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Guidelines for Analysis of Investments in Bicycle Facilities (2006)

Chapter: Appendix D: User Mobility Benefits

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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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Suggested Citation:"Appendix D: User Mobility Benefits." 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.
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D-1 APPENDIX D USER MOBILITY BENEFITS Trails, Lanes, or Traffic: Valuing Bicycle Facilities with an Adaptive Stated Preference Survey INTRODUCTION If bicycling is to be a viable mode of transportation, it must have appropriate facilities. Evaluating what is appropriate requires an understanding of preferences for different types of cycling facilities. In this study we explore and provide a quantitative evaluation of indi- vidual preferences for different cycling facility attributes. This under- standing can be incorporated into an evaluation of what facilities are warranted for given conditions. The facilities considered here are A) Off-road facilities, B) In- traffic facilities with bike lane and no on-street parking, C) In- traffic facilities with a bike lane and on-street parking, D) In-traffic facilities with no bike lane and no on-street parking, and E) In-traffic facilities with no bike lane but with on-street parking. The aim is to understand what feature people desire by quantifying how many additional minutes of travel they would be willing to expend if these features were to be available. This added travel time is the price that individuals are willing to pay for the perceived safety and comfort the attributes provide. A computer based adaptive stated preference survey was devel- oped and administered to collect data for this study. To understand if the value that people attach to attributes of facilities is systematically related to different individual and social characteristics, the study has also collected demographic, socioeconomic, household, and current travel mode information from each participant. This information is then used to build an empirical model to evaluate relationships between these independent variables and the additional travel time that people are willing to expend for different attributes of cycling facilities. In addition to giving a measure of the appeal of the attrib- utes under discussion, the model also highlights the social and indi- vidual factors that are important to consider in evaluating what facilities to provide. Interest in studying bicyclists and cycling environments is grow- ing. Recent papers by a number of authors have investigated pref- erences of cyclists and the bicycling environment as well as the relationship between the supply and use of facilities. Availability of cycling facilities and the type and quality of a cycling facility are important determinants of how well they are used. Studies by Dill et al. (55) and Nelson et al. (174) have shown that there is a positive correlation between the number of facilities that are provided and the percentage of people that use bicycling for commuting purposes. While both studies state that causality cannot be proved from the data, Nelson and Allen (174) state that in addition to having bicycle facil- ities, facilities must connect appropriate origins and destinations to encourage cycling as an alternative commuting mode. Bovy and Bradley (194) used stated preference (SP) to analyze bicycle route choice in the city of Delft. Their work looked at facility type, surface quality, traffic levels and travel time in route choice. They found that travel time was the most important factor in route choice followed by surface type. Another study by Hopkinson and Wardman (195) investigated the demand for cycling facilities using stated preference in a route choice context. They found that individ- uals were willing to pay a premium to use facilities that are deemed safer. The authors argue that increasing safety is likely more impor- tant than reducing travel time to encourage bicycling. Abraham et al. (196) also investigated cyclist preferences for different attributes using a SP survey again in the context of route choice. Respondents were given three alternate routes and their attrib- utes and were then asked to rank the alternatives. The responses were analyzed using a logit choice model. Among other variables that were of interest to their study, the authors found that cyclists prefer off- street cycling facilities and low-traffic residential streets. But the authors also claim that this may be due to an incorrect perception of safety on the part of the respondents, and education about the safety of off-road facilities may change the stated choice. Shafizadeh and Niemeier (197) investigate the role that proximity to an off-road bicycle trail plays in route choice decisions. Using inter- cept surveys along the Burke-Gilman trail in Seattle, they find that among people who reported origins near the off-road facility, travel time gradually increases as they are further from trail to a point and then decreases, leading them to speculate that there may be a 0.5 to 0.75 mile “bike shed” around an off-road bike path, within which indi- viduals will be willing to increase their travel time to access that facil- ity and outside of which a more direct route seems to be preferred. Aultmann-Hall, Hall, and Beatz (198) use GIS to investigate bicycle commuter routes in Guelph, Canada. While comparing the shortest path to the path actually taken, they found that people diverted very little from the shortest path and that most bicycle com- muters use major road routes. They found little use of off-road trails. While this may be due to the location of the trails and the O-D pair they connect, even in five corridors where comparably parallel off- road facilities do exist to in-traffic alternatives, they found that commuters used the in-traffic facilities much more often. Only the direct highest quality off-road facility (one that is “wide with a good quality surface and extends long distance with easy access points”) seemed to be used relatively more. Stinson and Bhat (199) using data from a web based stated pref- erence survey estimate a logit model to understand important attrib- utes for commuter cyclist route choice. They find that respondents preferred bicycling on residential streets to non residential streets, likely because of the low traffic volumes on residential streets. While their model showed that the most important variable in route prefer- ence was travel time, the facility was also significant. It was shown that cyclists preferred in-traffic bike lanes more than off-road facil- ities. Both facility types had a positive effect on utility but the for- mer added more to utility than the latter. In addition they find that cyclists try to avoid links with on-street parking. Another study by Taylor and Mahmassani (200), also using a SP survey to investigate bike and ride options, finds that bike lanes provide greater incen- tives to inexperienced cyclists (defined as those with a “stated low

