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225 This appendix documents the default values used in the vari- ous procedures of the scenario generation stage of the urban streets reliability methodology. Weather Event Procedure The weather event procedure is based on the weather charac- teristics identified in the following list. Default values are pro- vided for these characteristics in the software implementation of the reliability methodology (National Climatic Data Center 2011a; 2011b; 2011c). ⢠Total normal precipitation; ⢠Total normal snowfall; ⢠Number of days with precipitation of 0.01 in. or more; and ⢠Precipitation rate. The default data for 284 U.S. cities and territories are provided. Table H.1 illustrates the mean number of days with precipita- tion for 35 of these cities. The values shown represent an aver- age for several years at each city (i.e., minimum of 17 years, maximum of 125 years, average 61 years). Table H.2 illustrates the default total snowfall for 35 cities. The values shown represent an average for several years at each city (i.e., minimum of 11 years, maximum of 142 years, average 59 years). Table H.3 illustrates the default normal daily mean tem- perature for 35 cities. The values shown represent an average for 30 years at each city. Table H.4 illustrates the default nor- mal precipitation data for the same 35 cities. The values shown represent 30-year averages. Table H.5 illustrates the default average precipitation rate for 35 cities. The values shown represent an average for 5 to 10 years at each city. Traffic Demand Variation Procedure This section lists the default values for the hour-of-day, day-of- week, and month-of-year factors provided in the reliability methodology. They are based on research by Hallenbeck et al. (1997). They were found to vary by roadway functional class and by vehicle class. The functional classes considered are iden- tified in the following list. The number associated with each class corresponds to the column headings in Tables H.6 and H.8. ⢠Rural interstate (1); ⢠Rural principal arterial (2); ⢠Rural minor arterial (6); ⢠Rural major collector (7); ⢠Rural minor collector (8); ⢠Urban interstate (11); ⢠Urban other freeway and expressway (12); ⢠Urban principal arterial (14); and ⢠Urban minor arterial (16). The hour-of-day factors are multiplied by an annual average daily traffic (AADT) volume to estimate the annual average hourly volume. The factors for passenger cars are listed in Table H.6. This vehicle class was found to represent 65% to 75% of the traffic stream. The hour-of-day factors for the other vehicle classes show a similar variation. These factors were obtained from the tables in Hallenbeck et al. (1997, pp. 69â82). The day-of-week factors are multiplied by the AADT volume to estimate the annual average daily volume for a given day of week. The factors for passenger cars are listed in Table H.7. These factors were obtained from Table 3 of Hallenbeck et al. (1997). The month-of-year factors are multiplied by the AADT volume to estimate the annual average daily volume for a given month. The factors for four vehicle classes combined A P P E n D i x H Default Factors for the Urban Streets Reliability Methodology (text continues on page 229)
