Click for next page ( 67


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 66
B-1 APPENDIX B BICYCLING DEMAND AND PROXIMITY TO FACILITIES The Effect of Bicycle Facilities and Retail on Cycling and Walking in an Urban Environment INTRODUCTION munity on the environmental determinants of bicycling and walk- ing dually considers both modes (148, 149). For abstract or general Urban planners and public health officials have been stead- purposes this may suffice; the two modes are almost always aggre- fast in encouraging active modes of transportation over the past gated in transportation research. In terms of daily use, facility plan- decades. While the motives for doing so differ somewhat between ning, and community design, however, bicycling and walking dif- professions--urban planning to mitigate congestion, public health fer substantially. The following paragraphs point the reader to some to increase physical activity--both have ardently aimed to increase of the salient literature related to walking and cycling and assesses levels of walking and cycling among the U.S. population. Deci- some of the theoretical differences between the two modes. sions to walk or bike tend to be the outcome of myriad factors. Pedestrian travel and infrastructure have the following distin- Conventional thinking suggests two dimensions are important: for guishing characteristics. First, all trips--whether by car, rail transit, cycling, this includes the proximity of cycling-specific infrastruc- or bus--require pedestrian travel because they start and end with a ture (i.e., bicycle lanes or off-street paths); for walking, this includes walk trip. Second, sidewalks and other pedestrian related infrastruc- the proximity of neighborhood retail (i.e., places to walk to). ture (e.g., crosswalks, public spaces) are now often incorporated into This study focuses on two modes of active transportation--walking zoning codes. Third, pedestrian concerns typically apply to con- and cycling--and two different elements of the physical environ- fined travel-sheds or geographic scales (e.g., city blocks). Finally-- ment that are often discussed in policy circles, respectively, neigh- and most germane to the analysis presented herein--pedestrian travel borhood retail and bicycle facilities. Our work aims to answer the usually tends to be influenced by a broad array factors that go beyond following questions: (a) does having a bicycle lane/path close to simply sidewalks and other infrastructure. This means that both home increase the propensity to complete a cycling trip and (b) does the attractiveness of features along the route (e.g., interesting having neighborhood retail within walking distance increase the facades, a variety of architecture, the absence of long, blank walls) propensity to complete a walk trip from home? The primary advan- and destinations (e.g., close-by stores) are important. tage of this work is that it carefully analyzes these relationships for Early research on pedestrian travel underscored the importance an urban population employing detailed GIS/urban form data and a of neighborhood retail in creating inviting pedestrian environments robust revealed preference survey. The study uses multivariate (150152). Several studies offer detailed strategies using these mea- modeling techniques to estimate the effect of features of the built sures (153157). Much of the empirical work matches measures of environment on outcomes related to bicycling and walking. pedestrian behavior with assorted place-based destinations in their We first briefly review directly relevant literature to this pursuit work (158, 159) or even select measures of retail (160166). How- and describe some issues that limit the utility of previous research. ever, much of the available work on pedestrian behavior vis--vis We explain the setting for this application, the travel data, and the retail tends to lack detailed spatial attributes or be specific to urban detailed urban form data. We then report the results of our analysis design features such as benches, sidewalk width or other streetscape in two different tracks: one to estimate the odds of cycling; another improvements (167); few studies examine such behavior over an to estimate the odds of walking. The final section discusses these entire city with detailed measures of retail activity. results and offers policy implications. Bicycle travel and facilities, on the other hand, apply to longer corridors, and fail to be used as frequently as walking facilities. Such trips are usually considered more discretionary in nature. EXISTING LITERATURE AND THEORY Where pedestrian planning applies to a clear majority of the popu- lation (nearly everyone can walk), bicycle planning applies to a con- Attempts to document correlations between active transportation siderably smaller market of travelers--those who choose to own and community design have been a focus of much of the recent and ride a bicycle. During the summer months in most of the U.S., urban planning and public health literature. Available literature this includes just over a quarter of the American population (47). underscores the importance of this research (143, 144), establishes Bicycle travel has a longer travel shed and most of the population a common language for both disciplines (79, 145), helps refine has a heightened sensitivity to potentially unsafe conditions (e.g., approaches for future studies (146), and comprehensively reviews shared facilities with autos speeding by). The quality of the facility