to moderate comfort levels riding in light traffic”) as compared with more experienced cyclists, with the latter group not showing a sig- nificant preference to bike lanes over wide curb lanes. The results from these papers seem somewhat mixed. Though some of the research has shown a stated preference and revealed preference with some constraints for off-road facilities, others have shown that cyclists generally prefer in-traffic cycling facilities with bike lanes. Especially in revealed preference cases, the apparent pref- erence for in-traffic routes may be due to their ability to connect to many destinations in a more direct fashion and therefore leading to a lower travel time. In addition, route choice may be restricted by facil- ity availability, geographic features or missing information. It may also be that for people who regularly bicycle, who are most likely the subjects of the revealed preference studies, travel time and not per- ceived safety are likely of utmost importance, as these individuals are more likely to be conditioned to the cycling environment. The actual preference therefore may not be for the in-traffic facility; however, it may be the best alternative available to the cyclists. Commuter choices are clearly limited by facilities that are avail- able to them. Understanding preferences and behavior is crucial to providing choices that people desire. This can be best accomplished when the value of any given improvement in facility attribute is known. Valuation of facility attributes can be done by considering what people are willing to pay for using these facilities. In this study we try to uncover this value by measuring how much additional time individuals would be willing to spend bicycling between a given origin and destination if alternate facilities with certain attrib- utes were available to them. In the next section we present the methodology in detail. This is followed by a description of the survey instrument and design. The analysis methodology is presented, and then the results. METHODOLOGY The methodology we follow to extract this valuation of attrib- utes uses an Adaptive Stated Preference (ASP) survey. While both revealed and stated preference data can be used to analyze prefer- ences, there are certain advantages to using the latter method in this case. In using consumer revealed preference, often a limitation arises because only the final consumer choice is observed. This makes it dif- ficult to ascertain how consumers came to their final decision. This complication arises because the number of choices that are available to each consumer may be very large, and information on those alternatives that went into an individual’s decision may not be fully known. Even in cases where all possible alternatives are known, it is difficult to assess whether the decisionmakers considered all avail- able alternatives. In addition, the exact tradeoff of interest may not be readily available. Even in cases where the tradeoffs seem to be avail- able, one cannot be certain that the consumer is acting out his prefer- ence for the attributes we are observing. The lack of appropriate data can pose a major challenge in this respect. Stated preference surveys overcome these complications because the experimenter controls the choices. In SP settings, the experi- menter determines the choices and the respondent considers. While this may not reflect the actual market choice that individual would make because of the constraints the survey places on the choice set, it allows us to measure attribute differences between the presented alternatives. Further, by using specialized forms of SP such as ASP, one can measure the exact value individuals attach