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36 These factors were chosen by the research team, with input section. For example, efforts were made to identify sections of from the project panel, as those factors that could most eas- video in which the roadway width did not change during the ily be measured by engineers, those that were most important drive or that the sidewalk conditions were relatively consis- to drivers (as determined in previous studies and Phase I of tent. Using a portable mini-DV player, students identified the the study), and those that could be captured in the field portions of roadway to be made into a clip based on criteria through videotaping. such as arterial type, consistent cross section, lane position, Arterials were selected in the Washington, DC, metropol- and speed limit. After the general area of the clip was identi- itan area that captured the required combination of condi- fied, the researchers turned to Microsoft MapPoint. tions. As noted, some of the video clips were developed in After each section of roadway was identified, individual Phase I of the study; an additional subset of video clips were clips needed to be made. The video feed needed to be syn- developed by GMU in the summer/fall of 2005 in preparation chronized with the speedometer feed. This was done using of the data collection in the summer of 2006. the mini-DV player and the time stamps on it. The field team As with the Phase I pilot test, videos were created for day- had announced the run orally while the videocameras were light conditions only. Taping was also limited to clear days filming the study arterials. The researcher's voice was used to without precipitation, and for the most part, snow is not a synchronize time stamps of the videocameras. Then, the feature on the majority of tapes. researchers found the location of the beginning and end of In order to film the video clips, the following testing mate- the proposed clip and determined the tape length equivalen- rials were used: cies for the two video feeds, for example 1 minute 6 seconds into the tape was when the voice was first heard on tape 1, · Vehicle; 1 minute 20 seconds into the tape was when the voice was first · Two video cameras (one to capture the driver's perspective heard on tape 2. and one to capture the speedometer); and After identifying the time stamps for both the road video · Two camera tripods. and the speedometer, the team began editing using the video editing equipment available at GMU's Media Laboratory to Standard vehicles (e.g., station wagons, sedans, and, in a cut the clips and merge the speedometer video into the lower few cases, small sports utility vehicles) were used for video- righthand corner of the video screen to simulate driving the taping. Vehicles were rented from the GMU motor pool so as vehicle. Adobe Premiere 9.0 was used to merge the two videos to standardize the vehicle set up and ride quality. and create each clip. Once all the clips were made, transitions Researchers set up two cameras and the GPS unit when were put in between each clip on the final media to help proc- they arrived at the vehicle rental location. A professional JVC tors and participants identify each clip (for example, Clip #3) digital videocamera, loaned to the project by the GMU Media using the same software package. Then, the clips were merged Laboratory, was used to capture the roadway scene from the and burned onto DVDs. driver perspective (typically a full windshield view and pe- Exhibit 37 summarizes the characteristics of the auto clips. ripheral views of the roadside) and a palm-sized digital video- camera was used to capture the speedometer view. Bicycle Video Clips After the initial taping runs took place, the individual clips needed to be extracted. Based on the requirement of 1/2-mile Bicyclists are among the most vulnerable of travellers and on urban arterials (as determined through Phase I efforts), are affected by a broader variety of traffic and roadway envi- these clips were developed. The emphasis was on extracting ronmental factors (stimuli) than that of the motorized segments from the videos that met several criteria including: modes. Consequently, when collecting data and modeling After the videotaping took place, the researchers used the perceptions, care must be taken to capture this sensitivity to following to extract the videos: the many environmental factors. Previous research, model development, and nationwide · Video editing decks available in the GMU Media Laboratory; deployment of non-motorized LOS mode models have · Adobe Premiere 9.0 video editing software; demonstrated that field-based studies are desirable to capture · Microsoft MapPoint; accurate perceptions of bicyclists. Such studies place the par- · Microsoft Excel; ticipants in typical real-life situations and capture the partic- · Original mini-Digital Videos (DV) created in the field; and ipants' response to the host of stimuli present in roadway · Mini-DV player. environments affecting bicyclists. However, field studies can be expensive and, depending on the range of conditions and In order to depict a consistent scene to study participants, variables being explored, represent the highest risk for par- it was necessary to identify video clips that had consistent cross ticipants of any method.