226 Table H.1. Default Mean Number of Days with Precipitation JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC BIRMINGHAM AP, AL 11 10 10 9 9 10 12 9 7 6 8 10 HUNTSVILLE, AL 11 9 11 9 10 9 10 8 8 7 9 10 MOBILE, AL 10 9 9 7 8 11 15 13 9 6 7 9 MONTGOMERY, AL 10 9 9 7 8 9 11 9 7 5 7 9 FLAGSTAFF, AZ 7 7 8 5 4 2 11 12 6 5 5 6 PHOENIX, AZ 3 4 3 1 0 0 4 4 2 2 2 3 TUCSON, AZ 4 3 3 1 1 1 9 9 4 3 2 4 WINSLOW, AZ 4 4 4 3 2 2 6 8 5 3 3 4 YUMA, AZ 3 2 2 1 * * 1 2 1 1 1 2 FORT SMITH, AR 7 7 9 9 10 8 7 6 7 7 7 7 LITTLE ROCK, AR 9 8 10 9 10 8 8 6 7 7 8 9 NORTH LITTLE ROCK, AR 9 9 9 9 11 8 8 6 7 7 8 9 BAKERSFIELD, CA 6 6 6 4 1 0 0 0 1 2 4 5 BISHOP, CA 3 3 2 2 2 1 1 1 1 1 2 2 EUREKA, CA 16 14 15 12 8 5 2 2 4 8 13 15 FRESNO, CA 7 7 6 4 2 0 1 1 1 2 5 7 LONG BEACH, CA 5 5 5 3 1 1 1 1 1 2 3 5 LOS ANGELES AP, CA 6 6 5 3 1 0 0 1 1 2 3 5 LOS ANGELES C.O., CA 6 5 5 3 1 0 1 1 1 2 3 5 MOUNT SHASTA, CA 12 11 12 9 7 5 2 2 3 6 10 12 REDDING, CA 13 11 10 8 6 3 0 0 1 4 8 12 SACRAMENTO, CA 10 9 8 5 3 1 1 1 1 3 7 9 SAN DIEGO, CA 6 6 6 4 1 0 1 0 1 2 4 6 SAN FRANCISCO AP, CA 11 10 9 5 2 1 1 1 1 3 7 10 SAN FRANCISCO C.O., CA 11 10 10 6 3 1 1 1 2 4 8 10 SANTA BARBARA, CA 5 6 6 2 1 0 0 1 1 2 3 5 SANTA MARIA, CA 7 7 7 4 1 0 1 1 1 2 5 7 STOCKTON, CA 9 8 8 5 2 0 1 1 1 3 7 7 ALAMOSA, CO 3 3 5 5 5 5 8 10 6 4 3 4 COLORADO SPRINGS, CO 4 4 7 7 10 9 12 12 6 4 4 4 DENVER, CO 5 5 8 8 10 8 9 8 6 5 5 5 GRAND JUNCTION, CO 6 6 7 6 6 3 4 6 6 5 5 5 PUEBLO, CO 4 4 6 6 8 7 9 8 4 3 3 3 * Missing data. Table H.2. Default Total Snowfall (inches) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC BIRMINGHAM AP, AL 0.6 0.2 0.3 0.1 T T T 0 T T T 0.3 HUNTSVILLE, AL 1.4 0.8 0.4 T T T 0 T 0 T 0 0.2 MOBILE, AL 0.1 0.1 0.1 T T 0 T 0 0 0 T 0.1 MONTGOMERY, AL 0.2 0.1 0.1 0 T 0 0 0 0 T T 0 FLAGSTAFF, AZ 21.2 19.2 20.8 9 1.8 T T T 0.1 2.2 9.6 17 PHOENIX, AZ T 0 T T T 0 0 0 0 T 0 T TUCSON, AZ 0.3 0.2 0.2 0.1 T 0 T T T T 0.1 0.3 WINSLOW, AZ 2.5 1.8 1.9 0.4 0 0 0 0 T 0.2 0.7 3 YUMA, AZ 0 0 0 0 0 0 0 0 0 0 0 T FORT SMITH, AR 2.5 1.8 0.7 T T T 0 0 0 T 0.4 0.8 LITTLE ROCK, AR 2.4 1.6 0.5 T T T 0 0 0 T 0.2 0.6 NORTH LITTLE ROCK, AR 2.5 2.4 0.6 T T T T 0 0 T 0.3 0.5 BAKERSFIELD, CA T T 0 T 0 0 0 0 0 0 0 T BISHOP, CA 4 1.5 0.7 0.3 0.1 0 0 0 T 0 0.3 1.3 EUREKA, CA 0.1 0.1 T T 0 0 0 0 0 0 0 T FRESNO, CA 0.1 T T 0 0 T 0 0 0 T 0 0 LONG BEACH, CA T T 0 0 0 0 0 0 0 0 0 0 LOS ANGELES AP, CA T T T 0 0 0 0 0 0 0 0 T LOS ANGELES C.O., CA 0 T 0 0 0 0 0 0 0 0 0 T MOUNT SHASTA, CA 29.9 16.9 17.1 8.9 0.8 T 0 0 0 0.4 9 21.9 REDDING, CA 1.5 0.3 0.2 T 0.2 T 0 T 0 0 T 2 SACRAMENTO, CA T T T 0 T 0 0 0 0 0 0 T SAN DIEGO, CA T 0 T T 0 0 0 0 0 0 T T SAN FRANCISCO AP, CA 0 T T 0 0 0 0 0 0 0 0 0 SAN FRANCISCO C.O., CA T T T 0 0 0 0 0 0 0 0 T SANTA BARBARA, CA 0 0 0 0 0 0 0 0 0 0 0 0 SANTA MARIA, CA T T T 0 0 0 0 0 0 0 T T STOCKTON, CA 0 0 T T T 0 0 0 0 0 0 0 ALAMOSA, CO 4.5 4.2 5.5 4.4 1.8 0 T 0 0.2 3 4 5.2 COLORADO SPRINGS, CO 5.2 4.7 8.9 6.3 1.5 T T T 1 3.4 4.9 5.2 DENVER, CO 7.9 7.4 12.2 8.5 1.6 0 T T 1.6 4 8.7 7.8 GRAND JUNCTION, CO 6.6 3.8 3.4 1.2 0.1 T T T 0.1 0.5 2.8 5.1 PUEBLO, CO 5.8 4.2 6.7 3.7 0.6 T T T 0.6 1.4 4.1 5.3 Note: T = trace.
