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28 CHAPTER 3 BENEFITS ASSOCIATED WITH THE USE OF BICYCLE FACILITIES PREVIOUS APPROACHES A key to encouraging bicycling and walking is to ensure that adequate facilities exist to use these modes. For walking, this includes sidewalks, public spaces, and street crossings. For bicycling, this includes relatively wide curb lanes, on- street bike lanes or off-street bike paths, and even parking and showers at the workplace. But bicycle facilities cost money, their merits are often called into question, and many consider spending on them a luxury. Planners and other transportation specialists often find themselves justifying that these facili- ties benefit the common good and that they induce increased use. Especially in austere economic times, planners are often looking for ways to economize such facilities. Urban planners, policy officials, and decisionmakers have lacked a consistent framework from which to understand the merits of such facilities. These officials are often presented with information on how much these facilities cost. Opponents of bicycle projects consistently use such information to con- tend that trimming particular projects would preserve funds that could be used for other purposes. Cost data are readily obtained; it is relatively straightforward to account for the acquisition, development, maintenance, and other costs for site-specific or aggregate cases. The benefits of such facili- ties, however, are considerably more difficult to estimate. To respond to such policy and planning needs, the purpose of this section of the report is twofold. The first is to review and interpret existing literature evaluating the economic benefits of bicycle facilities. The second is to suggest methods and strategies to create guidelines. The purpose of a framework for organizing and catego- rizing benefits is to provide a clear means of identifying the myriad benefits being discussed and who benefits from them. This is important because different types of benefits, even if they appear similar, may be of different magnitudes, or stem from different policy decisions or facility invest- ments. The benefits of bicycling are largely a function of the amount of cycling; they will invariably depend on finer de- tails such as the location, purpose, person cycling, and char- acteristics of the facility being used. Understanding the size of the benefit requires at least a reasonable estimate of how large they are in oneâs own local area. Information on the size of benefits will be useful in justifying expenditures on cycling in general; while understanding how they might change over time will help in evaluating and prioritizing spe- cific investments. OVERVIEW OF ISSUES To estimate the economic benefits of bicycle facilities it is necessary to provide an overview of the main issues involved, the matters that confound such endeavors, and a justification for more structured research. The overarching issue is reliably determining an economic value for a facility for which there is no market value and lit- tle data for its use. Bicycle facilities, like wilderness, a clean environment, and access to open space, represent non-market goods not bought or sold. There are no prices for their use that can be manipulated and, as a result, they represent a good for which it is extremely difficult to derive an economic value. Furthermore, given current levels of bicycling use, one per- sonâs use does not interfere significantly with anotherâs and the costs of restricting entry to the facility outweigh any rev- enue that could be raised. Bicycle facilities exhibit character- istics closely resembling what economists call âpublic goods.â But if certain goods are thought to contribute positively to human well being, they are considered to have economic value (the reverse is also true). Under these circumstances, literature from the field of economics and transportation has devised general methods for estimating economic values attached to non-market goods and services. These include methods to measure both revealed and stated preferences for a good. Revealed preferences are used to identify ways in which non-market goods influence the actual market for some other goods and are estimated using methods such as hedo- nic pricing, travel cost, or unit day values. Stated prefer- ences are used to construct markets, asking people to attach an economic value to various goods and services and are esti- mated using methods such as contingent valuation or con- joint analysis. Measuring any aspect of bicycling facilities is also compli- cated because discussion of transportation facilities typically considers matters in terms of auto, transit, or non-motorized travel; doing so aggregates walking and cycling. For abstract or general purposes, this may suffice and is often done in transportation research. In terms of daily use and facility planning, however, bicycling and walking differ significantly.
Pedestrian travel and infrastructure have the following unique characteristics. First, all tripsâwhether by car, rail transit, or busârequire pedestrian travel because they start and end with a walk trip. Second, sidewalks and other pedestrian related amenities are often standard requirements in zoning codes. Third, pedestrian concerns typically relate to relatively con- fined travel-sheds or geographic scales (e.g., city blocks). Bicycle travel and facilities, on the other hand, tend to apply to longer corridors, fail to be used as readily and frequently as walking facilities, and are therefore considered more discre- tionary in nature. Most important, whereas pedestrian plan- ning applies to a clear majority of the population (nearly everyone can walk), bicycle planning applies to a consider- ably smaller market of travelersâthose who choose to ride a bicycle. During the summer months in most of the United States, this includes slightly more than one-quarter of the population (47). Poor data are a concern for all analysis of non-motorized transportation (bicycling or walking). There exists a variety of sources from which basic bicycle behavior can be deter- mined, for example, the census, metropolitan/nationwide travel surveys, facility specific surveys or counts, and national surveys such as that administered by the Bureau of Trans- portation Statistics (47). Specific use and facility information may be available for select areas throughout the country. The strengths and weaknesses of these data sources are adequately documented in a report issued by the U.S. Department of Transportation (56). A common theme is that existing behav- ioral bicycle data lack the breadth and quality necessary for reliable analysis. Analysis of cycling use has been especially marginalized because of the relatively low levels of bicycling (compared with other transportation modes). Such data deficiencies are recognized by the transportation