available work (147). Existing research, however, varies in geo- is often paramount. Such facilities along a route include wide curb graphical scope, the manner in which it captures different dimensions lanes, and on-street or off-street bike paths. of active transportation, and the strategies used to measure key fea- Similar concerns pervade available literature on cycling and tures of the built environment. Some of the difficulty in tackling the the provision of cycling-specific infrastructure. There is consider- literature on community design and physical activity--namely walk- able enthusiasm about the merits of bicycle trails and paths to induce ing and cycling--is that the bulk of the literature aggregates these two use (55, 168170). Little work, however, has rigorously tested such modes. Some known work coming from the non-motorized com- claims. Existing studies have examined the use of particular trails

OCR for page 66
B-2 (171173), cycling commute rates vis--vis bicycle lanes (174, 175) city also has a wide distribution of retail activity across the city (see or their impact on route choice decisions (176). Again, there exists the top half of Figure 10) and many homes with close proximity to a dearth of empirical knowledge about the merits of such cycling neighborhood retail.v infrastructure using disaggregate data for individuals who may live Our knowledge of who walked and cycled is derived from a home across entire cities. interview survey known as the 2000 Twin Cities Metropolitan Area Travel Behavior Inventory (TBI). This survey captures household travel behavior and socio-demographic characteristics of individuals SETTING AND DATA and households across the 7-county metropolitan area, encompassing primarily the urbanized and suburbanized parts of Twin Cities of Our research is based on the Twin Cities of Minneapolis and Minneapolis and St. Paul metropolitan area. The TBI data were orig- St. Paul, Minnesota, which border one another and are roughly the inally collected via travel diaries in concert with household telephone same geographic size (approximately 57 square miles each). The interviews.vi Participants were asked to record all travel behavior for separate central businesses districts for each city are less than ten a 24-hour period in which they documented each trip that was taken, miles from one another. According to the 2000 Census, Minneapo- including the origin and destination of the traveler, the mode of travel, lis has roughly 100,000 more residents than St. Paul (382,618 ver- the duration of the trip, and the primary activity at the destination, if sus 287,151). The setting of these cities proves to be almost ideal one was involved.vii Household characteristics and household loca- for several reasons. Both Minneapolis and St. Paul are well-endowed tion were attributed to each individual. We additionally linked house- with both on-street and off-street bicycle paths. Figure 9 shows a holds with neighborhood spatial attributes relative to their reported combined 60 miles of on-street bicycle lanes and 123 miles of off- home location. We selected all subjects from the TBI diary database street bicycle paths. Furthermore, the population comprise residents who were residents of Minneapolis or St. Paul and 20 years of age or who appear to cherish such trails, particularly in the summer months. older (n = 1,653).viii A key feature of this investigation is that it applies Minneapolis ranks among the top in the country in percentage of to two entire central cities, rather than precise study areas or specific workers commuting by bicycle (175). For the walking query, each corridors of interest. Figure 9. Map of study area showing bicycle facilities and home location of cyclists.

OCR for page 66
B-3 Figure 10. Maps of study area showing location of retail establishments (top) and home location of walkers (below).

OCR for page 66
B-4 POLICY VARIABLES OF INTEREST terms of comparing individuals who live within 400 meters of a bike trail and those who live more that 1,600 meters from a bike trail.xiv Our policy variables of interest differ for each mode and are based on distance which is often mentioned as a suitable measure of impedance (178). For cycling, our analysis examines the prox- COVARIATES imity of bicycle facilities in the form of on-street bicycle lanes and off-street bicycle paths (Figure 11). Three continuous distance mea- We identify several covariates to represent individual, household, sures were calculated using GIS layers furnished by the Minnesota and other characteristics. These covariates represent factors that Department of Transportation, with separate map layers for on- may differ across exposure levels and thus could potentially con- street and off-street trails. Marrying this data with precise household found our effect estimates. To help free our estimates from con- locations, we calculated the straight-line distance in meters to the founding explanations we use these covariates to statistically equate nearest on-street bicycle lane, the nearest off-street trail, and the subjects on observed characteristics across exposure groups; there- nearest bike facility of either type. Four distinct categories represent fore, the only measured difference between them is the proximity to the distance from one's home to the nearest bicycle trail as < 400 either the retail or the bicycle facilities. meters (one-quarter mile), 400799 meters, 8001,599 meters, and For individual characteristics, we use age, gender, educational 1,600 meters or