227 Table H.3. Default Normal Daily Mean Temperature (î¸F) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC BIRMINGHAM AP, AL 42.6 46.8 54.5 61.3 69.3 76.4 80.2 79.6 73.8 62.9 53.1 45.6 HUNTSVILLE, AL 39.8 44.3 52.3 60.4 68.6 76 79.5 78.6 72.4 61.3 51.2 43.1 MOBILE, AL 50.1 53.5 60.2 66.1 73.5 79.3 81.5 81.3 77.2 67.7 58.9 52.3 MONTGOMERY, AL 46.6 50.5 57.9 64.3 72.3 78.9 81.8 81.2 76.3 65.4 56.1 49 FLAGSTAFF, AZ 29.7 32.2 36.6 42.9 50.8 60.1 66.1 64.4 57.8 47.1 36.5 30.2 PHOENIX, AZ 56.1 59.9 64.6 71.2 80.7 89.8 94.8 93.1 87.3 74.9 62.7 55.5 TUCSON, AZ 51.7 55 59.2 66 74.5 84.1 86.5 84.9 80.9 70.5 58.7 51.9 WINSLOW, AZ 34.2 40 46.3 53.4 62.2 72.1 77.5 75.6 68.2 55.9 43.2 34.1 YUMA, AZ 58.1 62 66.5 72.7 79.9 88.8 94.1 93.5 88.2 77.2 64.8 57.4 FORT SMITH, AR 38 43.7 52.6 61.1 69.5 77.5 82.2 81.5 73.9 62.8 50.5 41 LITTLE ROCK, AR 40.1 45.2 53.4 61.4 70.1 78.4 82.4 81.3 74.4 63.3 51.7 43.2 NORTH LITTLE ROCK, AR 40.2 45.6 54.3 63 70.9 78.8 83.2 82.1 75 64.5 52.5 43.4 BAKERSFIELD, CA 47.8 53.3 57.3 62.7 70.3 77.7 83.1 81.9 76.7 67.2 54.8 47.2 BISHOP, CA 38 42.4 47.7 54.1 62.5 71.1 76.8 74.8 67.3 56.6 44.8 38 EUREKA, CA 47.9 48.9 49.2 50.7 53.6 56.3 58.1 58.7 57.4 54.5 51 47.9 FRESNO, CA 46 51.4 55.5 61.2 68.8 76.1 81.4 79.9 74.6 65 52.7 45.2 LONG BEACH, CA 57 58.3 59.7 63 65.9 69.8 73.8 75.1 73.4 68.6 61.8 57.1 LOS ANGELES AP, CA 57.1 58 58.3 60.8 63.1 66.4 69.3 70.7 70.1 66.9 61.6 57.6 LOS ANGELES C.O., CA 58.3 60 60.7 63.8 66.2 70.5 74.2 75.2 74 69.5 62.9 58.5 MOUNT SHASTA, CA 35.3 38.2 41.2 46.3 53.2 60.2 66.1 65.1 59.5 50.5 39.9 34.8 REDDING, CA 45.5 49.1 52.5 57.8 66.2 75.2 81.3 78.9 73.4 63.2 51.1 45.3 SACRAMENTO, CA 46.3 51.2 54.5 58.9 65.5 71.5 75.4 74.8 71.7 64.4 53.3 45.8 SAN DIEGO, CA 57.8 58.9 60 62.6 64.6 67.4 70.9 72.5 71.6 67.6 61.8 57.6 SAN FRANCISCO AP, CA 49.4 52.4 54 56.2 58.7 61.4 62.8 63.6 63.9 61 54.7 49.5 SAN FRANCISCO C.O., CA 52.3 55 55.9 57.3 58.4 60.5 61.3 62.4 63.7 62.5 57.5 52.7 SANTA BARBARA, CA 53.1 55.2 56.7 58.9 60.9 64.2 67 68.6 67.4 63.5 57.5 53.2 SANTA MARIA, CA 51.6 53.1 53.8 55.5 57.8 60.9 63.5 64.2 63.9 61.1 55.5 51.6 STOCKTON, CA 46 51.1 54.9 60 66.7 73.2 77.3 76.5 72.8 64.6 53.1 45.3 ALAMOSA, CO 14.7 22.5 32.7 40.8 50.4 59.4 64.1 62.1 54.5 42.8 28.4 17.1 COLORADO SPRINGS, CO 28.1 31.7 37.8 45.3 54.6 64.4 69.6 67.6 59.8 48.9 36.2 29 DENVER, CO 29.2 33.2 39.6 47.6 57.2 67.6 73.4 71.7 62.4 51 37.5 30.3 GRAND JUNCTION, CO 26.1 34.1 43.4 50.9 60.5 71.1 76.8 74.7 65.4 52.7 38.1 28.2 PUEBLO, CO 29.3 34.6 41.8 49.9 59.7 69.8 75.4 73.5 64.8 52.4 38.4 30.3 Table H.4. Default Normal Precipitation (inches) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC BIRMINGHAM AP, AL 5.45 4.21 6.1 4.67 4.83 3.78 5.09 3.48 4.05 3.23 4.63 4.47 HUNTSVILLE, AL 5.52 4.95 6.68 4.54 5.24 4.22 4.4 3.32 4.29 3.54 5.22 5.59 MOBILE, AL 5.75 5.1 7.2 5.06 6.1 5.01 6.54 6.2 6.01 3.25 5.41 4.66 MONTGOMERY, AL 5.04 5.45 6.39 4.38 4.14 4.13 5.31 3.63 4.22 2.58 4.53 4.97 FLAGSTAFF, AZ 2.18 2.56 2.62 1.29 0.8 0.43 2.4 2.89 2.12 1.93 1.86 1.83 PHOENIX, AZ 0.83 0.77 1.07 0.25 0.16 0.09 0.99 0.94 0.75 0.79 0.73 0.92 TUCSON, AZ 0.99 0.88 0.81 0.28 0.24 0.24 2.07 2.3 1.45 1.21 0.67 1.03 WINSLOW, AZ 0.46 0.53 0.61 0.27 0.36 0.3 1.18 1.31 1.02 0.9 0.55 0.54 YUMA, AZ 0.38 0.28 0.27 0.09 0.05 0.02 0.23 0.61 0.26 0.26 0.14 0.42 FORT SMITH, AR 2.37 2.59 3.94 3.91 5.29 4.28 3.19 2.56 3.61 3.94 4.8 3.39 LITTLE ROCK, AR 3.61 3.33 4.88 5.47 5.05 3.95 3.31 2.93 3.71 4.25 5.73 4.71 NORTH LITTLE ROCK, AR 3.37 3.27 4.88 5.03 5.4 3.51 3.15 2.97 3.53 3.81 5.74 4.53 BAKERSFIELD, CA 1.18 1.21 1.41 0.45 0.24 0.12 0 0.08 0.15 0.3 0.59 0.76 BISHOP, CA 0.88 