planning community, and procedures and protocol for bicycle data collection are improving. Bicycle and pedestrian travel are increasingly apparent outside the transportation commu- nity, including in matters related to livability and public health. For example, transportation and urban planning researchers are joining forces with public health researchers to better under- stand both derived and non-derived forms of âactiveâ trans- portation (i.e., bicycling and walking). These improvements are noteworthy and will surely benefit transportation research. There remains considerable range in how to measure ben- efits of bicycle facilities. Reviewing past research on the sub- ject in a systematic manner is challenging. Geographic scale, research depth, overall quality, and focus of past study vary considerably. The research is not cumulative (i.e., studies do not build on previous efforts). It is also challenging to find such research. The research team cast a relatively wide net to identify papers on bicycle benefits. The teamâs definition includes any research effort describing or attributing an eco- nomic value to bicycling or bicycle facilities. These studies are described in detail in Appendix C. A review of the studies suggests there are at least four issues that confound research on this topic: (1) what is the geo- 29 graphic scale or type of facility? (2) who benefits from the facility? (3) which benefits apply to the facility? (4) what units and methods are used? How does one compare the economic benefits gained from Coloradoâs mountain biking industry to the quality of life or neighborhood-scale benefits from build- ing a neighborhood bike path for children? How do the air pollution benefits of increased cycling relate to quality of life benefits from the serenity of a nearby rail-trail? How reliable are the safety estimates for different types of bicycle facili- ties, especially given existing debate over on-road versus off- road facilities (57, 58)? The studies and approaches to date represent initial attempts to understand such benefits. They often do so, however, by estimating them over inconsistent geographic scales and making a variety of assumptions (some of which go unstated or are extremely case specific). Each consideration is described in the following subsections. What is the Geographic Scale or Type of Facility? The first consideration pertains to the geographic scale of the inquiry or facility in question. Past work has analyzed the benefits of a specific greenway or active recreation trail (59â 65), a specific trunk roadway (66), a region (67, 68), an entire city (69), or an entire state (70). Some studies focus on a system of bicycle trails across the state. Others focus on the benefits of on-road versus off-road facilities. Different geo- graphic scales demand different data requirements, ranging from individual counts of a facility to aggregated counts or numbers for a specific area extrapolated to an entire state. Who Benefits from the Facility? A second matter relates to the population for whom the ben- efits apply. Benefits can be determined in a number of ways depending on the audience of interest and the geographic scope. State legislators may be interested in understanding how bicy- cling, the bicycle industry, or bicycle-oriented tourism impacts a stateâs economy. Such analysis would resemble input/output models examining expenditures across an entire state. In contrast, a city council member may seek to learn how bicy- cle facilities enhance quality of life for a given municipality. Advocates want to document induced or latent demand for facilities and possible relationships to decreased traffic con- gestion. Public health professionals are concerned about the safety benefits of such facilities. Can a single review do justice to the myriad interests and beneficiaries involved? This depends on the level of speci- ficity and need of the study. There are competing interests and multiple perspectives to capture. While actual users are likely to be the same for any given facility (i.e., people rid- ing bicycles), the information likely to be of benefit to the state bureau of tourism differs from a municipality looking to justify different types of bicycle investments.
One report identifies three user groups impacted by cycling facilities: road users, non-road users (e.g., occupants of adja- cent properties), and planning/financing agencies (66). The first group of road users includes all users, cyclists, motorists, pedestrians, horse riders, and public transport. Alternatively, some studies divide the benefits of non-motorized travel into internal versus external benefits. The former include the finan- cial savings, health benefits, increased mobility, and overall enjoyment for cyclists; the latter include the benefits to others, such as reduced (a) congestion, (b) road and parking facility expenses, (c) motor vehicle crashes, (d) air and noise pollu- tion, and (e) natural resource consumption. One of the most contentious issues is adequately account- ing for benefits accrued by cyclists of different ages. Adults and children value different types of facilities. Children enjoy trails for recreational purposes. Programs such as âSafe Routes to Schoolsâ are also becoming important. However, it is dif- ficult to obtain reliable data for an adult cycling population much less for a population of children. For this reason, almost all analysis of bicycle facilities, including the research reported herein, is based on the preferences of an adult population. Which Benefits Apply to the Facility? The range of benefits of cycling facilities include, but are not limited to, reduced pollution, congestion, capital investments (at least compared with roads and auto use), and increased livability, health, well-being, and quality of life. But anecdo- tally describing such benefits has limited value. Politicians and lobbyists seek reliable and quantifiable estimates. Spe- cific benefits range from the direct and easy-to-understand to the difficult to reliably calculate. Counting the number of cyclists using a new bicycle trail is relatively straightforward after the fact. The challenge is translating such levels of rid- ership into an economic value. One study suggests seven benefits to consider when esti- mating the economic value of walking: livability, accessibil- ity and transportation costs, health, external costs, efficient land use, economic development, and equity (71). Focusing just on greenways, Lindsey (72) articulates six valued bene- fits: recreation, health/fitness, transportation, ecological bio- diversity and services, amenity visual/aesthetic, and economic development. Which benefits are most important? Is it those that are accrued, those in which the sponsoring agency is pri- marily interested, or those for which there is available data? What Units and Methods are Used? The last issue involves the units