Roundabouts in the United States (2007) / Chapter Skim
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Pages 23-33
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23 This chapter describes a number of safety modeling and evaluation tasks. First, the results of the investigation of the ability of non-U.S.
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24 In light of these limitations and the poor goodness of fit to the U.S. data, the use of existing models to represent the U.S.
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25 Crash Type Model Term Parameter Value Mean Value Term at Mean Value Entering-Circulating Crashes (Crashes involving an entering and a circulating vehicle) L(constant)
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26 crashes, and the relatively poor goodness of fit to the U.S. data as evidenced by the relatively high values of MAD/siteyear (compared to the crashes/site-year)
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27 In selecting the recommended intersection-level models for total and injury crashes, the research team looked for low values of the dispersion parameter and statistical significance of the estimated variable coefficients. Tables 17 and 18 summarize these major considerations.
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28 The approach-level safety performance functions (SPFs) were developed for specific crash types: entering-circulating, exiting-circulating, and approaching.
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29 Because of correlations among the variables in the individual candidate models and the small sample size of crashes, calibration of a model with more than a few variables was not possible. The candidate models presented for each crash type are quite close statistically.
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30 Variable Entering-Circulating Exiting-Circulating Approaching Entry Radius (ft) 0.9901 to 0.9896 – – Entry Width (ft)
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31 especially relevant to roundabouts for which it is widely believed that speed management is the key to how safe a roundabout is. The models are of the following form: where AADT = average annual daily traffic X = independent speed-related variable b, c = calibration parameters Crashes were modeled with AADT and the observed speeds at various locations through the roundabout as independent variables.
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32 x = count of crashes in the n years before conversion P = prediction of annual number of crashes using SPF for intersection with similar characteristics k = dispersion parameter for a given model, estimated from the SPF calibration process with the use of a maximum likelihood procedure Factors then are applied to account for the length of the after period and differences in traffic volumes between the before and after periods. The result is an estimate of B
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From page 33... ...
Setting Previous Control Number of Legs Source of SPF Data Model Urban Signal 4 Howard and Montgomery Counties, MD Acc/yr = exp(-9.00)
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