0.97 0.62 0.24 0.26 0.21 0.17 0.13 0.28 0.2 0.44 0.62 EUREKA, CA 5.97 5.51 5.55 2.91 1.62 0.65 0.16 0.38 0.86 2.36 5.78 6.35 FRESNO, CA 2.16 2.12 2.2 0.76 0.39 0.23 0.01 0.01 0.26 0.65 1.1 1.34 LONG BEACH, CA 2.95 3.01 2.43 0.6 0.23 0.08 0.02 0.1 0.24 0.4 1.12 1.76 LOS ANGELES AP, CA 2.98 3.11 2.4 0.63 0.24 0.08 0.03 0.14 0.26 0.36 1.13 1.79 LOS ANGELES C.O., CA 3.33 3.68 3.14 0.83 0.31 0.06 0.01 0.13 0.32 0.37 1.05 1.91 MOUNT SHASTA, CA 7.06 6.45 5.81 2.65 1.87 0.99 0.39 0.43 0.87 2.21 5.08 5.35 REDDING, CA 6.5 5.49 5.15 2.4 1.66 0.69 0.05 0.22 0.48 2.18 4.03 4.67 SACRAMENTO, CA 3.84 3.54 2.8 1.02 0.53 0.2 0.05 0.06 0.36 0.89 2.19 2.45 SAN DIEGO, CA 2.28 2.04 2.26 0.75 0.2 0.09 0.03 0.09 0.21 0.44 1.07 1.31 SAN FRANCISCO AP, CA 4.45 4.01 3.26 1.18 0.38 0.11 0.03 0.07 0.2 1.04 2.49 2.89 SAN FRANCISCO C.O., CA 4.72 4.15 3.4 1.25 0.54 0.13 0.04 0.09 0.28 1.19 3.31 3.18 SANTA BARBARA, CA 3.57 4.28 3.51 0.63 0.23 0.05 0.03 0.11 0.42 0.52 1.32 2.26 SANTA MARIA, CA 2.64 3.23 2.94 0.91 0.32 0.05 0.03 0.05 0.31 0.45 1.24 1.84 STOCKTON, CA 2.71 2.46 2.28 0.96 0.5 0.09 0.05 0.05 0.33 0.82 1.77 1.82 ALAMOSA, CO 0.25 0.21 0.46 0.54 0.7 0.59 0.94 1.19 0.89 0.67 0.48 0.33 COLORADO SPRINGS, CO 0.28 0.35 1.06 1.62 2.39 2.34 2.85 3.48 1.23 0.86 0.52 0.42 DENVER, CO 0.51 0.49 1.28 1.93 2.32 1.56 2.16 1.82 1.14 0.99 0.98 0.63 GRAND JUNCTION, CO 0.6 0.5 1 0.86 0.98 0.41 0.66 0.84 0.91 1 0.71 0.52 PUEBLO, CO 0.33 0.26 0.97 1.25 1.49 1.33 2.04 2.27 0.84 0.64 0.58 0.39
228 Table H.5. Default Average Precipitation Rate (inches/hour) LOCATION YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC BIRMINGHAM AP, AL 1999 0.099 0.1 0.116 0.113 0.165 0.151 0.161 0.185 0.151 0.135 0.105 0.096 HUNTSVILLE, AL 1999 0.071 0.077 0.094 0.092 0.157 0.143 0.196 0.153 0.134 0.092 0.08 0.087 MOBILE, AL 1999 0.116 0.098 0.133 0.148 0.108 0.185 0.167 0.199 0.148 0.112 0.154 0.129 MONTGOMERY, AL 1999 0.105 0.117 0.141 0.132 0.158 0.171 0.211 0.17 0.143 0.12 0.091 0.085 FLAGSTAFF, AZ 1999 0.028 0.038 0.04 0.047 0.038 0.056 0.078 0.091 0.083 0.052 0.045 0.038 PHOENIX, AZ 1999 0.053 0.043 0.044 0.024 0.06 0.11 0.16 0.13 0.121 0.095 0.086 0.045 TUCSON, AZ 1999 0.036 0.044 0.033 0.034 0.057 0.127 0.087 0.158 0.165 0.074 0.045 0.041 WINSLOW, AZ 1999 0.033 0.029 0.04 0.065 0.05 0.133 0.144 0.08 0.058 0.053 0.049 0.019 YUMA, AZ 1996 0.048 0.069 0.037 0.033 0.067 0.025 0.394 0.159 0.12 0.07 0.057 0.039 FORT SMITH, AR 1999 0.054 0.079 0.081 0.115 0.107 0.18 0.172 0.131 0.096 0.099 0.116 0.087 LITTLE ROCK, AR 1999 0.114 0.115 0.111 0.143 0.132 0.178 0.158 0.264 0.115 0.218 0.152 0.12 NORTH LITTLE ROCK, AR 1999 0.074 0.069 0.1 0.102 0.113 0.147 0.211 0.184 0.082 0.1 0.091 0.072 BAKERSFIELD, CA 1999 0.046 0.042 0.051 0.04 0.033 0.075 0.048 0.048 0.021 0.059 0.048 0.032 BISHOP, CA 1999 0.06 0.045 0.034 0.038 0.044 0.048 0.075 0.125 0.014 0.052 0.032 0.046 EUREKA, CA 1999 0.043 0.038 0.043 0.036 0.038 0.035 0.025 0.036 0.062 0.046 0.048 0.047 FRESNO, CA 1999 0.043 0.044 0.041 0.037 0.11 0.138 0.22 0.156 0.092 0.049 0.061 0.046 LONG BEACH, CA 1999 0.054 0.057 0.059 0.068 0.073 0.075 0.014 0.0325 0.051 0.046 0.051 0.076 LOS ANGELES AP, CA 1999 0.054 0.06 0.067 0.059 0.068 0.087 0.029 0.0305 0.032 0.09 0.086 0.08 LOS ANGELES C.O., CA 1999 0.064 0.074 0.066 0.072 0.05 0.075 0.022 0.0365 0.051 0.038 0.06 0.076 MOUNT SHASTA, CA 1999 0.076 0.08 0.084 0.085 0.078 0.114 0.122 0.115 0.116 0.083 0.082 0.082 REDDING, CA 1999 0.078 0.107 0.107 0.08 0.079 0.112 0.072 0.14 0.19 0.09 0.067 0.079 SACRAMENTO, CA 1999 0.045 0.055 0.043 0.04 0.04 0.081 0.064 0.047 0.042 0.073 0.05 0.051 SAN DIEGO, CA 1999 