and methods used to calcu- late different benefits. An ideal analysis considers benefits in a framework using a common unit. But how does an increase in riders compare with a reduced need for parking spaces? How does increased livability compare with decreased health concerns? With adequate data, it would be possible to count 30 riders on existing facilities and possibly determine induced riders on a new facility. However, this is just one benefit and it remains unclear to which categories it applies. One article focuses exclusively on methods, reviewing the Travel Cost Method (TCM) to determine economic value and suggesting better alternatives for measurement (73). When it comes to estimating, many studies âguesstimateâ to solve the problem. Each of the methods and units are different, yielding varied output that precludes the desired aim of a common unit. Previous work provides the most precise guidance by sug- gesting a unit by which each characteristic could be mea- sured. These range from simple counts (e.g., reduction of casualties) to decibels to monetary amounts (e.g., vehicle operating costs) to descriptive measures (e.g., overall con- venience). More often, general measuring techniques are offered. For example, it is suggested that hedonic pricing could be used to measure livability or amenity visual/aesthetic val- ues; economic input/output models could describe economic development; time could be used to measure transportation savings; and surveys of different kinds (e.g., contingent val- uation) could be used to capture a host of values or benefits. PROPOSED BENEFITS AND METHODS Past research offers varying perspectives on the bicycle facility information different audiences require. The central challenge for urban planners, policy officials, and researchers focuses on the benefits of bicycle facilities that pointedly sat- isfy certain criteria. After reviewing existing literature, can- vassing available data and methods, and consulting a variety of policy officials, the team has determined that bicycling benefits need to satisfy five criteria: (1) be measured on a municipal or regional scale, (2) be focused on transportation and urban planning, (3) be estimable via available existing data or other survey means, (4) be converted to measures comparable with one another, and (5) be measured benefits for both users and non-users (i.e., the community at large). It is also important to describe the range of benefits, to whom they apply, and to suggest compelling methods in which they could be measured. The list of benefits is guided by previous research and includes direct benefits to the userâin the form of mobility, health, and safety benefitsâand indirect benefits to societyâin the form of decreased externalities, increased livability, and fiscal savings. Other benefits certainly exist, but the beneficiaries are not always clear. The aim is not to dismiss their significance but merely suggest that practical considerations related to data, methodologies, and measurement often preclude more detailed analysis. The benefits mentioned usually have dif- ferent beneficiaries. These range from society-at-large to individual users (potential and current) to agencies; there is crossover between beneficiaries for each benefit. Consider, for example, that the most common argument in favor of cycling suggests that an increase in facilities will result in
increased levels of cycling. This assumed increase in cycling will be derived from (1) existing cyclists whose current lev- els of riding will be heightened (because of more attractive facilities) and (2) potential cyclists whose probability for rid- ing will be increased. Thus, there are potential benefits for two different populations (current and potential cyclists). But if any of these heightened levels of cycling result in decreased auto use, then a third beneficiary resultsâsociety-at-largeâ in terms of reduced congestion and resource consumption. Descriptions follow of what each benefit refers to, the pri- mary user group to whom it applies, and a thumbnail explana- tion of strategies that could be used to measure such benefit. The proposed method is not to imply there is a single strategy for estimating this benefit but merely to provide the reader and researcher with an example of how it could be measured. Figure 1 (in the Summary) shows a simplified depiction of potential beneficiaries along with an indication of the primary benefit. Mobility The most directly cited benefits are often from bicycle facility users. These come in the form of greater satisfaction of existing cycling (e.g., cyclists would be able to reach their destination faster, safer, via a more attractive means). How- ever, existing information by itself (e.g., ridership counts) cannot reliably shed light on this issue. For this reason, the different transportation benefits for the user are best uncovered through stated preference surveys or experiments. Because stated preference methods provide individuals with hypo- thetical situations, it becomes feasible to analyze situations that are qualitatively different from the actual ones seen in practice (74). Because individuals respond to several different hypothet- ical choice situations offered to them, the efficiency of data collection is improved; enough data is hence available to cal- culate functions describing their preferences or utility. The disadvantage in stated preference methods is that people may not always do what they say. Individualsâ stated preferences might not be the preferences they actually show (75). The differences arise because of the systematic bias in survey responses or because of the difficulty in carrying out the posed task. Two techniques used in stated preference analyses are contingent valuation and conjoint analysis. Contingent valu- ation is based on the premise that the best way to find out the value an individual places on something is by asking. Like other non-market goods, the concept has been applied to wilderness, open space, or even more specifically to green- ways (76). The second stated preference technique, conjoint analysis, uses experiments to obtain the preferences of the customer. This market research technique can provide important information about new product development, fore- casting market segmentation, and pricing decisions. In this 31 case, it would help to understand the type of cycling facili- ties residents value. Conjoint analysis enables researchers to calculate the value that people place on the attributes or fea- tures of products and services; the aim is to assign specific values to the options that buyers look for when making a decision to use a good. It is a technique used to explore trade- offs to determine the combinations of attributes that satisfy the consumer. In these cases, an individual is provided a choice of alter- natives; for example, the various travel routes by which a particular