; this is slightly higher than the slope observed at the neighborhood level. Two important points are worthy of note. First, the range is large in relative terms; bicycling days per adult are 8 to 10 times larger in the high-bicycling cities and states compared with the lower level places. The difference between neigh- borhoods within a city can be even larger. These variations seem far larger than can be reasonably explained by differ- ences in formal policies and facilities, especially given that some low-cycling areas have similar circumstances to other high-cycling areas. It seems that local attitudes and perhaps history play a substantial role in the perception of bicycling as an appealing or normal thing for an adult to do. Thus, these guidelines leave considerable scope for the planner to apply local knowledge and judgment to modify the estimated range of demand levels estimated. The second important point is that while the range is very large in relative terms, it is very small in absolute terms. An estimate of total usage or total benefits in a given local context is unlikely to be off by very much in absolute terms because the numbers overall are so small. Because costs tend to be rel- atively small as well, even somewhat inaccurately predicted demand is unlikely to lead to poor decisions on major invest- ments. The demand model outlined should be accessible to decision-makers and provide a known range of outcomes.iii Description of Process The process for estimating the increase in demand due to a facility is based on two steps derived from the team’s research described in this chapter and in the Appendix A and B. The first step uses the bicycling commute share in the area around the proposed facility to generate low, medium, and high estimates of the total number of current adult bicy- clists in the area (i.e., the average number on a given day). 27 This is a baseline level of demand. Then the findings for the higher levels of bicycling for residents in the immediate vicinity of a facility are applied to this baseline to estimate the rates that would exist after the facility is built. This process is described in more detail in Chapter 4. It should be noted that this process is estimating the daily average number of adult bicyclists from the area around the facility, based on observed relationships from around the United States. It is not estimating the number of people who will actually use the facility itself. A given facility will likely be used by many people who do not live near it, and some local residents may ride but not on the facility. For purposes of esti- mating benefits, one wants to know the number of cyclists in general (on or off the facility), and how the presence of a facility might impact that number, based on empirical obser- vation. Estimating total users, including those from outside the immediate area, would have required a level of data that is not available and details of local geography that would be hard to account for in a general way. Once the demand estimate has been reduced to a range of possible values based on readily available data and a set of tested relationships between different measures of bicycling demand, the user can apply his or her own judgment and local knowledge to choose a most likely point within the range that has been determined. It could be that circumstances are so unusual in a given situation that the user will even want to choose a point outside the recommended range. The kinds of factors that the user might want to consider in this step could be things like design details of the facility, spe- cial local land uses that could affect bicycling demand, how the facility might fit into a larger system, and so on. These kinds of factors would not be included in the primary demand range estimate for two reasons. First, there is no compelling evidence in terms of how these factors affect demand or that the effect is sufficiently predictable and reliable that it can be included in a model that can be applied across a variety of locations. For example, universities tend to be associated with higher levels of bicycling in general, but the size of this effect seems to be highly variable depending on the specific situa- tion. Second, the number of possible local details would be so large as to be unmanageable, especially when there is no basis for attaching specific numbers to most of them anyway. Although examples can be listed to prompt the user to think of additional local factors, putting together a comprehensive list would be nearly impossible. The demand estimation method, in summary, provides a range of possible demand levels for a given situation, based on a simple method derived from high quality, nationally consistent data and from well-tested relationships among various measures of bicycling demand. The user would then be able to choose a most likely estimate based on this range by applying local knowledge and judgment regarding other more qualitative factors.