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37 Exhibit 37. Summary of Auto Clip Characteristics. Width of parking lane (ft) Separation from right-of- Rigth Shoulder width (ft) Average Lane Width (ft) Left Shoulder Width (ft) Pres. Of Rt Turn Lane- # Stops (below 5 mph) Pres. Of Ex. LT Lane - Width of bike lane (ft) Width of sidewalk (ft) Clip Distance (miles) Presence of Median Space Mean Speed Width of Median (ft) Number of Through way to sidewalk (ft) Total Travel Time Total # of Signals LOS as per HCM PED on sidewalk Tree Presence Street Name HCM Class (seconds) Signals Signals Lanes Clip # 1 0.50 Rt 234 1 1 3 3 119 15.1 0 1 2 1 1 2 12 54 0 3 0 4 3 0 2 0.46 Gallows Road 3 6 2 3 48 34.5 0 0 3 1 1 2 13 4 0 0 0 4 3 0 5 0.50 Wilson Blvd 3 5 2 3 60 30.0 2 0 3 1 1 1 14 0 0 0 7 10 0 5 6 0.43 Clarendon 3 3 2 1 87 18.3 2 1 2 1 0 1 14 0 0 0 7 4 0 0 7 0.48 Wilson Blvd 3 4 2 1 86 20.1 2 0 3 1 0 1 14 0 0 0 7 10 0 5 8 0.49 Wilson Blvd 3 2 2 1 130 13.6 2 2 5 1 1 1 12 0 0 0 8 14 0 6 10 0.53 Washington Blvd 3 3 1 0 113 16.9 2 2 3 0 0 3 12 0 0 0 8 6 0 0 12 0.47 Wilson Blvd 3 3 2 0 118 14.3 0 2 2 0 0 1 11 0 0 0 8 11 5 0 13 0.50 Washington Blvd 3 5 1 0 71 25.4 1 0 1 0 0 3 12 0 0 0 8 6 0 0 14 0.50 Glebe Road 2 1 3 3 161 11.2 2 3 3 1 1 1 11 4 0 0 0 8 0 0 15 0.50 Glebe Road 2 1 3 3 229 7.9 2 3 3 1 1 1 11 4 0 0 0 8 0 0 16 0.55 Fairfax Drive 3 1 2 3 163 12.1 2 4 4 1 1 1 11 10 0 0 8 16 0 5 19 0.52 23rd St 4 4 2 0 116 16.1 2 3 8 0 0 2 10 0 0 0 7 6 5 0 20 0.55 Rt 50 1 2 2 3 122 16.2 2 1 2 1 0 1 11 17 8 2 0 0 0 0 21 0.50 Rt 50 1 2 2 3 89 20.2 2 2 3 1 1 2 11 17 8 2 0 0 0 0 23 0.54 M St 4 2 2 0 243 8.0 2 3 8 0 0 1 10 0 0 0 10 10 0 0 25 0.54 M St 4 3 2 0 179 10.9 2 2 8 0 0 1 10 0 0 0 10 10 0 0 29 0.50 Rt 234 2 4 3 3 79 22.8 0 1 3 1 1 2 12 54 0 3 0 0 0 0 30 0.55 M St 4 1 2 0 298 6.6 2 8 8 0 0 1 10 0 0 0 10 10 0 0 31 0.50 M St 4 1 2 0 471 3.8 2 9 8 0 0 1 10 0 0 0 10 10 0 0 51 0.44 M St 4 1 2 0 240 6.5 2 4 9 0 0 1 10 0 0 0 10 10 0 0 52 0.41 M St 4 2 2 0 186 7.9 2 3 7 0 0 1 10 0 0 0 10 10 0 0 53 0.60 Prosperity 2 3 2 3 121 18.5 0 1 2 1 1 2 12 15 0 0 0 4 4 0 54 0.60 Lee Hwy 2 4 2 2 93 24.5 0 2 4 1 1 3 12 14 4 4 0 4 10 0 55 0.45 Braddock Rd 2 1 2 3 128 12.7 0 1 1 1 1 3 12 15 0 0 0 6 0 0 56 0.50 Sunset Hills Rd 2 4 2 3 77 23.1 0 1 1 1 0 3 12 8 0 0 0 0 0 0 57 0.61 Sunset Hills Rd 2 3 2 0 129 17.4 0 2 2 0 0 3 12 0 0 0 0 4 2 0 58 0.60 Sunrise Valley Rd 2 1 2 3 144 11.2 0 1 3 1 0 3 12 10 0 0 0 3 4 0 59 0.61 Sunset Hills Rd 2 1 2 0 182 12.1 0 3 2 0 0 3 12 0 0 0 0 4 4 0 60 0.50 Lee Hwy 2 2 2 2 120 15.0 0 1 3 1 0 1 12 14 0 0 0 4 4 0 61 0.70 Rt 50 1 4 3 0 91 27.7 0 1 3 1 0 3 12 0 0 0 0 0 0 0 62 0.50 Rt 50 1 5 3 0 49 36.7 0 0 2 1 0 3 12 0 0 0 0 0 0 0 63 0.50 Rt 50 1 6 2 3 53 41.9 0 0 2 1 1 3 12 6 4 4 0 0 0 0 64 0.50 Rt 50 1 2 2 3 92 19.6 0 1 3 1 0 3 12 6 0 0 0 0 0 0 65 0.50 Lee Hwy 2 6 2 2 50 36.0 0 0 3 1 0 2 12 14 0 0 0 0 0 0