travel destination can be reached. The choice of a particular mode is assumed to depend on the relative attrac- tiveness of the various travel options that the individual faces. These methods use experimental procedures to obtain preferences based on the individualâs evaluation of the vari- ous options given. Typically, these experiments provide hypothetical travel scenarios to obtain an individualâs pref- erences (77). Stated preference surveys need to be stratified by audi- ence: current users versus potential users. Current cyclists could be asked to respond to questions about factors that would provide for a more attractive cycling environment through different types of environments or facilities. It is necessary to have forced trade-offs so that a better environ- ment might be coupled with higher costs for bicycle storage or a higher travel time. This will allow one to value each component of the userâs preference. These preferences can then be translated to economic benefits using consumersâ surplus measures (78) to determine, for example, the value of an off-road bicycle facility for users of that facility. For potential users, it is important to create scenarios based on constructed markets, asking people to attach a value to goods or services. This technique quantifies the benefits that non-bicycling residents would accrue from a more desir- able bicycling infrastructure. For example, questions could be what mode they would choose for work and non-work trips based on the quality of the transportation environment, including travel by auto, walking, transit, and bicycle. It would query residents about the degree to which they perceive dif- ferent bicycling services or how facilities will improve the conditions of their commute, recreational activities, and so forth. By measuring how demand might change, one can ascer- tain the preferences for current non-users, some of whom would become users if a certain infrastructure package were constructed. The teamâs approach to determining user mobility benefits is described in detail in Appendix D. It quantitatively evalu- ates individual preferences for five different cycling envi- ronments. The respondent is asked to trade off a higher travel time as a cost incurred to choosing a better facility while allowing the respondent the option of selecting a less attrac- tive facility at a lower travel time. The trade-off of travel time to amenities of a particular facility determines the value attached to different attributes such as bike lanes, off-road trails, or side street parking. The facilities considered in this
application are off-road facilities, in-traffic facilities with bike lane and no side street parking, in-traffic facilities with a bike lane and side street parking, in-traffic facilities with no bike lane and no side street parking and in-traffic facilities with no bike lane but with street side parking. The results indicate that respondents are willing to travel up to 20 min more to switch from an unmarked on-road facility with side parking to an off-road bicycle trail, with smaller changes for less dramatic improvements. Health Scientific literature from researchers and practitioners from a variety of disciplines show relationships between com- munity design, transportation facilities, and levels of physical activity (79, 80). âSprawlingâ land use practices and result- ing auto-dependent travel are themes that now have moved to the front of the American consciousness; the link to pub- lic health and obesity remains an important component of this discussion (81â83). One goal of this research is to learn the extent to which rates of physical inactivity can be linked to features of the built environment (see Krizek et al. [84]). At a regional or neighborhood level, most inquiries focus on land use patterns characterized by relatively scattered, single use and low-density development. At a street or facility level, such research focuses on access to sidewalks, trails, other non- motorized facilities, and destinations. While recent research has linked neighborhood design to travel behavior (85, 86), little of it has exclusively focused on relationships between specific facilities, bicycling and walking travel, and levels of physical activity. To establish a health-care, cost-based reason for bicycle facilities, several types of specific empirical evidence must be gathered and communicated to interested parties. Using reasoning from Goetzel et al. (87), researchers must first demonstrate relationships between a given feature of the built environment (e.g., a bicycle facility) and levels of cycling. This activity is similar to methodologies previously described to measure the demand induced from various facil- ities. International research on this question is likely to have reliable results that can enhance this line of inquiry in rela- tively short order time (i.e., a couple of years). Second, any amount of induced cycling that could be âteasedâ out from a facility would then need to be translated into an average per- centage of oneâs weekly physical activity. For example, the daily recommended level of physical activity is defined as 30 min of moderate physical activity on 5 or more days per week (88, 89). Cycling 5 mi in 30 min or 4 mi in 15 min would meet these current public health guidelines for physi- cal activity (90â92).iv Third, researchers must then demonstrate that lack of phys- ical activityâbecause it is indicative of certain risk factorsâ imposes a financial burden to the individual or to society. A fourth step would be to show that improving certain risk fac- 32 tors (i.e., increasing physical activity) does result in reduced cost. The final step is for researchers to demonstrate that health habits can be changed and that the resultant lower risk can be maintained over time. As can be seen, the challenges associated with documenting a health financial payback from a bicycle facility are significant. Looking at the problem opti- mistically and from the perspective of needing analytical jus- tification, such exercise is not completely out of the realm of possibility. For this reason, these later steps (three through five) constitute the focus of the following review. The benefits of physical activity in enhancing overall health are well established. The task of attaching monetary value to levels of physical activity is a more challenging endeavor. One attempt is offered by Wang et al. (93) who derive cost- effectiveness measures of bicycle/pedestrian trails by dividing the costs of trail development and maintenance by selected physical activity-related outcomes of the trails (e.g., number of trail users). The average annual cost for persons becoming more physically active was found to be $98; the cost was $142 for persons who are active for general health, and the $884 for persons who are active for weight loss. Estimating the effect of physical inactivity on