greater (greater than one mile). attainment (college degree or not), and employment status (employed For walking, we measure neighborhood retail in a detailed and rig- or not). For household characteristics, we use household income orous manner. We first obtained precise latitude and longitude infor- (five categories), household size, and whether the household had mation for each business in Minneapolis and St. Paul.ix Relying on any children younger than 18 years old. We also use two other the North American Industrial Classification System (NAICS),x we measures: household bikes per capita and household vehicles per included businesses such as general merchandise stores, grocery capita. We calculate these by dividing the total number of bicycles stores, food and drinking establishments, miscellaneous retail and by household size and dividing the total number of vehicles by the sort.xi These types of businesses were retained because they household size. would likely attract walking trips for neighborhood shopping and be Spatial measures and other attributes of the built environment in representative of good walking environments that would likely gen- this study are limited to proximity to retail or bicycle facilities. erate non-shopping walking trips. We again combined this informa- Focusing on a sample contained within the boundaries of Min- tion with household location data. Finally, we calculated the network neapolis and St. Paul helps to control for other variation among spa- distance between the home location and the closest retail satisfying tial measures by nature of the research design. For example, our the above criteria. For analysis, we used the distance variables to sample has little variation in density,xv regional accessibility, and classify subjects into one of four categories. The four categories rep- access to open space. Other than a few small bluffs--where there resent the distance from home to the nearest retail establishment as are few known residences--there is little variation in topography. < 200 meters (one-eighth mile), 200399 meters, 400599 meters, And, almost every neighborhood street in Minneapolis and St. Paul and 600 meters or greater.xii To provide the reader with visual repre- has sidewalks on both sides, thereby controlling for an often cited sentations of the retail "catchment" areas for varying distances, we important urban design feature. provide Figure 12 showing a home location (in the center) and retail establishments within varying walking distances from the home. When measuring each of the policy relevant variables, a four- RESULTS AND INTERPRETATION level ordinal variable is advantageous over the continuous distance measure in two respects. First, the categorical measure allows us to Overall, our sample was nearly evenly split on gender (52% female relax the strong linearity assumption that underlies continuous mea- vs. 48% male) and two-thirds (67%) were residents of Minneapolis sures.xiii Second, the four-level categorical measure allows flexibil- (as opposed to St. Paul). Most subjects were employed (83%) and had ity relative to ease of presentation and intuitive interpretation. Given at least a 4-year college degree (63%). The majority lived in house- that we used distance cut-points with relatively simple interpreta- holds with no children (80%) and reported household incomes less tion, it provides a compelling way to grasp the reported findings in than $50,000 per year (36%). Figure 11. Representative photographs of off-street trail and on-street bicycle lane (respectively).

OCR for page 66
B-5 Lake Calhoun Lake Calhoun Lake Harriet Lake Harriet 200 meters 400 meters Lake Calhoun Lake Calhoun Lake Harriet Lake Harriet 600 meters 800 meters Lake Calhoun Lake Calhoun Lake Harriet Lake Harriet 1200 meters 1600 meters Figure 12. Retail "catchment" areas for an example home (shown in the center) and varying network walking distances. Dots represent retail locations; polygon shaded areas represent a catchment area. We first used descriptive techniques (i.e., chi-square and t-tests) viduals reported doing so (5.2 percent).xvi While this rate is higher to characterize our sample by proximity to each type of facility. We than both the larger TBI sample and national average--which tend explored the distributions of individual and household characteris- to hover around two percent of the population (179)--one needs to tics for subjects at each level of exposure. Subjects living within dif- recognize this is a relatively small number. However, a close look at ferent proximity levels to bike facilities or retail differ somewhat this population showed that they did not have exceptional or pecu- with respect to many of the individual and household characteris- liar characteristics (e.g., the majority of them showing up near the tics. For example, subjects living in close proximity to any bicycle university). The second outcome of interest was if the respondent facility are more likely to be 40 or older, have a college degree, and completed a walking trip from home, 12.4 percent of our sample live in households with no children than subjects living farther away (n = 205).xvii from a bike facility. Different covariate patterns emerge depending Because our outcome measures are dichotomous, we use multiple upon which proximity measure we examine. logistic regression models to examine the effect of facilities and retail The outcomes of interest in this application are twofold; both were on bicycling or walking. For each proximity measure (e.g., distance operationalized in a dichotomous manner. The first is whether the to any trail, distance to on-street trail, distance to off-street trail, dis- respondent completed a bicycle trip as documented in the 24-hour tance to retail), we conduct a series of analyses; they build from a sim- travel behavior diary. A total of 86 individuals from our 1,653 indi- ple logistic regression of the exposure on the outcome to a multiple

