Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies (2012) / Chapter Skim
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124 C h a p t e r 7 potential Model Forms Background The primary goal of the statistical analysis was to produce a highly practical set of relationships that could be used to predict reliability, especially within the contexts of existing technical applications such as travel demand forecasting models and simulation models. The Phase 1 report proposed two model forms to be investigated: (a)
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125 Notes: 1) " " means "...is a function of..." 2)
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126 slice under study, and capacity is physical (HCM) capacity.
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127 Eight data points on reliability were obtained. A data point consisted of mean, standard deviation, and 95th percentile travel time measurements for each direction of travel on each segment for each peak period.
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128 examine both link-level and section-level predictive models using more complete data sets. Final Link-Level Reliability Predictive Models Data from 164 detector locations on the Atlanta study sections were analyzed.
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129 good measure of how accurately a model predicts the response, and is the most important criterion for fit if the main purpose of the model is prediction, which is the aim here. The predictive equations are 95 16 3 th percentile TTI mean TTI RMSE 1.6954 = = .
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130 80 7 4 th percentile TTI mean TTI RMSE 1.3162 = = . %; alpha level of coefficient <( )
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131 Table 7.4. Recurring, Nonrecurring, and Total TTIs for Seattle Study Sections During Peak Periods Section Time of Peak Congestion Type TTI Mean Standard Deviation I-405 Bellevue northbound a.m.
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132 I-405 North southbound a.m. Nonrecurring 3.534 1.879 Recurring 2.254 1.320 Total 2.820 1.320 I-405 North southbound p.m.
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133 I-5 Lynnwood southbound a.m. Nonrecurring 2.238 1.151 Recurring 1.572 0.641 Total 1.898 0.641 I-5 Lynnwood southbound p.m.
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134 I-5 Seattle North southbound a.m. Nonrecurring 2.721 1.611 Recurring 1.484 0.812 Total 2.157 0.812 I-5 Seattle North southbound p.m.
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135 I-90 Bellevue westbound a.m. Nonrecurring 1.570 0.601 Recurring 1.216 0.241 Total 1.307 0.241 I-90 Bellevue westbound p.m.
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136 I-90 Seattle westbound a.m. Nonrecurring 1.423 0.527 Recurring 1.095 0.288 Total 1.210 0.288 I-90 Seattle westbound p.m.
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137 SR 520 Redmond westbound a.m. Nonrecurring 1.088 0.271 Recurring 1.022 0.119 Total 1.037 0.119 SR 520 Redmond westbound p.m.
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138 95 15 7 th percentile TTI mean TTI RMSE 1.8834 = = . %; alpha level of coefficient <( )
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139 where PctTripsOnTime30mph is the percentage of trips that occur at space mean speeds above the threshold of 30 mph. standard deviation mean TTI= −( )
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140 six Orlando study sections. Unlike urban freeways, on the signalized arterials there was no apparent relationship between mean TTI and the on-time reliability metrics.
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141 10th percentile TTI mean TTI RMSE 0.2689 = = 4 0. %; alpha level of coefficient <( )
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142 was found to be 1.0, which is to be expected under routinely uncongested conditions. It also is worth noting that for these rural sections, mean TTI ranged from 1.025 to 1.045, extremely low values compared with the urban sections studied.
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143 • The above discussion points out an issue with statistical modeling of reliability. Rare events that cause extreme disruptions are difficult to relate to the percentiles of an annual travel time distribution; the more common occurrences (e.g., bottleneck congestion, incidents, rainfall)
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144 80th percentile TTI dccrit= +e 0 13992 0 01118.
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145 50th percentile TTI dcaverage = ( )
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146 The lengths of the time periods differ: the peak hour is 1 hour long, the midday period is 3 hours long (11:00 a.m.