direct med- ical costs is a strategy more often employed, though con- siderably less straightforward. Part of the reason for ambi- guity in this research is that the amount of physical activity required to realize certain health benefits is relatively unknown (i.e., what is the elasticity?) (88, 94, 95). In the field of pub- lic health, this matter is often approached from the perspec- tive of dose-response relationships. The aim is to learn what change in amount, intensity, or duration of exposure (in this case, cycling) is associated with a change in risk of a speci- fied outcome (in this case, cost of health care). Existing literature examining relationships between levels of physical activity and health costs varies considerably in methodology and scope. The majority of existing studies pur- sue a dichotomized approach, separating respondents into two classes: those that satisfy an accepted dose of 30 min per day for 5 days and those who do not. In this first group of studies, there are at least five statewide reports whose methodology and assumptions are relatively general in nature. In most cases, estimates are derived from an aggregation of medical expen- ditures that can in some form be traced back to physical inac- tivity. For example, a study commissioned by the Michigan Fitness Foundation (96) concentrated on the economic costs to the residents of Michigan. The authors used estimates (acknowledged to be conservative) to derive direct costs (e.g., medical care, workersâ compensation, lost productivity) and indirect costs (e.g., inefficiencies associated with replace- ment workers). The final amount totaled $8.9 billion in 2003 ($1,175 per resident). A 2002 report from the Minnesota Department of Health (97) estimates that in 2000, $495 mil- lion was spent treating diseases and conditions that would be avoided if all Minnesotans were physically active. This amount converts to more than $100 per resident. Additional reports claim that too little physical inactivity was responsi-
ble for an estimated $84.5 million ($19 per capita) in hospi- tal charges in Washington State (98), $104 million ($78 per capita) in South Carolina (99), and $477 million in hospital charges in Georgia ($79 per capita) (100). These reports from various state agencies are complemented with more academically oriented research. For example, Colditz (101) reviewed literature on the economic costs of inac- tivity and concluded that the direct costs for those individuals reporting lack of physical activity was estimated to average approximately $128 per person. A separate analysis by Pratt et al. (102) analyzed a stratified sample of 35,000 Americans from the 1987 national Medical Expenditures Survey. Exam- ining the direct medical costs of men and women who reported physical activity versus those who did not reveals that the mean net annual benefit of physical activity was $330 per per- son in 1987 dollars. An alternative method used a cost-of- illness approach to attribute a proportion of medical and pharmacy costs for specific diseases to physical inactivity in 2001 (97). The authors first identified medical conditions associated with physical inactivity and then collected claims data related to those conditions from approximately 1.6 mil- lion patients 16 years old and older from a large, Midwest health plan. While the resulting conditions from lack of phys- ical inactivity include depression, colon cancer, heart dis- ease, osteoporosis, and stroke, the results from this study conclude that the costs of claims to the health plan attribut- able to physical inactivity translates to $57 per member. One challenge of these analyses is the decision whether to include diseases causally related to obesity. A different approach than the dichotomized strategy esti- mates the impact of different modifiable health risk behaviors and measures their impact on health care expenditures. After gathering information from more than 61,500 employees of 6 employers gathered over a 5-year study period, Goetzel et al. (87) focused on a cohort of slightly more than 46,000 employ- ees. The analysis found that a ârisk-freeâ individual incurred approximately $1,166 in average annual medical expenditures while those with poor health habits had average annual med- ical expenditures of more than $3,800. Thus they estimated the per-capita annual impact of poor exercise habits to be approximately $172. Pronk et al. (89) also identify the rela- tionship between modifiable health risks and short-term health care charges. This research surveyed a random sam- ple of 5,689 adults 40 years old or older enrolled in a Min- nesota health plan. Multivariate analysis on the modifiable health risks (diabetes, heart disease, body mass index, phys- ical activity, and smoking status) concluded that an addi- tional day of physical activity (above zero) would yield a 4.7% reduction in charges (or a $27.99 reduction). The over- arching result of the study is that obesity costs approximately $135 per member, per year and those with low fitness (inac- tivity) cost approximately $176 per member per year. Several matters should be noted when determining values for health benefits. First, annual per capita cost savings vary between $19 and $1,175 with a median value of $128 (see 33 Table 25 in Appendix E). Second, some studies are disag- gregate in nature and estimate costs by inpatient, outpatient, and pharmacy claims; others compare average health care expenditures of physically active versus inactive individuals. Third, some use a dichotomized approach to determine who constitutes a physically active individual while others employ a modifiable health risks approach and do so in a relatively continuous scale. The studies are difficult to compare, how- ever, because some include different conditions, outpatient and pharmacy costs, and actual paid amounts rather than charges. Nonetheless, existing literature provides adequate, though developing, methodologies for estimating the public health impact of bicycle facilities in economic terms. These approaches have recently been made more accessi- ble to planners, decisionmakers, and the public through the Robert Wood Johnsonâs Active Leadership Program. The physical inactivity calculator available on the website (103) provides an easy-to-use tool to estimate the financial cost of physically inactive people to a particular community, city, state, or business. It also supplies companion resources and information needed to re-allocate resources and plan for health- ier workplaces and communities that are more supportive of physical activity. Safety Increased cyclist safety is an often assumed, poorly under- stood, and highly controversial benefit of bicycle facilities. The task of establishing a safety derived, cost-based justifi- cation for bicycle facilities is similar to the process described in the previous section for estimating public health benefits, albeit