0.052 0.052 0.055 0.043 0.032 0.048 0.119 0.0895 0.06 0.07 0.056 0.058 SAN FRANCISCO AP, CA 1999 0.048 0.049 0.032 0.038 0.041 0.022 0.041 0.06 0.03 0.083 0.037 0.043 SAN FRANCISCO C.O., CA 1999 0.045 0.045 0.036 0.043 0.04 0.026 0.0365 0.047 0.059 0.098 0.049 0.051 SANTA BARBARA, CA 1999 0.079 0.12 0.113 0.097 0.07 0.1 0.067 0.1 0.1 0.124 0.115 0.087 SANTA MARIA, CA 1999 0.041 0.048 0.063 0.036 0.031 0.076 0.023 0.0465 0.07 0.057 0.062 0.044 STOCKTON, CA 1999 0.047 0.049 0.041 0.031 0.063 0.029 0.053 0.053 0.077 0.05 0.061 0.031 ALAMOSA, CO 1999 0.017 0.034 0.026 0.034 0.076 0.058 0.094 0.084 0.045 0.034 0.027 0.035 COLORADO SPRINGS, CO 1999 0.029 0.038 0.032 0.044 0.053 0.109 0.126 0.103 0.058 0.037 0.037 0.046 DENVER, CO 1999 0.044 0.041 0.058 0.057 0.063 0.115 0.192 0.125 0.076 0.072 0.052 0.058 GRAND JUNCTION, CO 1999 0.025 0.022 0.031 0.037 0.041 0.042 0.088 0.061 0.054 0.037 0.033 0.023 PUEBLO, CO 1999 0.02 0.023 0.037 0.05 0.078 0.098 0.103 0.117 0.147 0.033 0.033 0.038 Table H.6. Default Hour-of-Day Factors for Passenger Cars Functional Class Category Hour 1 2 6 7 8 11 12 14 16 Weekday 0 0.011 0.009 0.012 0.012 0.008 0.011 0.010 0.010 0.010 1 0.008 0.005 0.006 0.005 0.004 0.006 0.006 0.006 0.006 2 0.006 0.003 0.003 0.004 0.001 0.005 0.004 0.005 0.004 3 0.006 0.003 0.003 0.003 0.001 0.004 0.004 0.005 0.002 4 0.008 0.006 0.005 0.004 0.001 0.007 0.007 0.009 0.002 5 0.015 0.019 0.018 0.010 0.006 0.022 0.025 0.030 0.007 6 0.034 0.048 0.045 0.028 0.020 0.051 0.058 0.054 0.023 7 0.056 0.072 0.072 0.063 0.034 0.069 0.077 0.071 0.067 8 0.054 0.059 0.060 0.060 0.035 0.055 0.053 0.058 0.066 9 0.053 0.050 0.044 0.049 0.037 0.046 0.037 0.047 0.054 10 0.056 0.051 0.044 0.048 0.047 0.046 0.037 0.046 0.051 11 0.058 0.053 0.046 0.050 0.056 0.049 0.042 0.050 0.056 12 0.060 0.054 0.049 0.052 0.055 0.052 0.045 0.053 0.071 13 0.062 0.057 0.050 0.055 0.056 0.053 0.045 0.054 0.066 14 0.067 0.064 0.056 0.062 0.061 0.060 0.057 0.063 0.060 15 0.074 0.075 0.080 0.071 0.071 0.070 0.073 0.069 0.062 16 0.080 0.083 0.088 0.083 0.080 0.077 0.087 0.072 0.063 17 0.077 0.083 0.089 0.093 0.107 0.081 0.090 0.077 0.075 18 0.060 0.062 0.059 0.072 0.085 0.065 0.068 0.062 0.070 19 0.045 0.043 0.048 0.049 0.066 0.049 0.049 0.044 0.053 20 0.036 0.034 0.036 0.040 0.060 0.039 0.040 0.035 0.044 21 0.031 0.030 0.031 0.039 0.049 0.036 0.037 0.033 0.035 22 0.024 0.023 0.026 0.028 0.038 0.028 0.029 0.026 0.033 23 0.018 0.016 0.020 0.019 0.020 0.020 0.019 0.021 0.019 (continued on next page)
229 Table H.7. Default Day-of-Week Factors for Passenger Cars Number Urban Rural 1 Sunday 0.87 1.01 2 Monday 0.98 0.95 3 Tuesday 0.98 0.91 4 Wednesday 1.00 0.93 5 Thursday 1.03 0.98 6 Friday 1.15 1.16 7 Saturday 0.99 1.05 Day of Week Weekend 0 0.016 0.016 0.024 0.022 0.016 0.021 0.023 0.023 0.028 1 0.011 0.010 0.013 0.012 0.009 0.014 0.015 0.014 0.023 2 0.009 0.006 0.007 0.007 0.004 0.010 0.008 0.010 0.021 3 0.007 0.005 0.005 0.005 0.002 0.006 0.005 0.006 0.008 4 0.007 0.005 0.005 0.004 0.002 0.006 0.005 0.006 0.005 5 0.010 0.009 0.008 0.006 0.002 0.010 0.009 0.010 0.005 6 0.017 0.016 0.016 0.010 0.007 0.017 0.016 0.017 0.011 7 0.027 0.025 0.025 0.018 0.014 0.025 0.023 0.024 0.018 8 0.039 0.036 0.033 0.028 0.025 0.035 0.036 0.035 0.030 9 0.052 0.050 0.045 0.039 0.046 0.047 0.045 0.046 0.048 10 0.063 0.062 0.055 0.049 0.058 0.057 0.057 0.056 0.054 11 0.070 0.070 0.063 0.060 0.063 0.065 0.066 0.054 0.057 12 0.072 0.075 0.072 0.068 0.086 0.072 0.076 0.071 0.074 13 0.072 0.075 0.073 0.071 0.081 0.072 0.073 0.071 0.071 14 0.073 0.075 0.072 0.073 0.077 0.071 0.074 0.072 0.069 15 0.075 0.077 