to attributes of D-2 interest. In this type of survey each option is presented based on choices the respondent has already made. This allows for the pre- sentation of choices that the individual can actually consider while removing alternatives that the respondent will surely not consider. This methodology has been adopted in a number of con- texts, including value of time for commercial vehicle operators (201), in mode choice experiments (202), and in evaluating tran- sit improvements (203). SURVEY INSTRUMENT, DESIGN, AND ADMINISTRATION All respondents of the ASP survey were given nine presentations that compared two facilities at a time. Each presentation asks the respondent to choose between two bicycle facilities. The respondent is told that the trip is a work commute and the respective travel time they would experience for each facility is given. Each facility is pre- sented using a 10-second video clip taken from the bicyclists’ per- spective. The clips loop three times and respondents are able to replay the clip if they wish. Each facility is compared with all other facilities that are theoret- ically of lesser quality. For example, an off-road facility (A) is compared with a bike lane no on-street parking facility (B), a bike lane with parking facility (C), a no bike lane and no parking facil- ity (D), and a no bike lane with parking facility (E). Similarly, the four other facilities (B, C, D and E) are each compared with those facilities that are theoretically deemed of a lesser quality. The less attractive of the two facilities is assigned a lower travel time and the alternate (higher quality) path is assigned a higher travel time. The respondent goes through four iterations per presentation with travel time for the more attractive facility being changed according to the previous choice. The first choice set within each presentation assigns the lesser quality facility a 20-minute travel time and the alternate (higher quality) path a 40-minute travel time. Travel time for the higher quality facility increases if the respondent chose that facility, and it decreases if the less attractive facility was selected. A bisection algorithm works between 20 and 60 minutes either raising or lower- ing the travel time for the alternate path so that it becomes less attrac- tive if it is chosen or more attractive if the shortest path is chosen. By the fourth iteration, the algorithm converges on the maximum time difference where the respondent will choose the better facility. This way the respondent’s time value for a particular bicycling environ- ment can be estimated by identifying the maximum time difference between the two route choices that he/she will still choose the more attractive facility. Pictures of these facilities are shown on Figure 13. Figure 14 maps the locations of the facilities where the videos were taken in St. Paul, Minnesota. The procedure used to converge on the time trade-off for the par- ticular facility is illustrated as follows. If the subject first chose the longer option, then the next choice set assigns a higher travel time for the higher quality path (raised from 40 minutes to 50 minutes). If the respondent still chooses the longer option, the travel time for that choice increases to 55 minutes and the choice is posed again. If on the other hand, the 50-minute option is rejected and the respondent chose the 20-minute route, the bisection algorithm will calculate a travel time that is between the now rejected option and the previously accepted option, in this case 45 minutes. By the time the respondent makes a fourth choice, the survey will have either narrowed down the respondent’s preference to within 2 minutes or the respondent will