with different data. Researchers must first demonstrate relationships between a given cycling facility and safety out- comes. Then they need to demonstrate that the measured out- come of conditions with decreased safety imposes a financial burden to the individual or to society. In general, the literature about the safety dimensions of bicycling manifests itself in three primary aspects: (1) helmet use, (2) safety programs, and (3) number of crashes or per- ceived level of safety that can be ascribed to facility design. The last category is most germane to the construction of facilities and is the center of the following discussion. One issue is how to combine data about safety (e.g., crashes or perceived comfort) with different attributes of cycling facil- ities. The teamâs aims to understand the degree to which dif- ferent cycling facilities lead to an incremental safety benefit, measured in terms of decreased crashes or medical costs. Existing literature measures safety in one of three ways: (1) number of fatalities, (2) number of crashes, and (3) per- ceived levels of comfort for the cyclist. Key explanatory vari- ables behind these outcome measures are myriad and complex to identify. For example, the overwhelming majority of bicy- cle crashes resulting in fatalities are caused by collisions with motor vehicles (104). Less severe crashes tend to occur
at intersections or at locations where motor vehicles and bicycles come in contact with each other (105); it is further suggested that crashes are caused by differing expectations between auto drivers and bicyclists (106). However, there is evidence to suggest that some bicycle crashes do not involve any other party (107, 108); this is especially true for children (109). The prevailing argument is that enhanced facilitiesâbike lanes, bikeways, and special intersection modificationsâ improve cyclist safety (83). This claim, however, is con- troversial and a source of debate between Forester (57) and Pucher (58). One of the issues concerns differences between what cyclists state they prefer (i.e., their perception) and what studies with collision data actually reveal. It is widely acknowledged that increased perception of safety is important to encourage cycling as a means of trans- portation and recreation (51, 110). Subsequently, providing separated bicycle facilities along roadways is mentioned as a key to increased perception of safety according to the litera- ture related to bicycle-related stress factors (111); bicycle interaction hazard scores (112), relative danger index (113), compatibility indexes (114). The goal of these works is to determine and predict condi- tions for safe bicycling based on different cyclistsâ percep- tions of safety. The culmination of these works can best be described under the banner of level of service (LOS) models, originally developed in 1987 in Davis, California (115, 116). The participants of these studies were of diverse demographic and skill backgrounds and cycled 30 roadway segments. Including the variables of traffic volume per lane, posted speed limit weighted with the percentage of heavy vehicles, adjoining land use, width of outside through lane, and pave- ment conditions, the researchers were able to explain almost 75% of the variation of perceived safe conditions. The model consists of four basic factorsâpavement conditions, traffic speed, lane width, and traffic volume per lane which aim to serve as a tool for predicting perceived safety and comfort along roadways between automobiles and bicycles. The bulk of the existing literature on bicycle LOS and per- ceived safety focuses primarily on through travel on mid-block roadway segments. Previous research has rarely considered bicycle lanes separately from other shared use conditions (wide curb lanes or paved shoulders) and rarely considered the role of intersections. While stretches of roadways are important, often the most significant and complex design and safety challenges occur at street intersections (117). Two recent research papers focused on this matter (118, 119). Landisâ recent work (118) derived a model to evaluate the perceived hazard of bicyclists riding through intersections. Again, with a highly varied demographic and cyclist ability sample, this study produced a model with a high degree of explanatory power (R2 = 0.83) for bicycle intersection LOS. Significant variables included motor vehicle volume, width of the outside lane, and the crossing distance of the intersec- tion. In this study, there was no control for the presence or 34 absence of a bicycle lane, but the width of the outside lane variable did include the bicycle lane were it present. The research by Krizek and Roland (119) analyzed the severity of instances where existing bicycle lanes terminated and their corresponding physical characteristics. The findings suggest that bicycle lane discontinuations ending on the left side of the street, increased distance at intersection crossings, park- ing after a discontinuation, and width of the curb lane are sta- tistically significant elements that contribute to higher levels of discomfort for the cyclist. The degree to which perception of safety translates into actual increased safety, however, is still debated. It is diffi- cult to translate perceived measures of safety into quantifiable or economic estimates. There is evidence to support the notion that collision-type crashes are lower on off-road paths (120). Using before and after analysis, Garderâs research (121) found raised bicycle crossings to be more appealing and safer for cyclists than at- grade crossings. However, there exists an equal, if not greater body of research suggesting no relationship or a relationship in the opposite direction. Research examining conflicts at approaching intersections on bike lane and wide curb lane segments determined that both facilities improve riding con- ditions for bicyclists, but that the two facilities themselves are not different in safety (122). Smith and Walsh analyzed before and after crash data for two bike lanes in Madison, Wisconsin, finding no statistically significant difference (123). Also, Hunterâs analysis of a bike box in Eugene, Oregon, showed that the rate of conflicts between bicycles and motor vehicles changed little in the before and after periods (124). No conflicts took place while the box was used as intended. Hunter also evaluated colored (blue) pavement and accom- panying signing used in weaving areas at or near intersec- tions in Portland, Oregon (125). The colored rectangular area within the bike land came to be known as âblue bike lanes,â even though only the weaving area was colored. Although conflicts were rare, the rate of conflict per 100 entering bicy- clists decreased from 0.95 in the before period to 0.59 in the after period. In addition, significantly more motorists yielded to bicyclists in the after period. There appears to be good reason for the existing debate over the safety benefits of bicycle facilities. While there is considerable literature suggesting cyclists perceive greater safety with facilitiesâand advocates certainly argue for suchâthe bottom line is that there is little conclusive evi- dence to suggest this. One theory suggests that if a particular setting is deemed unsafe, a cyclist will likely be vigilant and avoid an incident. As a result, the number of incidents may be no greater with an unsafe condition than a safe condition. However, such argument does not support the conclusion that both conditions are equally safe. In the less safe condi- tion, the cyclist will either avoid it or endure a cost of stress to use it. Yet an alternative theory, not directly applicable to spe- cific facilities, is derived from the concept of safety in num-
