National Academies Press: OpenBook
« Previous: Chapter 5 - Applying the Guidelines
Page 51
Suggested Citation:"Endnotes." National Academies of Sciences, Engineering, and Medicine. 2006. Guidelines for Analysis of Investments in Bicycle Facilities. Washington, DC: The National Academies Press. doi: 10.17226/13929.
×
Page 51
Page 52
Suggested Citation:"Endnotes." National Academies of Sciences, Engineering, and Medicine. 2006. Guidelines for Analysis of Investments in Bicycle Facilities. Washington, DC: The National Academies Press. doi: 10.17226/13929.
×
Page 52

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

51 ENDNOTES i In theory, this task could assume proportions considerably more complex and sophisticated not pursued in this application for sev- eral reasons. Some literature attempts to relate the amount of bicy- cling to facility measurement, but with very limited accuracy. Other literature discusses possible ways of describing the bicycle envi- ronment but does not relate this empirically to the amount of bicy- cling in an empirical measurement. The team believed that a frame- work that required planners to evaluate the detailed bicycling environment of their area would be exceedingly complex and labor intensive for the planners, and difficult for us to explain in an unam- biguous way that would be easy to apply. Even conceptually sim- ple measures such as miles of off-road bike paths proved hard to define in a way that would allow comparability across different loca- tions. Local subtleties of design and location can be vitally impor- tant in issues that might arise in different places, let alone place val- ues on them that could be used to develop an overall environment rating. ii The amount of data needed for bicycle forecasting using these methods is actually much larger than for an auto forecasting model for two reasons. First, because so few people ride bikes in any short period, relative to the number that drive cars. To have a large enough sample of bicyclists to have some sense of their geographic distribution would require a very large survey; of the roughly 10,000 adults in the Twin Cities Travel Behavior Inventory, only about 200 rode a bike on their survey day, not nearly enough to esti- mate a regional model. Second, because the number of factors that might influence bicycling decisions is much larger than for car or transit travel, which are typically just assumed to rely on travel time and monetary cost. By contrast, highly subjective factors such as perceived safety and pleasantness of the riding environment could play major roles in bicycling decisions. iii In addition, it may in fact be more useful than a more complex model that may be slightly more accurate, but that given the issues discussed may also have only the appearance of accuracy. iv Based on an analysis of several sources (e.g. travel dairies such as the National Household Travel Survey, direct questionnaires administered by the Bureau of Transportation Statistics), the team project’s that approximately 3% of the U.S. population cycles one day per week and an estimated one percent of the U.S. population receives their recommended weekly level of physical activity by cycling. vA quick look at the data shows that 69% of our adult subjects within Minneapolis and St. Paul have a retail location within 400 meters of their home. viHouseholds were recruited to participate in the TBI using a strat- ified sampling design. Telephone interviews were used to collect both household and individual socioeconomic and demographic data. Subsequent to the demographic interview, households were assigned a travel day on which 24-hour travel diaries were com- pleted for all household members five years or older. viiHome phone call interview information helped ensure the relia- bility of these self reported measures of walking and/or cycling. viiiThe sample was restricted to residents of Minneapolis and St. Paul primarily because theses two cities—as opposed to the suburbs— had adequate representations of walking and cycling behavior. Of the adults who completed a bicycle trip during their diary day (a total of 138 throughout the seven county area), 86 of them (62%) were from the Minneapolis or St. Paul. We also only included the population who reported having com- pleted any type of travel during their assigned travel diary day, a pro- cedure consistent with other transportation-related research (177. Zahavi, Y. and J. M. Ryan, Stability of Travel Components Over Time. In Transportation Research Record 750, TRB, National Research Council, Washington, D.C., 1980, p. 19–26). Of the orig- inal 1,801 individuals, 148 individuals (8.2%) took no trips on the travel diary day and were thus excluded. This left us with an effec- tive sample size of 1,653 subjects (91.8% of our original sample). The 148 subjects that were excluded were not significantly differ- ent from subjects retained for analysis with respect to the likelihood of living in a household with kids or living in Minneapolis. How- ever, compared with excluded subjects, included subjects were more likely to be employed (83% vs. 41%, p < 0.001); more likely to have a college education (64% vs. 20%, p < 0.001) and more likely to be male (48% vs. 35%, p = 0.002). Included subjects were also less likely to live in households with an annual income less than $50,000 (36% vs. 56%, p < 0.001) and less likely to be over 60 years of age (15% vs. 37%, p < 0.001). ixSuch data was obtained from 2001 employment records of the Min- nesota Department of Employment and Economic Development. xWhen measuring this dimension, it is important to measure the diversity of different types of retail establishments while controlling for the potential disproportionate drawing power of larger establish- ments (e.g., a large clothing store offers high employment but little diversity). The upper limit is set at businesses containing more than 200 employees and the number of employees for each area is tallied. The final measure is the number of employees within the “neighbor- hood retail” subset within 1,600 meters of each home location. xiThese include all businesses in the following NAICS categories: Food and Beverage Stores (e.g., grocery, supermarket, conve- nience, meat, fish, specialty, alcohol) Health and Personal Care (e.g., pharmacy, drug store) Clothing and Clothing Accessory Stores (e.g., shoe, jewelry, luggage) Sporting Goods, Hobby (e.g., needlepoint, musical instrument), Book, and Music Stores General Merchandise Stores (e.g., includes department stores) Miscellaneous Store Retailers (e.g., florists, novelty, used mer- chandise, pet, art, tobacco) Food Services and Drinking Places (e.g., restaurants) xiiThe principal reason from these breakpoints was to ensure adequate distribution across each category. For example, 32 percent of the households had a retail establishment within 200 m, 37 percent within 400 m, 21 percent within 600, and 10 percent for the remaining. xiiiA continuous measure assumes that for each additional meter of distance closer/farther there is a consistent incremental increase/ decrease in the odds of bike or walk use. xiv One potential disadvantage is that by subclassifying into cate- gories, a strong homogeneity assumption is imposed. That is, the team assumes that the effects are the same for everyone within a given category regardless of their individual proximity to a bike