D-3 Figure 13. Cycling facilities used in the study. Figure 14. Location of facilities used in the Adaptive Stated Preference survey. (Note: (A) off-road facility; (B) bicycle lane, no parking facility; (C) bicycle lane, on-street parking facility; (D) no bicycle lane, no parking facility; (E) no bicycle lane, on-street parking facility.) (D) Bike lane, no parking (E) No bike lane, on-street parking (A) Off-road bicycle facility (B) Bike lane, no parking (C) Bike lane, on-street parking

have hit the maximum travel time that can be assigned to the longer trip, which is 58.5 minutes. Table 16 shows the pairs of comparisons that were conducted and used in the analysis. Table 17 shows a sample series of travel time presentations and Figure 15 shows sample screenshots of the survey instrument. The survey was administered in two waves, once during winter and once during summer. The winter and summer respondents were shown video clips that reflected the season at the time of the survey taken at approximately the same location. Our sample for both waves comprised employees from the University of Minnesota, excluding students and faculty. Invitations were sent out to 2,500 employees, randomly selected from an employee database, indicating that we would like them to participate in a computer based survey about their commute to work and offering $15 for participation. Participants were asked to come to a central testing station, where the survey was being administered. A total of 90 people participated in the winter D-4 survey and another 91 people participated in the summer survey, making a total of 181 people. Among these, 13 people had to be removed due to incomplete information, leaving 168 people. Of these 168, 68 people indicated that they have bicycled to work at least once in the past year. Thirty-eight of these 68 identified themselves as regular bicycle commuters at least during the summer. Also, 127 of the 168 people said they have bicycled to somewhere, including work, in the past year. Further demographic information on the respondents is given in Table 18. MODEL SPECIFICATION AND RESULTS Switching Point Analysis The adaptive nature of the survey allows us to extract the actual additional minutes each individual is willing to travel on an alternate facility. In the context of the survey, this is the maximum travel time beyond which the respondent would switch to use the base facility. For each pair of facilities that are compared during the summer and the winter, the averages of this switching point are computed and plotted in Figure 16. On average, individuals are willing to travel more on an alternate facility when the base facility is E (undesig- nated with on-street parking), followed by D (no bike lane without parking) and C (bike lane with parking). For example, individuals are willing to travel further on facility B when the base facility is E, as opposed to D or C. Figure 16 shows the hierarchy between facilities clearly—each of the lines plotted connects the average additional travel time that indi- viduals are willing to bicycle over the 20 minutes that they would have bicycled if they had chosen the base facility. For example, look- ing at the winter data, the top solid line connects the average addi- tional time individuals say they would travel on an alternate facility when the base facility is E (in-traffic with parking at 20 minutes). The alternate facilities are as shown on the horizontal axis. For exam- ple, on average respondents are willing to travel about 22 additional TABLE 16 Facility pairs compared in the ASP survey Base Route B Bike lane, no parking C Bike lane with on- street parking D No bike lane, no parking E No bike lane with on-street parking A off-road T1 T2 T3 T4 B Bike lane, no parking N/A T5 T6 T7 C Bike lane with on-street parking N/A N/A N/A T8 set u or eta nretl A D No bike lane, no parking N/A N/A N/A T9 Ti represents the average additional travel time users are willing to travel. TABLE 17 Choice order for a sample presentation Facility Travel Time Presentation Route 1 Route 2 Choice choice set 1 40 min 20 min Route 2 choice set 2 30 min 20 min Route 1 choice set 3 35 min 20 min Route 1 choice set 4 37 min 20 min Route 2 Ti 36 min

D-5 Figure 15. Top: comparing designated bicycle lanes with no parking with in-traffic bicycling with no parking. Bottom: same presentation three iterations later.

