Intersection Crash Prediction Methods for the Highway Safety Manual (2021) / Chapter Skim
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Pages 26-56
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26 Chapter 2. Literature Review and Survey of Practice This section summarizes literature relevant to the objectives of this research and results of a survey of transportation agencies intended to gain knowledge about their experience with the current HSM intersection predictive methods and assess their needs and priorities as they relate to additional (new)
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27 Figure 1. HSM definitions of segments and intersections (AASHTO, 2010)
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28 Intersection SPFs generally take one of the two forms shown in Equation 2 and Equation 3.
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29 HSM Chapter 11 -- Predictive Methods for Intersections on Rural Multilane Highways Chapter 11 of the HSM includes SPFs for the following intersection configurations and traffic control types on rural multilane highways: • Three-leg intersections with minor road stop control on rural, four-lane divided or undivided highways (3ST) • Four-leg intersections with minor road stop control on rural, four-lane divided or undivided highways (4ST)
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30 Where: Npredicted int = predicted average crash frequency for an individual intersection for the selected year (crashes/year) Nbi = predicted average crash frequency of an intersection (excluding vehicle-pedestrian and vehicle-bicycle crashes)
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31 Where: Nbimv(FI) = predicted average crash frequency of MV, FI crashes of an intersection for base conditions (crashes/year)
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33 and cover crossroad ramp terminals with anywhere from two to six crossroad through lanes (total of both travel directions)
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34 Figure 2. Ramp terminal configurations (AASHTO, 2014)
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35 Figure 2. Ramp terminal configurations (AASHTO, 2014)
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36 One-way, stop-controlled crossroad ramp terminal SPFs generally take the form shown in Equation 15.
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37 PaS,x,at,A = probability of an incapacitating injury crash (given that a fatal or injury crash occurred) for all ramp terminal sites (aS)
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38 HSM Predictive Method Calibration The intersection predictive methods in the HSM contain calibration factors to adjust predictions of the HSM models, developed with data from selected jurisdictions and for specific time periods, to be applicable to other jurisdictions and time periods. Equation 1, for example, presents the calibration factor in general form as Ci, with i representing a specific site type.
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39 Where: Pp,aS,ac,at,KAB = predicted probability of a severe crash (i.e., K, A, or B) for all collision types (at)
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40 where: Nexpected = expected average crash frequency obtained by combining the predicted average crash frequency (Npredicted) with the observed crash frequency (Nobserved)
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41 (2010) , the Federal Highway Administration (FHWA)
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42 without those characteristics, commonly referred to as "base conditions" in the HSM predictive model context. For any given characteristic or treatment, CMF values greater than one indicate that the characteristic or treatment is expected to increase the number of crashes compared to the base conditions, while values lower than one indicate that the treatment is expected to decrease the number of crashes.
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43 intersection SPFs for different levels of severity were independently estimated. For any given collision type (e.g., MV)
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44 Where: Xjr = a row of observed characteristics (e.g., driver, vehicle, roadway, environment) associated with crash r that have an impact on injury severity outcome j βj = a vector of parameters to be estimated that quantify how the characteristics in Xjr impact injury severity outcome j εjr = a disturbance term that accounts for unobserved and unknown characteristics of crash r that impact injury severity outcome j There are as many such linear functions as there are possible injury severity outcomes.
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45 Where j in this case represents all possible injury severity outcomes except for the base outcome. In NCHRP Project 17-45, for example, possible injury (C)
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46 value distributed, resulting in the model structure shown in Equations 32-34 (as outlined in Washington et al., 2010 and Savolainen et al., 2011)
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48 vehicle-bicycle. Handling collision types within HSM predictive methods remains a topic of ongoing research.
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49 • Three-leg intersections with all-way stop control on rural highways - Software makes use of SPF for four-leg intersections with all-way stop control on rural highways for three-leg intersections with all-way stop control on rural highways • Three-leg intersections with signal control on rural highways - Software makes use of SPF for four-leg intersections with signal control on rural highways for three-leg intersections with signal control on rural highways • Four-leg intersections with all-way stop control on rural highways • Three-leg intersections with all-way stop control on urban streets - Software makes use of SPF for four-leg intersections with all-way stop control on rural highways for three-leg intersections with all-way stop control on urban streets • Four-leg intersections with all-way stop control on urban streets - Software makes use of SPF for four-leg intersections with all-way stop control on rural highways for four-leg intersections with all-way stop control on urban streets The Pennsylvania DOT developed SPFs (Donnell et al., 2014) for rural two-lane highway segments and intersections.
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50 • Install dynamic signal warning flashers • Discontinue late night flash operations at signalized intersections • Construct bypass lanes In NCHRP Project 17-59, researchers developed CMFs for intersection sight distance at unsignalized intersections (i.e., intersections with minor road stop control)
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51 382 treated sites (302 four-leg, 80 three-leg intersections) and 367 untreated sites (319 four-leg, 48 three-leg intersections)
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52 Very often 11.8% (4) Regularly 26.5% (9)
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53 • Has your agency developed its own SPFs for use with HSM Part C?
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54 • Are there any other intersection configurations not listed in the previous question that you believe should receive high priority for inclusion in the HSM? - U-turn intersections, also referred to as J-turn intersections, were mentioned the most by survey respondents.
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55 • What types of data does your agency have available for intersections that might be useful for development of intersection crash prediction models? (Please check all that apply.)
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56 SPFs for intersections are equations that relate the expected intersection crash frequency (possibly by type and/or severity) for some defined time period to characteristics of the intersection.
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