OCR for page 66
B-6 TABLE 12 Models comparing the effect of distance to an on-street bicycle facility on odds of bike use (Models 1a-1c) and the effect of distance to neighborhood retail on odds of making a walk trip from home (Models 2a2c) Bicycle Use Walk use Model 1a Model 1b Model1c Model 2a Model 2b Model 2c Distance to nearest on-street bicycle path Distance to nearest retail establishment < 400 meters 2.933 3.101 2.288 < 200 meters 3.098 3.060 2.348 (3.11)** (3.21)** (2.23)* (3.41)** (3.36)** (2.51)* 400 799 m 2.108 2.012 1.511 200 399 m 1.653 1.616 1.316 (2.05)* (1.89) (1.07) (1.48) (1.41) (0.80) 800 1599 m 1.390 1.361 1.163 400 599 m 1.448 1.422 1.288 (0.88) (0.81) (0.39) (1.02) (0.97) (0.69) >= 1600 m referent referent referent >= 600 m referent referent referent Individual Characteristics Male subject 2.015 2.160 0.760 0.787 (2.96)** (3.12)** (1.80) (1.57) College 1.753 2.840 1.113 1.271 (2.15)* (3.47)** (0.68) (1.42) Employed 0.783 1.187 0.771 0.901 (0.71) (0.43) (1.24) (0.49) 40-59 years 0.520 0.623 1.004 1.112 (2.73)** (1.83) (0.03) (0.64) >=60 years 0.081 0.115 0.769 0.752 (3.49)** (2.98)** (1.03) (1.10) Household Characteristics $15,000 - $49,000 0.402 0.874 (2.30)* (0.41) $50,000 - $74,999 0.293 0.704 (2.83)** (1.00) >= $75,000 0.206 0.880 (3.33)** (0.35) Income missing 0.172 0.886 (3.00)** (0.32) Household w/ kids 0.640 0.790 (2.21)* (2.08)* HH bikes per capita 2.463 0.892 (7.85)** (0.80) HH vehicles per 0.114 0.300 capita (5.29)** (4.56)** Wald chi-square = 137.65 Wald chi-square = 55.61 Log pseudolikelihood = -262.34 Log pseudolikelihood = -583.69 Pseudo R-square = 0.224 Pseudo R-square = 0.058 # of observations in all models = 1653 Odds ratios, robust z statistics in parentheses. * significant at 5 %; ** significant at 1 % logistic regression fully adjusted for all subsets of covariates (Mod- model to the fully adjusted model, the odds of bike use did not dif- els 1 and 2 in Table 12). fer significantly by proximity to any bike facility (this includes Because our data are hierarchically structured--individuals are either on-street bicycle facility or off-street bicycle trails). Our nested within households--we use robust standard errors to account model suggests that there is no effect of proximity to any bike facil- for the effects of this clustering. Subjects who reside in the same ity on bike use. We therefore used a separate model to estimate the household are more alike within a household than they are with sub- effect of proximity to off-street facilities on the odds of bike use. jects residing in other households. Accordingly, less independent Examining the simple logistic regression model to the fully adjusted information is contributed by individuals from the same household, model for off-street bicycle facilities, the odds of bike use did not which may artificially decrease the standard error of the estimate.xviii differ significantly by proximity to a trail. We detected no effect of proximity to off-street bike facilities on bicycle use. Finally, we examined the effect of proximity to on-street bicycle Bicycling facility on the odds of bike use. In the simple logistic regression model (Model 1a in Table 12), subjects living within 400 meters of an on- Our first models explore the odds of bicycle use and proximity to street bicycle facility had significantly increased odds of bike use com- any type of bicycle facility. From the simple logistic regression pared with subjects living more than 1,600 meters from an on-street

OCR for page 66
B-7 bike facility. As expected, those that lived within 400 to 799 meters of bike facility. Walking use increases if retail is within 200 meters. an on-street bike facility also had significantly increased odds of bike While not the focus of this analysis, our study reaffirmed that use compared with subjects living more than 1,600 meters from an on- many of the socio-demographic and economic variables used in street bike facility, although the odds of bike use were slightly lower other studies are important. than for those living closest to an on-street facility. Some officials have supported the use of community design to After adjusting for individual and household characteristics, the induce or enable physical activity, but this analysis suggests that the effects were somewhat attenuated (see Models 1b and 1c). Subjects argument is more complex. First, our results underscore the fact that living in close proximity to an on-street facility (< 400 meters) still we are addressing fringe modes and rare behavior (180). Even had statistically significantly increased odds of bike use compared among the urban population, only five percent cycled and twelve with subjects living more than 1,600 meters from an on-street bike percent walked. And, the criteria for satisfying this measure were facility. However, subjects within 400 to 799 meters still tended generous--any cycling or walking trip from home that was reported toward increased odds of bike use, however this failed to reach the by the individual over a 24-hour period.xix Second, the research sup- level of statistical significance. Consistent