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 29
29 3. All methods need to be applicable to a full range of pedes- Where trian facility types. Presence of sidewalk should not be a Wol = Width of outside lane (feet) prerequisite. Wl = Width of shoulder or bike lane (feet) 4. The scale methodologies, although innovative, need fur- fp = On-street parking effect coefficient (=0.20) ther work to overcome problems with overlap of factors, %OSP = Percent of segment with on-street parking small sample sizes, and nonlinear performance. fb = Buffer area barrier coefficient (=5.37 for trees spaced 5. There is a need to consider a full and far broader range of 20 feet on center) factors for determining LOS. Wb = Buffer width (distance between edge of pavement and sidewalk, feet) Landis et al.  developed a method to measure pedes- fsw = Sidewalk presence coefficient = 6 0.3Ws (3) trian LOS, to aid in design of pedestrian accommodations on Ws = Width of sidewalk (feet) roadways, that is based on field measurements of pedestrian Vol15 = Traffic count during a 15-minute period perceptions of quality of service. L = total number of (through) lanes (for road or street) The survey included 75 volunteer participants walking a SPD = Average running speed of motor vehicle traffic (mi/hr) 5-mile (8-km) looped course consisting of 48 directional seg- ments. Traffic volumes ranged between 200 and 18,500 vehi- Use of Visual Simulation cles on the day of the survey. Heavy vehicles accounted for 3% or less of the traffic that day. Traffic running speeds Miller et al.  describes the use of computer-aided ranged from 15 to 75 mph (25-125 km/h). visualization methods for developing a scaling system for The participants were asked to evaluate each segment ac- pedestrian level of service in suburban areas. A group of test cording to a 6-point (A to F) scale (see Exhibit 33) how subjects was presented with simulations (computer anima- safe/comfortable they felt as they traveled each segment. Level tions and still shots) of scenarios of improvements to a sub- A was considered the most safe/comfortable (or least haz- urban intersection at an arterial. The subjects were asked to ardous). Level F was considered the least safe/comfortable (or rate each option from A (best) to E (worst) and also to give most hazardous). a numerical score from 1 to 75. These ratings were compared Scoring fatigue was noticed as segment scores decreased as with a set of LOS ratings derived from a scale in which points each participant walked the length of the course (Partici- were assigned based on various intersection characteristics: pant's expectations for the quality of the service drifted median type, traffic control, crosswalks, and speed limits. downward as they walked the course. Initial segments were The results of the experiment led to a substantial revision of rated more critically than later segments. It required about the scale ranges that correspond to specific levels of service. 2 hours to walk the length of the course). This problem was The authors concluded that, although visualization cannot dealt with by walking people in opposite directions over the replace real-world experience, it can be an appropriate tool looped course and letting the fatigue effect cancel itself out for site-specific planning. The methods discussed are ". . . in- through averaging of the responses. expensive, practical, and original ways of validating a scale After eliminating outliers, a total of 1,250 observations were that help ensure that the pedestrian environment is not available for analysis. A stepwise linear regression was per- unnecessarily compromised, especially on automobile- formed. The resulting equation had an R-square value of 85%, dominated arterials." but later researchers have noted that this value was for the abil- ity of the model to predict the average LOS for a segment, not Midblock Crossing LOS Studies the actual LOS values reported by each individual participant. Human factors are completely absent from the pedestrian Chu and Baltes  developed a LOS methodology for LOS model. Age, sex, physical condition, experience, and res- pedestrians crossing streets at mid-block locations. idential location (i.e., urban, suburban, or rural) have no effect on the perceived LOS in this model. Crowding and in- Exhibit 33. LOS termodal conflicts with bicycles using the same facility are Categories. among the operational factors not included in the model. Grades, cross-slopes, and driveways are among the physical LOS Model Score factors not included in the model. A 1.5 B 1.5 and 2.5 C 2.5 and 3.5 Ped LOS = -1.2021 ln (Wol + Wl + fp × %OSP D 3.5 and 4.5 + fb × Wb + fsw × Ws) + 0.253 ln (Vol15/L) E 4.5 and 5.5 + 0.0005 SPD2 + 5.3876 (Eq. 8) F 5.5