minutes if an off-road bike path is available if the alternative is to bike in traffic. We can further describe the data by employing tech- niques such as the non-parametric bootstrap. The bootstrap approx- imates the sampling distribution of the mean by repeatedly sampling with replacement from the original data. We employ the nonparam- etric bootstrap where no prior assumptions are made on the distrib- ution of the statistic. The bootstrap approach was first developed by Efron in 1979 (204). Consider the histogram shown in Figure 17, it reflects the addi- tional travel times individuals in the sample said they would travel between facilities A (off-road) and C (in traffic with parking). It is dif- ficult to make any distributional assumptions based on the observed sample. Employing the nonparametric bootstrap on this data with 5,000 resamples (Figure 18), we can see that the bootstrap distrib- ution of the mean is very close to normal, and hence a normal inter- val can be built around it. The bootstrap distributions of all nine pairs of comparisons lead to very symmetric distributions that show no evi- dence of non-normality. The percentile confidence interval based on the actual 2.5% and 97.5% values of the bootstrapped mean are also computed. The bootstrap also allows us to estimate the bias of the sample mean. The sample mean, the estimate of the bias and the con- fidence interval (CI) using the normal distribution and the percentile of the bootstrap are reported in Table 19 for each pair of comparisons both for the combined and season specific data. Model We start with the economic paradigm of a utility maximizing indi- vidual, where given a bundle of goods the individual chooses that bundle which results in the highest possible utility from the choice set. In the current context then, given two alternatives, the chosen alternative is the one that the respondent derives a higher utility from. We can then break down each bundled alternative to its components to understand what amount each contributes to utility. This will enable us to extract the contribution of each feature of the facility in the choice consideration of the individual. Mathematically, we would state this as alternative A is selected if UA is greater than UB, where A and B are the alternatives and U is the utility function. We hypothesize that the utility a user derives from using a bicycle facility depends on the features of the facility and the expected travel time on the facility. Choices are also affected by individual char- acteristics that we may not directly observe but can try to estimate using individual specific variables such as income, sex, age etc. As discussed earlier, each individual records a response over various alternatives, and therefore the data reflects the repeated choices over the same respondent. This implies that the errors are no longer inde- pendently distributed. To overcome this problem one can use a gen- eralized linear mixed model which would estimate a random effect for the between-subject effect, thus separating the within-subject and between-subject errors. Both subject random effects are assumed to have a normal distribution with zero mean and separate variances. The error term of the utility’s linear component is assumed to have a Gumbel distribution. The model’s linear utility component is spec- ified as follows: The utility of a particular alternative can be written as follows: Where: W = Weather (winter = 1, summer = 0) O = dummy indicating whether the facility is off-road (1 = Yes, 0 = No) B = dummy indicating whether the facility has a bike lane (1 = Yes, 0 = No) P = dummy indicating whether on-street parking is absent or present (1 = absent, 0 = present) T = Expected travel time on the facility being considered S = Sex (Male = 1, Female = 0) A = Age I = Household Income (Inc/1000) H = Household Size (>2 = 1, Otherwise = 0) C = Cyclist at least during summer (Yes = 1, No = 0) β = estimable coefficient  ∼ Gumbel (0, λ) To interpret the model appropriately it is important to note how the dummy variables are coded (Table 20). Variable B represents whether a facility has a designated bike lane, O represents whether V W O B P T SiA iA iA iA iA iA iA= + + + + + + +β β β β β β β0 1 2 3 4 5 6 β β β β 7 8 9 10 A I H C iA iA iA iA+ + + U ViA iA iA= +  U = f (Facility, Travel Time, Season, Individual Variables) D-6 TABLE 18 Demographic distribution of respondents Number of subjects 168 Sex % Male 34.5% % Female 65.5% Age Mean (Std. deviation) 44.19 (10.99) Usual mode (Year round) %Car 69.7% %Bus 18.5% %Bike 9.2% %Walk 2.6% Bike commuter All season 9.2% Summer 22.6% HH income < $30,000 8.3% $30,000 - $45,000 14.3% $45,000 - $60,000 19.6% $60,000 - $75,000 15.5% $75,000 - $100,000 20.2% $100,000 - $150,000 17.9% > $150,000 4.2% HH Size 1 25.0% 2 32.7% 3 16.7% 4 20.8% > 4 4.8%