bers where the likelihood of bicycle-automobile crashes inter- act in a nonlinear manner (the exponent for growth in injuries is roughly 0.4) for an entire metropolitan area. Applying this concept, one would need to calculate the total bicyclists at the metropolitan level (X). Then one could compute 1.X exp 0.4. This number provides the additional bicycle safety cost of adding bicyclists. The cost of car crashes could even be reduced by the proportional reduction in cars on the road. Then, each additional bicyclist increases the total number and thus the total cost of bicycle crashes (though not the per unit cost). In low-volume portions of the nonlinear relation, the decrease in fatality rate outpaces the increase in volume so that, even with more cyclists, the number (not just the rate) of fatalities decreases. Values for the benefits and costs of such crashes could be obtained from third party sources (e.g., http://www.oim.dot.state.mn.us/EASS/) which typically summarize the cost per injury of car crashes per type. Reduced Auto Use The most common assumption is that cycling trips substi- tute for auto trips, yielding transportation benefits to society- at-large such as decreased congestion, improved air quality, and decreased use of non-renewable energy sources. While the substitution element may hold true for some cyclists it is extremely difficult to reliably determine the trips that would otherwise be made by car. The nature and magnitude of any substitution is important to determine and could be estimated via a variety of means. In some instances, a bike trip may replace a car commute; in many cases, however, bicycle trips are likely made in addi- tion to trips that would otherwise occur (126) or for a differ- ent reason (e.g., recreation). Assuming a fixed demand of overall travel, a best-case scenario for bicycle substitution stems from an assumption well known in the field of travel behavior modeling referred to as Independence from Irrelevant Alternatives (IIA). That is, bicycles draw from other modes in proportion to their current mode shares. For instance, bicycles would draw 85% from current drive alone trips, 5% from auto passenger trips, 5% from transit trips, and 5% from walk trips. This of course is unlikely to be strictly true, so an important part of the benefit analysis would be to determine which of these groups are more likely to switch to bicycling and fur- thermore, which socio-economic characteristics could be tar- geted to result in higher rates of cycling. Assuming bicycling facilities can help bicyclists travel faster, more safely, in a better environment or for shorter dis- tances, its utility compared with other modes will increase. There may be an estimable effect in terms of substitution, and there are different approaches for measuring this phenome- non. At a crude level, one could estimate the number of bicy- cle miles of travel and auto miles of travel. Assuming a fixed rate of substitution (i.e., 60% of all cycling trips are utilitar- ian in nature and are substituting for a car trip), one could 35 estimate an upper bound of all mileage that is substituted and the overall social costs being saved. However, this does not account for the possibility that bicycle trips may be substi- tuting for modes other than driving. Furthermore, it says lit- tle about how many additional trips from potential cyclists could be induced. Such information would be most reliably obtained by estimating a mode-choice model for different types of cycling trips and calculating the likelihood of sub- stitution rates in that manner. The latter strategy is subject to elaborate modeling schemes and survey data. It is important to recognize, however, that any reduced congestion benefit to society needs to be tempered by an induced demand phenomenon which may obviate congestion or pollution reductions due to diversion (127). This implies that reduced traffic congestion that may result from the con- struction of an additional bike lane may largely (though not entirely) be consumed by other drivers making additional trips, drivers lengthening trips, and additional development. This suggests that any reduction in congestion (and subse- quently pollution and energy benefits) will be small at best. Nevertheless, the additional opportunities for drivers to pur- sue activities that previously had been too expensive prior to the capacity expansion (of roads or bike lanes) engender some benefits for new drivers. Livability Another benefit refers to social attributes accrued by indi- viduals who receive benefits of such facilities, either directly or indirectly. One of the reasons people pay a premium to live in desirable areas is that they are paying for the option to use specific facilities, whether or not they actually do. For instance people may pay a premium to live near a bike path despite not cycling themselves because they might want to in the future. In this respect, such proximity would be valued by current and potential users. These benefits are revealed through preferences that represent an elusive phenomenon to which an economic value can be attached. A compelling strat- egy to measure these non-market goods analyzes the choices people reveal in their purchase of home locations in efforts to understand how they implicitly or explicitly evaluate the desirability of a certain good. A revealed preference approach would measure individualsâ actual behavior, and this can be done through hedonic modeling to learn if and how much residents value accessibility to bicycle facilities. Discerning the relative value of non-market goods using hedonic modeling techniques is a method that has been employed for years since its first applications by Lancaster (128) and Rosen (129). An extensive review of this literature (130) contains nearly 200 applications that have examined home purchases to estimate values of several home attributes including structural features (e.g., lot size, a homeâs finished square feet, and number of bedrooms), internal and external features (e.g., fireplaces, air conditioning, garage spaces, and