0.082 0.074 0.077 0.073 0.075 0.073 0.067 16 0.075 0.078 0.075 0.079 0.086 0.073 0.075 0.073 0.071 17 0.071 0.075 0.074 0.080 0.086 0.072 0.071 0.073 0.068 18 0.061 0.065 0.067 0.075 0.067 0.064 0.063 0.063 0.067 19 0.051 0.051 0.054 0.063 0.056 0.052 0.051 0.052 0.056 20 0.041 0.041 0.043 0.052 0.051 0.043 0.043 0.044 0.049 21 0.033 0.033 0.035 0.045 0.039 0.038 0.037 0.038 0.040 22 0.026 0.026 0.029 0.034 0.032 0.032 0.032 0.033 0.035 23 0.019 0.019 0.024 0.025 0.019 0.025 0.023 0.026 0.024 Functional Class Category Hour 1 2 6 7 8 11 12 14 16 Table H.6. Default Hour-of-Day Factors for Passenger Cars (continued) are listed in Table H.8. The vehicle classes include motor- cycles; passenger cars; other two-axle, four-tire, single-unit vehicles; and two-axle, six-tire, single-unit trucks. Collec- tively, these classes represent about 90% of the traffic stream. These factors were obtained from tables by Hallenbeck et al. (1997, pp. 65â68). The report did not provide these factors for rural minor collectors, so the factors for rural major col- lectors are substituted in the exhibit. Traffic incident Procedure This section lists the default values for the distribution of incidents by the categories identified in the following list. The last three categories define the incident type. ⢠Weather condition 44 No precipitation and dry pavement 44 Rainfall 44 Wet pavement but not raining 44 Snowfall 44 Snow or ice on pavement but not snowing; ⢠Street location 44 Segment 44 Signalized intersection; ⢠Event type 44 Crash 44 Noncrash; ⢠Lane location 44 One lane 44 Two or more lanes 44 Shoulder; and ⢠Severity 44 Property-damage-only crash 44 Fatal or injury crash 44 Breakdown 44 Other. A review of the literature indicated that most examinations of incident data focus on freeways, and few consider urban streets. Also, very few of these examinations separately quan- tify incidents by weather condition. No publications were identified that separately addressed incident duration for street segments and for signalized intersections. Weather Conditions Data for incidents on highways in New York were examined by List et al. (2008, Table 6.17, p. 3.1-38). A total of 1,083
230 incidents were identified for which weather conditions were reported. The distribution of incident type by weather condi- tion indicated that, for any given incident type, the propor- tion varied less than 0.01 among weather conditions. For example, the proportion of property-damage-only crashes decreased from 0.42 for no precipitation and dry pavement to 0.41 for wet pavement, and it increased to 0.43 when snow or ice was on the pavement. This pattern was also noted by Andrey et al. (2001) following their review of weather-related safety research. Although the trends noted in the previous paragraph are plausible, they are not based on data for urban streets, and the effect appears to be very small. Therefore, no adjustment is made to the default incident type distribution based on weather. Additional research is needed to determine the severity distribution for weather conditions. The literature review identified two research publications that quantified the effect of weather condition on incident duration. Garib et al. (1997) examined the duration of 277 incidents occurring on I-880 in Oakland, California. They found that incident duration during rainfall was reduced by 21% relative to incidents occurring without rainfall. The data assembled by List et al. (2008, Table 34, p. 3.2-36) were examined with regard to the influence of weather on incident duration. This examination indicated