the facility is off-road, and P represents whether a facility has no parking adjacent to it. This would allow separately valuating bike lanes as well as being off-road. It should be observed that ‘O’ is not equivalent to an off-road trail. ‘B’ and ‘O’ together constitute an off-road trail. The parameter estimates of binomial logit model are given in Table 21. The model is estimated such that the results indicate the D-7 odds of choosing the theoretically better facility. Choices depend on the attributes of the facilities, the travel time the user experiences on the facilities, and individual characteristics. The signs of the esti- mated parameters are as expected. The travel time is negative show- ing an aversion to longer trips. The improvements (off-road, bike lane and no parking) all have a positive and significant influence on choice of different magnitudes. Of these three, a bike lane improvement Figure 16. Hierarchy of facilities. (Note: (A) off-road facility; (B) a bike lane, no parking facility; (C) a bike lane, on-street parking facility; (D) a no bike lane, no parking facility; (E) a no bike lane, on-street parking facility.) Maximum Additional Travel Time Between Facility Pairs (Combined Data) 0.00 5.00 10.00 15.00 20.00 25.00 Alternate Facility Tr av el T im e B C D E Maximum Additional Travel Time Between Facility Pairs (Winter data) 0.00 5.00 10.00 15.00 20.00 25.00 Alternate Facility Tr av el T im e B C D E Maximum Additional Travel Time Between Facility Pairs (Summer Data) 0.00 5.00 10.00 15.00 20.00 25.00 30.00 A B C D A B C D A B C D Alternate Facility Tr av el T im e B C D E Base Facility Base Facility Base Facility

D-8 Figure 17. Distribution of additional travel time for facility C over facility A. time Fr eq u en cy 0 10 20 30 40 35 30 25 20 15 10 5 0 Figure 18. The bootstrapped mean for the additional travel time between facilities A and C (based on 5,000 resamples). time Fr eq u en cy 14 16 18 20 40 0 30 0 20 0 10 0 0 increases the odds much more than a parking elimination or that of an off-road improvement alone. The season variable is negative and significant, indicating that people have lower odds of choosing the higher travel time facility during winter than during summer. Looking at the individual covari- ates that are used, income and sex are not significant at the 0.10 level; however the signs seem to indicate that women have a higher ten- dency to choose the facilities that are perceived safer (better quality) than men (p-value = 0.11); and higher incomes seem to be associated with a tendency to choose the better quality facility (p-value = 0.11). The cyclist variable, which indicates if the respondents use bicycling as their main mode at least during summer, is highly insignificant; indicating that preferences are not dictated by experience at least in this SP context. The model also tells us that older individuals have higher odds of choosing the better quality facility. Also, individuals whose household size is greater than two have lower odds of choos- ing the better quality, longer travel time facility. This may be because these individuals have higher constraints on their time than individ- uals who live in single or two person households. The estimates of a linear utility model can be used to determine the value of an off-road facility, a bike lane facility and a facility with no parking in terms of the time cost of travel. These are derived using the marginal rate of substitution between each of the facility features and travel time (Table 22). These values are derived based on SP ques- tions that have a 20-minute base travel time, and should be interpreted as such. Accordingly, a bike lane improvement is valued at 16.3 min- utes, a no parking improvement is valued at 8.9 minutes and an off- road improvement is valued at 5.2 minutes. This is to say, keeping utility at the same level, one can exchange the off-road improvement for 5.2 minutes of travel time, a bike lane for 16.3 minutes of travel time and a no parking improvement for 8.9 minutes of travel time. This says that the most value is attached to having a designated bike lane. While having an off-road facility would certainly increase the utility of the individual, most of the gains of an off-road facility seem to be derived from the fact that such facilities provide a designated bike lane. The absence of parking is also valued more than taking the facility off-road. An alternate specification of the model looks at time as a depen- dent variable and features of the facility as independent variables along with demographic covariates. This specification also employs a mixed models approach to account for the repeated measurements taken over the same subject. The dependent variable is the switching point travel time minus the base facility travel time. This approach yields similar patterns in the order of valuation of the different attrib- utes of the facilities and the expected directions of the parameter estimates. A side by side comparison of the two model coefficients is not possible; however, we can compare the values derived for dif- ferent facility pairs based on our logit model and the linear model (Table 23). This is given in Table 24 and Figure 19. As can be seen, most comparisons are very close to one another in magnitude. As Figure 19 shows, the results derived from the logit model more closely replicate what is observed in the raw data, even though that is not always the case across the nine comparisons. The overall assessment of the models suggests that designated bike lanes seem to be what are desired the most. It is also important to consider that both the linear and logit models found no evidence against the possibility that preferences between cyclists and non- cyclists are the same. This is encouraging in many respects, because it avoids the dilemma of which interest to serve. The policy impli- cation is that by addressing this common preference, we can ensure cyclists receive the facilities they prefer and non-cyclists get the facilities that they could at least consider as a viable alternative. CONCLUSION This appendix analyzes preferences for different cycling facilities using a computer-based adaptive stated preference survey with first person videos. Using the survey on 168 randomly recruited individ- uals, we derive the values that users attach to different cycling facil- ity features and expose which are most important. The choice data were collected based on individual preferences between different