trail. For example, the effect of living 400 meters from a bike trail is no different than living 799 meters from a bike trail. However, given that the increments are within roughly 400 meter, there is rel- atively little difference, if any. xvOutside the downtown core of each city (for which there are very few respondents in the TBI), there is similar housing density spread across mostly all neighborhoods. xviThese 86 cyclists completed between 1 and 10 bike trips on the assigned travel day (mean = 2.9, SD = 1.79). For 73 of these cyclists (85%) the total distance traveled by bicycle is also calculated, which ranged from 0.74 km to 36.71 km (mean = 8.64, SD = 7.10). As expected, the proportion of bikers varied across levels of bike facil- ity proximity, 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. In other words, the distribution of cyclists across cate- gories of proximity to any bike facility was not statistically signifi- cant, nor was the distribution of bikers across categories of prox- imity to an off-street facility. However, the distribution of cyclists across categories of proximity to an on-street facility was statisti- cally significantly different, with increasing proportions of cyclists in the hypothesized direction (chi-square = 13.42; p = 0.004). xvii Our definition of “walkers” did not include people who only reported a walk trip from a different location (e.g., work or other). Individuals who only reported such walk trips are not included in an effort to more cleanly identify correlations between the residen- tial environment and walking. xviiiThis in turn can lead to an increase in the Type I error rate; that is, finding a statistically significant effect, when in fact there is none. xix Some respondents may be pursuing walk trips from work or other types of locations. Only walk trips from home were considered. An additional 137 people report having a completed a walk trip, how- ever, none of the walk trips they reported were from home. 52 xx As mentioned, this analysis only included residents from Minneapolis and St. Paul. Most other communities within the metropolitan area are more suburban in nature in terms of lower den- sity, lower accessibility, and other related urban form features. xxi To be technically correct, sampling weights should have been employed. Given the secondary nature of the analysis and the fact that a sub-sample was selected, proper survey sampling weights were not available. xxii Our sample began with 42,750 records. Geocoding and remov- ing records with missing or unreasonable data (e.g., homes with zero bathrooms, zero square feet, or built before 1800) reduced our sample to 35,002. The relatively small number of records removed still provided an even distribution of home sales across the metro area. xxiiiActive open spaces are primarily used for recreation, and consist of neighborhood parks and some regional parks. Passive open spaces are less accessible on foot. They include areas such as golf courses, cemeteries, and large regional parks that are accessible only through designated entrance points and often only by car. xxivOpen space and bicycle variable names are prefixed by a c for city and s for suburb. xxv In Minneapolis, several of the streets in the downtown core have bicycle lanes (although there are few home sales downtown). Most other on-street bicycle lanes are on busy commuting arterials or around the University of Minnesota commercial district. On-street lanes in St. Paul are a different story. They tend to be along a well maintained boulevard-type corridor (Summit Avenue) and the Mis- sissippi River corridor. These counteracting effects between Min- neapolis and St. Paul may possibly cancel out one other. xxvi The median sale prices in the city and suburbs for 2001 were $148,475 and $184,500, respectively. No significant relationship was found between home prices in the city and proximity to on- street bicycle lanes, so no effect is estimated in Table 27.

Next: Bibliography and Sources »
Guidelines for Analysis of Investments in Bicycle Facilities Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

TRB’s National Cooperative Highway Research Program (NCHRP) Report 552: Guidelines for Analysis of Investments in Bicycle Facilities includes methodologies and tools to estimate the cost of various bicycle facilities and for evaluating their potential value and benefits. The report is designed to help transportation planners integrate bicycle facilities into their overall transportation plans and on a project-by-project basis. The research described in the report has been used to develop a set of web-based guidelines, available on the Internet at http://www.bicyclinginfo.org/bikecost/, that provide a step-by-step worksheet for estimating costs, demands, and benefits associated with specific facilities under consideration.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!