that incident duration was reduced by about 18% relative to clear or cloudy conditions when the pavement was wet but there was no precipitation. When there was precipitation, incident duration was reduced by 20% (although heavy rain was noted to increase duration). In contrast, the presence of snow or ice on the pavement tended to increase incident duration by 36%. The findings associated with incident duration indicated a significant effect of weather condition. The percentages attrib- uted to List et al. were used to derive the default durations by weather condition. Default Values Table H.9 shows the default incident type distribution and duration values for conditions with no precipitation and dry pavement. The same distribution values were used for all weather conditions and street locations. Similarly, the same duration values were used for all street locations. This approach was taken because no information could be found regarding the possible variation of these values by street loca- tion. Specifically, documented evidence regarding the varia- tion of incident frequency or duration by street location could not be found in the literature or in the available agency incident records. The proportions shown in Table H.9 are based on incident data collected by the SHRP 2 Project L08 research team. These data were obtained from incident logs for five arterial streets in California totaling 86.5 mi. A total of 2,081 incidents are in the database used to derive the proportions shown. The proportions shown for lane location indicate that many incidents occur on the shoulders of the streets included in the data assembled by the SHRP 2 L08 researchers. The proportion of the streets in these data that have shoulders is unknown. Given that many urban streets do not have shoul- ders, the extent to which shoulder presence influenced the lane location distribution shown in Table H.9 is unclear. When shoulders are not present, the proportions allocated to the shoulder category should be added to those for the one-lane category to estimate the likely distribution. Ide- ally, additional research would be conducted to separately develop the lane location distribution for streets with and without shoulders. The default joint proportion for each incident shown in Column 8 is used in the reliability methodology. The aver- age incident duration shown in the far-right column is also used in the reliability methodology. It is equal to the sum of the incident detection time, response time, and clearance time. Research by Raub and Schofer (1997) indicates that Number Month 1 2 6 7 8 11 12 14 16 1 January 0.747 0.813 0.834 0.812 0.812 0.836 0.802 0.831 0.881 2 February 0.828 0.855 0.935 0.935 0.935 0.863 0.874 1.021 0.944 3 March 0.926 0.891 0.973 0.977 0.977 0.936 0.936 1.030 1.016 4 April 0.994 0.958 1.004 1.044 1.044 0.992 0.958 0.987 0.844 5 May 1.087 1.091 1.091 1.009 1.009 0.990 1.026 1.012 1.025 6 June 1.105 1.087 1.106 1.041 1.041 1.039 1.068 1.050 1.060 7 July 1.243 1.125 1.016 0.982 0.982 1.152 1.107 0.991 1.150 8 August 1.137 1.130 1.015 1.056 1.056 1.050 1.142 1.054 1.110 9 September 1.087 1.038 1.062 1.054 1.054 1.081 1.088 1.091 1.081 10 October 0.996 1.041 1.080 1.028 1.028 1.012 1.069 0.952 1.036 11 November 0.974 0.965 0.983 1.007 1.007 1.012 0.962 0.992 0.989 12 December 0.872 0.910 0.966 0.998 0.998 0.995 0.933 0.938 0.903 Functional Class Table H.8. Default Month-of-Year Factors for Four Vehicle Classes Combineda a Motorcycles, passenger cars, other two-axle four-tire single-unit vehicles, and two-axle, six-tire single-unit trucks.