D-9 TABLE 19 Mean additional travel time between facility pairs and confidence interval of the bootstrapped distribution of the mean Fac1 Fac2 Original Mean Bias Standard Error Normal 95% CI Percentile 95% CI Combined Data A B 14.21 0.0223 0.962 (12.30, 16.08) (12.41, 16.17) A C 16.00 0.0136 0.964 (14.10, 17.88) (14.16, 17.92 ) A D 18.46 -0.0160 0.984 (16.55, 20.41) (16.58, 20.40) A E 23.14 -0.0051 0.939 (21.30, 24.98) (21.26, 24.94) B C 10.13 0.0092 0.973 (8.21, 12.03) (8.25, 12.06) B D 13.73 -0.0008 0.957 (11.85, 15.61) (11.90, 15.62) B E 20.87 0.0245 0.956 (18.97, 22.72) (19.09, 22.84) C E 19.65 -0.0033 0.950 (17.79, 21.51) (17.79, 21.49) D E 18.25 0.0211 1.002 (16.27, 20.20) (16.35, 20.22) Winter Data Fac1 Fac2 Original Mean Bias Standard Error Normal 95% CI Percentile 95% CI A B 15.33 0.0208 1.335 (12.69, 17.92) (12.78, 18.00) A C 13.69 0.0339 1.327 (11.06, 16.26) (11.21, 16.40) A D 17.57 -0.0252 1.344 (14.96, 20.23) (14.99, 20.19) A E 20.66 -0.0025 1.319 (18.08, 23.25) (18.16, 23.28) B C 6.17 -0.0064 1.197 (3.83, 8.52) (3.97, 8.57) B D 10.86 -0.0244 1.180 (8.57, 13.19) (8.58, 13.25) B E 17.45 -0.0101 1.248 (15.02, 19.91) (15.02, 19.91) C E 17.39 -0.0097 1.264 (14.92, 19.87) (14.98, 19.92) D E 15.72 0.0074 1.270 (13.22, 18.20) (13.22, 18.22) Summer Data Fac1 Fac2 Original Mean Bias Standard Error Normal 95% CI Percentile 95% CI A B 13.04 -0.0051 1.338 (10.43, 15.67) (10.49, 15.74 ) A C 18.43 0.0146 1.353 (15.76, 21.07 ) (15.84, 21.16 ) A D 19.40 0.0079 1.434 (16.58, 22.20 ) (16.58, 22.25 ) A E 25.73 -0.0071 1.292 (23.21, 28.27 ) (23.18, 28.27 ) B C 14.28 0.0154 1.397 (11.53, 17.01 ) (11.63, 17.10 ) B D 16.75 -0.0128 1.481 (13.86, 19.66 ) (13.89, 19.68 ) B E 24.46 -0.0072 1.332 (21.85, 27.07 ) (21.78, 27.06 ) C E 22.03 0.0013 1.403 (19.27, 24.77 ) (19.30, 24.82 ) D E 20.92 -0.0055 1.485 (18.01, 23.83 ) (17.96, 23.82 )