with prevailing theories ports the theory that the built environment matters; however, it sug- of bicycle use, our models show that cyclists tend to be male, from gests that one needs to live very close to such facilities to have an older populations, and from households with children. statistically significant effect (i.e., less than 400 meters to a bicycle trail for bicycling and less than 200 meters to retail for walking-- approximately the length of two football fields). While the odds- Walking ratios for longer distances failed to reach levels of statistical signif- icance, it is important to mention that in all model estimations, they We employed a similar approach to examine walking behavior were always in decreasing orders of magnitude and always in the vis--vis retail and discovered similar results. In the simple logistic assumed direction. Planners need to be aware of such distance regression model (Model 2a in Table 12), subjects living within considerations when designing mixed land use ordinances (181). 200 meters of a retail establishment had significantly increased odds The results, however, need to be viewed in the following light. The of making a walk trip compared with subjects living more than first consideration is that the analysis is reported for only an urban and 600 meters away from retail. Households living between 200 adult population. Conventional wisdom suggests children (84), rural 400 meters and 400600 meters of retail, however, failed to reach or suburban residents (182) may value different features of the built a level of statistical significance. environment.xx The second is that the original TBI survey was the Again, after adjusting for individual and household characteris- result of a complex sampling design which needs to be taken into tics, the effects were somewhat attenuated (see Models 2b and 2c). account.xxi Subjects living in close proximity to retail (< 200 meters) still had Being based on cross-sectional analysis, these results cannot be statistically significantly increased odds of walking. Interestingly used to infer causal relationships (183). We can conclude that enough, however, household with children was the only household respondents living very close to bicycle paths or retail bike or walk characteristic variable that was significant. more than their counterparts further away. However, consistent In each model, the results suggest that distance to bicycle facilities with emerging theories about travel behavior, the decision to live in and retail is statistically significant; however, the relationship is not close proximity to such features is likely endogenous (184). There linear. The most important point is that close proximity matters, which challenges conventional wisdom that people are willing to walk up to are likely attitudes, preferences, or other attributes motivating such one-quarter mile as well as analogous cycling specific hypotheses. bicycling or walking behavior (185, 186). Such attributes are not directly captured in this analysis--and, strictly using the results from this research, we would be remiss to conclude that adding CONCLUSION retail or bicycle paths would directly induce such behavior. This investigation makes progress by using focused research and This research reports the results of individual level models predict- carefully measured variables. The work raises a number of important ing bicycling and walking behavior and correlations with proximity to data, measurement, and methodological issues for future researchers bicycle paths and neighborhood retail, respectively. We do so focus- endeavoring to predict levels of walking or bicycle use for entire ing on particular behavior--whether an individual biked or walked cities or metropolitan areas. We make headway in learning that dis- from home--and robustly measuring policy relevant dimensions of tance matters--particularly close distance. Relative to the larger the built environment. The travel, bicycle facility, and the retail data picture of travel behavior, however, our understanding remains we employed are the most precise among city-wide measures for a unclear. The evidence suggests that features of the built environ- metropolitan area in the U.S. The primary merits of this exercise focus ment matter, although it is hardly compelling. Statistical analysis specifically on measuring the exposure measures, each of which have like ours needs to be complemented with more direct sampling as direct policy relevance. To our knowledge, this question has not well as qualitative modes of analysis to shed light on different fac- previously been asked or answered across an entire city. tors and attitudes as well as sorting out the issue of residential self- We separated facilities into two categories: off-street bicycle selection. Further work will inevitably allow planners and modelers trails and on-street bicycle lanes. For the former group of facilities, to better understand relationships between cycling and walking there is no effect of proximity to off-street bike facilities on bicycle infrastructure and physical activity. Continued and thorough under- use. For on-street bicycle lanes, subjects living within 400 meters standing will therefore assist policymakers to construct better of a bike facility had significantly increased odds of bike use com- informed policies about using features of the built environment to pared with subjects living more than 1,600 meters from an on-street induce physical activity, namely walking and cycling.