231 the detection time varies from 1 to 2 min. A default value of 2.0 min is used in the reliability methodology described in this paper. The average response times listed in Table H.9 are shown to be 15 min for all incident times. It is likely that this time will vary among jurisdictions and facilities, depending on the priority placed on street system management and the con- nectivity of the street system. Dowling et al. (2004) indicate this time can vary from 5 to 30 min for freeways, with the shorter time likely when freeway service patrols are used. A default value of 15 min is used for all weather conditions, except when snow is on the pavement. When there is snow- fall, or snow or ice on the pavement, this value is increased 36% (20.4 min) based on the analysis discussed in the section above titled âWeather Conditions.â Additional research is needed to quantify this time by incident type and street location. The average clearance times shown in Column 10 of Table H.9 are based on times reported by Raub and Schofer (1997). They are based on an evaluation of 1,497 incidents on urban streets in Illinois. The durations reported by Raub and Schofer equal the sum of the response time and clearance time. A response time of 15 min was subtracted from the reported durations to obtain the clearance times shown in the exhibit. The times reported for disabled and fire were com- bined to obtain the values shown for breakdown. The clearance times shown in Table H.9 are consistent with those found by the SHRP 2 L08 researchers in their examina- tion of clearance times for several arterial streets in California and Oregon. One exception to this consistency is with the noncrash incidents in California. The clearance times for noncrash incidents in California are as long, or longer, than those for crash-related incidents. It is believed that these longer clearance times reflect the occasional occurrence of road closure by landslide, which is not representative of most streets in the United States. The times shown in Column 10 of Table H.9 are adjusted for weather conditions based on the analysis discussed above in âWeather Conditions.â Specifically, for rainfall conditions, the default clearance time is reduced such that the combined response time and clearance time is decreased by 20%. When there is wet pavement but no rainfall, the default clearance time is reduced such that the combined response time and clearance time is decreased by 18%. When there is snowfall or snow or ice on the pavement (but no snowfall), the default clearance time is increased by 36%. References Andrey, J., B. Mills, and J. Vandermolen. Weather Information and Road Safety. Department of Geography, University of Waterloo, Waterloo, Ontario, Canada, 2001. Table H.9. Default Incident Values for No Precipitation and Dry Pavement Response Clearance Total Street Event Lane Joint Time Time Time Location Type Proportion Location Proportion Severity Proportion Proportion (min) (min) (min) Segment Crash 0.358 One lane 0.335 Fatal or Inj 0.304 0.036 15.0 56.4 73.4 0.358 0.335 PDO 0.696 0.083 15.0 39.5 56.5 0.358 2+ lanes 0.163 Fatal or Inj 0.478 0.028 15.0 56.4 73.4 0.358 0.163 PDO 0.522 0.030 15.0 39.5 56.5 0.358 Shoulder 0.502 Fatal or Inj 0.111 0.020 15.0 56.4 73.4 0.358 0.502 PDO 0.889 0.160 15.0 39.5 56.5 Non-crash 0.642 One lane 0.849 Breakdown 0.836 0.456 15.0 10.8 27.8 0.642 0.849 Other 0.164 0.089 15.0 6.7 23.7 0.642 2+ lanes 0.119 Breakdown 0.773 0.059 15.0 10.8 27.8 0.642 0.119 Other 0.227 0.017 15.0 6.7 23.7 0.642 Shoulder 0.032 Breakdown 0.667 0.014 15.0 10.8 27.8 0.642 0.032 Other 0.333 0.007 15.0 6.7 23.7 Total: 1.000 Inter- Crash 0.310 One lane 0.314 Fatal or Inj 0.378 0.037 15.0 56.4 73.4 section 0.310 0.314 PDO 0.622 0.061 15.0 39.5 56.5 0.310 2+ lanes 0.144 Fatal or Inj 0.412 0.018 15.0 56.4 73.4 0.310 0.144 PDO 0.588 0.026 15.0 39.5 56.5 0.310 Shoulder 0.542 Fatal or Inj 0.109 0.018 15.0 56.4 73.4 0.310 0.542 PDO 0.891 0.150 15.0 39.5 56.5 Non-crash 0.690 One lane 0.829 Breakdown 0.849 0.486 15.0 10.8 27.8 0.690 0.829 Other 0.151 0.086 15.0 6.7 23.7 0.690 2+ lanes 0.141 Breakdown 0.865 0.084 15.0 10.8 27.8 0.690 0.141 Other 0.135 0.013 15.0 6.7 23.7 0.690 Shoulder 0.030 Breakdown 0.875 0.018 15.0 10.8 27.8 0.690 0.030 Other 0.125 0.003 15.0 6.7 23.7 Total: 1.000
232 Dowling, R., A. Skabardonis, M. Carroll, and Z. Wang. Methodology for Measuring Recurrent and Nonrecurrent Traffic Congestion. In Transportation Research Record: Journal of the Transportation Research Board, No. 1867, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 60â68. Garib, A., A. Radwan, and H. Al-Deek. Estimating Magnitude and Duration of Incident Delays. Journal of Transportation Engineering, Vol. 123, No. 6, 1997, pp. 459â466. Hallenbeck, M., M. Rice, B. Smith, C. Cornell-Martinez, and J. Wilkinson. Vehicle Volume Distributions by Classification. Report No. FHWA- PL-97-025. Chaparral Systems Corporation, Santa Fe, N.M., 1997. List, G., J. Falcocchio, K. Ozbay, and K. Mouskos. Quantifying Non- Recurring Delay on New York Cityâs Arterial Highways. University of Transportation Research Center, City College of New York, New York, 2008. National Climatic Data Center. Comparative Climatic Data for the United States Through 2010. National Oceanic and Atmospheric Administration, Asheville, N.C. http://www.ncdc.noaa.gov. Accessed Sept. 21, 2011(a). National Climatic Data Center. Global Summary of the Day. National Oceanic and Atmospheric Administration, Asheville, N.C. http:// www7.ncdc.noaa.gov/CDO/cdoselect.cmd?datasetabbv=GSOD. Accessed Sept. 21, 2011(b). National Climatic Data Center. Rainfall Frequency Atlas of the U.S.: Rainfall Event Statistics. National Oceanic and Atmospheric Admin- istration, Asheville, N.C. http://www.ncdc.noaa.gov/oa/document library/rainfall.html. Accessed Sept. 21, 2011(c). Raub, R., and J. Schofer. Managing Incidents on Urban Arterial Road- ways. In Transportation Research Record 1603, TRB, National Research Council, Washington, D.C., 1997, pp. 12â19.