D-10 TABLE 21 Logit model Random effects: VarianceGroup Std.Dev. 1.2451.550subject Fixed effects: Variable Description Estimate Std. Error z value Pr(>|z|) 0.1885 0.0025 0.0000 0.0000 0.0000 0.0000 0.0335 0.1171 0.1132 0.6003 –1.315 –3.028 –12.685 4.386 7.067 12.475 2.126 –1.567 1.584 –2.589 –0.524 0.472 0.207 0.004 0.060 0.065 0.067 0.010 0.223 0.003 0.229 0.253 –0.620 –0.627 –0.051 0.264 0.456 0.831 0.021 –0.350 0.005 –0.594 –0.133 (Intercept) W T O P B A S I H C Season (1 = winter, 0 = summer) Travel time Offroad Improvement? Parking Improvement? Bikelane Improvement? Age Sex (1 = M, 0 = F) Income HHsize ( if>2, 0 otherwise) Cyclist (1 = atleast summer, 0 = No) * * * * * * * * * * * * * * * * * TABLE 22 Time values of facility attributes Attribute Marginal Rate of Substitution (minutes) O – Off street improvement 5.20 P – Parking improvement 8.98 B – Bike lane improvement 16.36 TABLE 20 Coding for facility features Facility O B P A (Off-road) 1 1 1 B (Bike lane, No parking) 0 1 1 C (Bike lane, on-street parking) 0 1 0 D (In traffic, No parking) 0 0 1 E (In traffic, on-street parking) 0 0 0

D-11 TABLE 23 Linear model TABLE 24 Comparison of travel time values between facilities using the linear model and the logit model Random Effects (Intercept) Residual StdDev: 8.98 8.01 Fixed effects: Description Value Std. Error t-stat p-value (Intercept) 7.24 –4.13 2.38 3.50 5.98 0.15 –3.36 –3.75 –2.22 0.03 3.377 1.485 0.429 0.456 0.456 0.071 1.604 0.021 1.645 1.818 2.143 –2.782 5.540 7.673 13.127 2.092 2.093 1.475 –2.278 –1.221 0.032 0.006 0.000 0.000 0.000 0.038 0.038 0.142 0.024 0.224 * * * * * * * * * * * * * * W Season Winter? Yes=1 No=0 Yes=1 No=0 Yes=1 No=0 Yes=1 No=0 Yes=1 No=0 Yes=1 No=0 Male=1 Female=0 >2=1 ≤2=0 O P B A S I H C Offroad Improvement? Parking Improvement? Bikelane Improvement? Age Sex Inc/1000 Household Size Summer cyclist? ***0.001Significance **0.01 *0.05 +0.1 Comparison Facility 1 Facility 2 Logit Linear Mean (raw data) 1 A A A A B B C C D D E E E E B B C D 2 3 4 5 6 7 8 9 5.2 14.2 21.6 30.5 9.0 16.4 16.4 9.0 25.3 9.6 15.6 13.2 16.7 13.2 13.2 20.9 22.0 16.7 24.5 14.3 25.7 19.4 19.1 10.7 18.4 13.0 13.1

facilities having different travel times, but the same origin and desti- nation. From the raw data we have demonstrated that a hierarchy exists between the facilities considered, and we have extracted a mea- sure of how many additional minutes an individual is willing to expend on an alternate facility if it were available and provided cer- tain features that were not available on the base facility. The data were then used to fit a random parameter logit model using a utility maxi- mizing framework. A linear model was also estimated and compared with the results from the mixed logit model. The results show that D-12 users are willing to pay the highest price for designated bike lanes, followed by the absence of parking on the street and by taking a bike lane facility off-road. In addition, we are able to extract certain indi- vidual characteristics that are indicative of preferences such as age and household structure and make loose connections with sex and household income. Such an understanding can be incorporated into the planning process to help planners make appropriate recommen- dations and investment decisions in developing bicycle facilities that are more appealing to the public. Figure 19. Comparison of the estimates of the additional time willing to travel between facility pairs based on logit model, linear model, and the raw data. 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 1 2 3 4 5 6 7 8 9 Comparision (See Table 24) Tr av el T im e Logit Linear Raw data

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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.

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