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147 Station ID Hourly Volume 99th Percentile 30th Highest Ratio 200511 8,558 7,756 0.91 200512 6,115 5,698 0.93 200516 8,067 7,697 0.95 200517 8,095 7,600 0.94 200520 2,524 2,986 1.18 750502 11,931 11,278 0.95 750503 14,848 14,385 0.97 750505 11,377 11,631 1.02 750506 11,210 11,612 1.04 750508 12,119 11,987 0.99 750509 11,795 11,955 1.01 750510 8,939 9,542 1.07 750511 9,325 10,020 1.07 750512 8,613 8,907 1.03 750513 9,298 9,435 1.01 750515 8,446 8,730 1.03 750516 8,548 8,833 1.03 750517 6,791 6,342 0.93 750518 9,904 9,864 1.00 750519 10,012 10,001 1.00 750520 10,457 10,188 0.97 750521 10,081 10,037 1.00 750522 9,582 9,296 0.97 750523 7,846 7,490 0.95 750524 9,882 9,646 0.98 750526 6,930 6,968 1.01 751472 5,706 6,439 1.13 751473 5,872 6,073 1.03 751475 8,458 8,209 0.97 751476 8,176 8,184 1.00 751477 8,327 8,181 0.98 751479 9,380 9,805 1.05 751480 10,096 9,510 0.94 751481 9,000 9,669 1.07 751482 9,390 9,476 1.01 751484 9,750 10,185 1.04 751486 9,880 9,926 1.00 751487 9,775 10,075 1.03 751488 9,873 9,648 0.98 751491 12,394 12,369 1.00 751495 14,396 14,027 0.97 751496 12,494 12,551 1.00 2850002 4,110 3,880 0.94 2850003 8,028 7,945 0.99 2850004 12,823 12,634 0.99 2850005 10,688 10,585 0.99 2850008 9,552 9,129 0.96 2850009 9,649 9,290 0.96 2850010 10,308 10,094 0.98 2850011 10,270 10,069 0.98 2850012 10,063 9,935 0.99 2850013 10,112 10,015 0.99 2850014 12,370 12,046 0.97 2850015 10,309 10,048 0.97 2850016 10,345 10,077 0.97 2850017 8,897 8,684 0.98 2850020 6,813 6,880 1.01 2850021 8,399 9,692 1.15 2850023 9,529 9,257 0.97 2850024 7,736 8,314 1.07 2850025 8,307 8,589 1.03 2850026 9,402 9,820 1.04 2850028 7,930 9,056 1.14 2850029 7,911 8,384 1.06 2850031 8,020 8,707 1.09 2850032 7,935 8,503 1.07 2850033 8,256 8,748 1.06 2850034 8,233 8,960 1.09 2850035 8,786 9,633 1.10 2850036 9,130 9,348 1.02 2850042 3,705 3,983 1.08 2851004 4,457 4,796 1.08 2851005 5,204 5,330 1.02 2851006 8,343 8,027 0.96 2851007 11,484 11,980 1.04 2851008 13,046 13,553 1.04 Station ID Hourly Volume 99th Percentile 30th Highest Ratio Table 7.8. Comparison of 99th Percentile Hourly Volumes and K-Factor Volumes (continued on next page)
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148 output. Using the HCM to calculate hourly capacity, a typical way to compute the v/c ratio from empirical data is v c AADT -factor -factor hourly capacity= ( )
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149 • 0.580 if lane-blocking incidents are not moved to the shoulder. This factor was developed by considering laneblocking incidents that were moved to the shoulder, and reassigning them back to lane-blocking status; and • 1.140 if usable shoulders are unavailable.
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150 congestion is high (e.g., mean TTIs greater than 2.5)
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151 Table 7.10. Peak Period Model Validation for Seattle Section Mean TTI 80th Percentile TTI 95th Percentile TTI Actual Predicted Error (%)
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152 Table 7.11. Weekday Model Validation for Seattle Section Mean TTI 80th Percentile TTI 95th Percentile TTI Actual Predicted Error (%)
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153 95th percentile TTIs in Seattle are so much higher compared with their means, but this indicates that further validation of the models with data from other cities is warranted. reference 1.
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