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83 Bicycle LOS Model 1 BSeg = 0.507 Ln (V/(4*PHF*L)) + 0.199Fs*(1 + 10.38HV)2 + 7.066(1/PC)2 Bicycle LOS #1 = 0.160*(ABSeg) + 0.011*(exp(ABInt)) - 0.005(We)2 + 0.760 (Eq. 31) + 0.035*(Cflt) + 2.85 (Eq. 29) Where Bicycle LOS Model 2 BSeg = Bicycle score for directional segment of street. Bicycle LOS #2 = 0.20*(ABSeg) + 0.03*(exp(ABInt)) Ln = Natural log + 0.05*(Cflt) + 1.40 (Eq. 30) PHF = Peak Hour Factor (see Chapter 10 for default values) Where L = Total number of directional through lanes ABSeg = The length weighted average segment bicycle score V = Directional motorized vehicle volume (vph). Exp = The exponential function, where e is the base of nat- (Note: V > 4 *PHF * L) ural logarithms. Fs = Effective speed factor = 1.1199 In(S - 20) + 0.8103 ABInt = Average intersection bicycle score S = Average running speed of motorized vehicles (mph) Cflt = Number of unsignalized conflicts per mile, i.e., the (Note: S >= 21) sum of the number of unsignalized intersections per HV = Proportion of heavy vehicles in motorized vehicle mile and the number of driveways per mile volume. The output of either model is a numerical value, which Note: if the auto volume is < 200 vph, the %HV used must be translated to a LOS letter grade. in this equation must be <= 50% to avoid unrealis- Exhibit 91 provides the numerical ranges that coincide tically poor LOS results for low volume and high with each LOS letter grade. percent HV conditions. The first model provides a better fit with the numerical PC = FHWA's five point pavement surface condition rat- scores given by the video lab participants to the video clips. ing (5=Excellent, 1=Poor) (A default of 3 may be This model was derived based on a statistical fitting process used for good to excellent pavement) to the video clip data. However, this first model does not pre- We = Average effective width of outside through lane (ft) dict LOS A or B for the video clips. Consequently the second = Wv - (10ft %OSP) (ft) ** If W1 < 4 model was developed. = Wv + W1 - 2 (10 %OSP) (ft) ** Otherwise The second model has an inferior numerical fit with the %OSP = Percentage of segment with occupied on-street video lab data (measured in terms of squared error) but pro- parking duces the full range, LOS A through F, for the video clips. The W1 = width of paving between the outside lane stripe and second model was derived from the first model by reducing the the edge of pavement (ft) constant so that the second model would predict LOS A for Wv = Effective width as a function of traffic volume (ft) video clips #328 and #330. The other parameters in the model = Wt (ft) ** If V > 160 vph or street is divided were then manually adjusted until the second model could = Wt*(2 - (0.005 V)) (ft) ** Otherwise produce LOS F for one or more of video clips #314, 317, 323, Wt = Width of outside through lane plus paved shoulder and 324 (which were rated LOS F by the video lab participants). (including bike lane where present) (ft) Both models use the same bicycle segment and bicycle in- Note: parking lane can be counted as shoulder only tersection submodels. if 0% occupied. Bicycle Segment LOS Bicycle Intersection LOS The segment bicycle LOS is calculated according to the The intersection bicycle LOS is calculated according to the following equation: following equation: IntBLOS = -0.2144Wt + 0.0153CD Exhibit 91. Bicycle LOS Numerical + 0.0066 (Vol15/L) + 4.1324 (Eq. 32) Equivalents. Where LOS Numerical Score IntBLOS = perceived hazard of shared-roadway environment A 2.00 through the intersection B >2.00 and 2.75 Wt = total width of outside through lane and bike lane C >2.75 and 3.50 D >3.50 and 4.25 (if present) E >4.25 and 5.00 CD = crossing distance, the width of the side street F > 5.00 (including auxiliary lanes and median)