Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies (2012) / Chapter Skim
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Pages 229-244
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229 A p p e n d i x e Weather varies by time and locations for which there are no actual data sources. Consequently, the weather data used for these analyses were obtained from publicly available records collected from the National Oceanic and Atmospheric Administration (NOAA)
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230 simultaneously in other areas of the Seattle metropolitan region can be different. For example, a storm moving north ward that affects SeaTac at 5:00 p.m.
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231 segment when the past 2 hours was set to 4 (severe = snowy) was 612 seconds, but the mean travel time for the same time period was 676 seconds when the variable was set to 3.
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232 Table E.1. Percentage of Travel Time Occurring in Specific Regimes Given Different Weather Conditions (A.M.
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233 Table E.2. Percentage of Travel Time Occurring in Specific Regimes Given Different Weather Conditions (P.M.
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234 Figure E.2, can be seen in the percentage shift from Regime 3 travel to Regime 4 travel. Regime 4 sees a much sharper change in speed than Regime 3.
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235 ignored in the analysis results (discussed below) , thus slightly underestimating the potential impacts of rain on travel time.
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236 The resulting estimates and 95% confidence bounds for p1/ p2 are shown in Table E.7 and graphically illustrated in Fig ure E.3. The estimates for p1/p2 are consistently around 0.53 to 0.61.
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237 Figure E.4. Sea520WB travel times in relation to accident severity or no accident.
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238 The pvalues for all eight comparisons (four segments, two commute periods each) are given in Table E.8.
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239 The analysis examined whether accident rates are higher during the 6 hours after rainfall after it has been dry for 504 hours (3 weeks) or 336 hours (2 weeks)
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240 • Adjusting the 6 hours of rain threshold to capture 8 or 10 hours after the rain begins to see if there are more accidents; and • Developing a statistical test to determine whether the above findings are statistically relevant. Analysis of Snowfall effects An analysis comparing roadway performance when snow was falling versus when snow was not falling and when no pre cipitation was falling resulted in counterintuitive findings that snow was not a significant contributing factor to road way performance.
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241 The analytic tests performed on the Seattle test corridors showed that travel times in all test corridors were not equally affected by wind. In fact, in many corridors, wind did not have any statistically significant effect on travel times.
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242 exist, and wind does not cause the roadway to move. Table E.9 gives examples of how wind affects various corridors dif ferently, even though the corridors are directly connected.
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243 is most likely due to a combination of factors: the I90 bridge is more modern, has full shoulders, and sits higher off the water (and therefore experiences less winddriven spray)
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244 Analysis of Fog Effects The analysis of the effects of fog was problematic, as fog tends to be highly localized. Thus, while the airport may be very foggy (to the point that landings and take-offs are restricted for lack of visibility)
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