porches), the natural environment features (e.g., scenic views), attributes of the neighborhood and location (e.g., crime level, golf courses, and trees), public services (e.g., school and infrastructure quality), marketing, and financing. The appli- cation germane to this inquiry focuses on the relative impact of bicycle lanes and trails. It is important, however, to under- stand the relative value of different types of facilities as they may have substantially different appeal. Some trails are on existing streets (demarcated by paint striping); some are next to existing streets (separated by curbs); others are clearly sep- arated from traffic and often contained within open spaces. The last category, being the most attractive for many bicy- clists, is likely to have the largest effect. To effectively esti- mate the value of such facilities, it is important to be able to explain and control for the degree to which open space ver- sus the bike trail contained within the open space contributes to a homeâs value. In many metropolitan areas, bike trails and open space share a spatial location and at minimum exhibit similar recreational qualities. Any research failing to account and control for such correlation would be misguided in its attempt to estimate the independent value of bicycle trails. For this reason, not only is it important to control for struc- tural attributes of the home, characteristics of the neighbor- hood, and geographic location, but it is also important to consider the value of adjacent open space. The value of open space has been estimated in several applications of hedonic regression (131â136). The hedonic pricing method is appealing because it is rooted firmly in market prices and provides a strategy to per- form an economic valuation for non-market facilities. To the teamâs knowledge, the only attempt to extend such methodol- ogy to bicycle facilities was conducted by Lindsey et al. (72) who analyze the property value using a one-half mi buffer around a greenway. The outcome of this methodology would then be econometric models that can be used to reliably mea- sure if residents value access to bicycle facilities and if so, to what degree. This value could then be easily converted to monetary amounts. Fiscal ROW preservation is the process of preserving land needed for future infrastructure, most often in the form of transporta- tion. It is a benefit reaped exclusively by the public agencies planning such facilities. Consider the situation where there may be a plan to build a rail transit corridor in 10 years; it may be economically prudent to acquire the land sooner rather than later for several reasons (137). First, the price of land may rise faster than inflation. Second, acquiring the land now may ensure it is not developed, while not acquiring it now may require the destruction of recently constructed buildings. There are, of course, risks associated with ROW preserva- tion. Land may be acquired but the resources never found to 36 complete the project. ROW acquired prior to use for a future road or transit line may still be used for transportation. Plac- ing a bicycle facility along the ROW is relatively inexpen- sive, ensures a transportation use for the corridor (ensuring it will not be viewed as park land) and provides user benefits. The economic value of ROW preservation can be esti- mated by multiplying the probability of use in the future by the difference of the net present value of future cost if not preserved and the present cost. Because acquiring ROW that is already developed is more expensive, this should output a positive value. The probability of future use is an important variable that is usually case specific. For example, a plan may suggest three alternative ROWs for a route. The probability of any route would then be less than one-third. Thus, the ROW preservation benefit would depend on the difference in costs multiplied by that probability. There are similar ways of estimating this value that might produce different results. For example, the present cost of the ROW could be esti- mated in the cost category, and then consider âsellingâ the ROW in the future to the other transportation project as part of the salvage value of the bicycle facility. This salvage value is an estimate of the market value of the land. If the net pres- ent value of the salvage value exceeds the present cost, there may also be a right of preservation benefit. In such delibera- tions, it would be important to account for the discount value of completing the projectâthe present value of using avail- able funds to complete a project and buying land for future proj- ects later. For example, a benefit-cost ratio of 1.1 that would imply that 1 million dollars spent on a project will generate stream of benefits worth 1.1 million in present dollars. This is the baseline to compare with early ROW purchase. That is, the baseline is that some amount of money âxâ greater than 1 mil- lion dollars will be spent to buy ROW in the future. To estimate the present value of using the 1 million dollars to buy ROW for future use, delaying a hypothetical project that would have been done with that money, consider how that the benefit stream would change. First, a given project may eventually generate the same stream of benefits, but delayed by n years, giving a lower present value. However, the money that is saved (x minus $1M) by not paying a higher land price later, means that an additional project can be done at that time, yielding extra benefits, again starting n years in the future. CONCLUSIONS For such information to be useful in policy circles, several actions need to be taken (in addition to improving data col- lection efforts). First, the majority of past work has a clear advocacy bent; it is not always known how and where much of the data are derived. It is unclear from most of the studies if the available data were analyzed in a completely objective manner. Second, it is important to focus the discussion and analysis at an appropriate scale and for a particular purpose.
Third, such analysis and frameworks need to be better incor- porated into policy discussions. In its current state, this research lacks appeal because many studies are conducted at a rela- tively abstract scale rather than at a project scale. For this reason, it is suggested that benefits be estimated on a municipal (or regional) scale or in even more disaggregate units. Finally, there exists considerable room for improving 37 the manner in which these methodologies are approached. The intent is to provide the foundation for urging a consistent framework in which different benefits can be estimated and subsequently compared. If the goal is to implement plans that systematically integrate or account for such consideration, then such methods and improvements will ultimately lead to more sound policy decisions and bicycle facility investment.
