Skip to main content

Currently Skimming:


Pages 205-215

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 205...
... 205 Incidents Defined and Classified An incident is an unplanned disruption to the capacity of the facility. Incidents do not need to block a travel lane to disrupt the capacity of the facility.
From page 206...
... 206 incidents represent the service provided for vehicles. The incident occurrence rate and its duration are fed into the model to fully characterize the problem.
From page 207...
... 207 incidents recorded and categorized as defined in a previous section of this appendix, along with their durations. Study period monthly probabilities of different incident types are computed from Equation F.1.
From page 208...
... 208 Incident Rates Estimation in the Study Periods in a Month All subsequent discussions in this section refer to items numbered 1, 2, and 4 in Figure F.1. Two possible approaches for incident rate estimation can be carried out.
From page 209...
... 209 in Appendix D and depicted in Table F.4. Equation F.3 gives a definition of G(i)
From page 210...
... 210 Core Incident Methodology Model This model begins with the widely used assumption that the number of incidents in a given study period is Poisson distributed (Skabardonis et al.
From page 211...
... 211 is also Poisson with the rate nj × ;i. Thus, the probability of incident type i in month j is computed using Equation F.12: Prob incident type in an SP in month 1 1 (F.12)
From page 212...
... 212 Figure F.2. Proposed process flow.
From page 213...
... 213 100 million VMT, and the CTI factor is 7 based on local data (Khattak and Rouphail 2005)
From page 214...
... 214 shown to be 7.64%. All computations are shown in Equation F.20: P e e n E ij i  { } = − = − = − = ( )
From page 215...
... 215 The methodology could benefit from a number of enhancements, notably in acknowledging the correlation between incidents and weather conditions. The team is aware of a parallel effort under the auspices of SHRP 2 Project L04 in which a model is being tested in the New York area that provides conditional incident probabilities based on weather events.

Key Terms



This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More information on Chapter Skim is available.