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26 The video clips showed various characteristics, including a Between the two bicycle quality of service studies, the lab- range of curb lane widths, motor vehicle speeds, traffic vol- oratory study conducted by FHWA found very similar factors umes, and bicycle/paved shoulder widths. that influenced quality of service ratings. However, the field Participants were asked to rate their comfort level based on studies revealed variables that would be difficult to simulate a 6-point scale in the following categories: volume of traffic, in a laboratory setting, such as percentage of heavy vehicles speed of traffic, width or space available for bicyclists, and and pavement surface condition. The participants in the field overall rating. In the end, eight variables were found to be sig- study rode alongside traffic and rated the percentage of heavy nificant in the BCI regression model: vehicles as one of the top important factors followed by the condition of the pavement. This comparison of data collec- Number of lanes and direction of travel; tion opportunities is the only one that can be made at this Curb lane, bicycle lane, paved shoulder, parking lane, and time for similar modes of travel, but may provide insight into gutter pan widths; the limitations of laboratory studies as compared with field Traffic volume; studies. Speed limit and 85 percentile speed; Median type (including two-way left turn lane); Measuring LOS Through Route Choice Driveway density; Presence of sidewalks; and Stinson and Bhat [41] conducted a web-based stated- Type of roadside development. preference survey of 3,145 individuals. The individuals were recruited through announcements placed with 25 bicyclist- Given that this research was done in a laboratory setting, oriented listservers in the United States. Additional an- the subjects could not take into account the comfort effects of nouncements were made to a few non-bicyclist-oriented pavement condition, crosswinds, and suction effects caused e-mail lists. The sample of respondents was heavily by high-speed trucks and buses. These factors consequently weighted toward members of bicycling groups. either do not show up or show up to a lesser extent in the BCI The authors identified 11 link and route attributes (each model. with multiple levels) for testing. To avoid participant over- Landis et al. [40] conducted a field survey of nearly 150 bi- load, no more than four attributes were considered in any cyclists who rode a 27-km (17-mile) course in Tampa, Florida. given survey instrument; thus, nine different instruments The subjects ranged in age between 13 and over 60 years of were required so as to cover the full range of attributes (and age, with 47 percent being female and 53 percent being male. levels) of interest. The range of cycling experience was also broad--25 percent of The respondent characteristics were as follows: the participants rode less than 322 km (200 miles) yearly to approximately 39 percent of the participants riding over 91% were experienced bicycle commuters. 2,414 km (1,500 miles) yearly. In the study, participants were 22% were female. asked to evaluate the quality of the roadway links, not the in- About 9% lived in rural areas, 39% lived in urban areas, the tersections, on a 6-point scale (A to F) as to how well they were rest of the respondents lived in suburbs. served as they traveled each segment. They were asked to only include conditions within or directly adjoining the right of way Stinson and Bhat identified travel time as the most impor- and to exclude aesthetics of the segments. Several significant tant factor in choosing a route, followed by presence of a factors were found to influence bicyclists' perceived quality of bicycle facility (striped lane or a separate path). Road class service or perceived hazard rating: (arterial or local) was the third most important factor. Stinson and Bhat obtained 34,459 observations of route Volume of directional traffic in 15-min period; choice and found that the best model of route choice consid- Total number of through lanes; ered the interactions between the bicyclist characteristics Posted speed limit; (e.g., age, residential location, and experience bicycling) and Percentage of heavy vehicles in the traffic stream; the route attributes. Stinson and Bhat noted however that the Trip generation intensity of the land adjoining the road attributes of the route had a greater effect on route choice segment; than the characteristics of the bicyclists themselves. Effective frequency per mile of non-controlled vehicular access (e.g., driveway and on-street parking spaces); Models of Rural Road Bicycle LOS FHWA's five-point pavement surface condition rating; and Jones and Carlson [42] developed a rural bicycle compat- Average effective width of the outside through lane. ibility index (RBCI) following a similar approach as that used