Pavement Marking Materials and Markers: Real-World Relationship Between Retroreflectivity and Safety Over Time (2006) / Chapter Skim
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Pages 94-111
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82 3. Chapter 3 Study Methodology This chapter presents the study methodology for correlating the safety impact of pavement markings and markers with their retroreflectivity.
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83 traffic data (30)
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84 3.2. Methodology Outline The methodology adopted has five major steps.
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85 Road Class 2-lane Highway Road Class Multi-lane Highway Road Class Multi-lane Freeway Retroreflectivity Pavement Type Concrete,Asphalt Retroreflectivity Modeling Safety Modeling Volume AADT Snow Removal Heavy, medium-light No Snowfall Climate Region Retroreflectivity and Safety Delineation Markings & Markers Delineation Markings & Markers Delineation Markings & Markers Crash / Severity Crash / Severity Crash / Severity Figure 20. Full range of experimental conditions
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86 3.3. Study Enhanced Methodology There are three features of this research methodology that expands on what has been previously accomplished: • The focus is on determining the direct relationship between retroreflectivity and crash frequency and severity (safety)
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87 upon industry panel sources (91)
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88 effect. As discussed in the literature review (Section 2.2.3)
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89 affect the probability of target crash occurrence and/or of crash severity (if perception affects speed choice)
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90 July 1999 and July 2000, the month of remarking will be indexed "0" and subsequent months as a (a=0,1,…A)
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91 Table 22. Illustration of retroreflectivity table for analysis (months of remarking in bold)
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92 It is assumed that each crash count (rightmost column in Table 22) is a realization of a Poisson random variable the mean of which is μy, m, i to indicate that it varies as a function of Year (y)
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93 ∏ ∏= = = − Y 1y 12 1m μ my, c im,y, Y,12Y,2Y,11,121,21,1 im,y, my, e !
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94 By applying Equation 5 and simultaneously solving for pm and qr, the seasonal effect and the safety effect of retroreflectivity will be estimated. The values for pm and qr are multipliers may be thought of as crash or accident modification factors.
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95 Table 25. Variables: pavement material type Year State District 2-lane Highway Start MPEnd MPPavement 1998 California 12 Sinclair Rd 13.5 15.0 Concrete 1998 California 12 Sinclair Rd 15.0 18.5 Asphalt 1999 California 12 Sinclair Rd 13.5 15.0 Concrete 1999 California 12 Sinclair Rd 15.0 18.5 Asphalt 2000 California 12 Sinclair Rd 13.5 15.0 Concrete 2000 California 12 Sinclair Rd 15.0 18.5 Asphalt Table 26.
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96 Table 28. Variables marking restriping dates Year State District 2-lane Highway Start MP End MP Pavement Volume (AADT)
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97 Table 29. Number of total target crashes required by road type Road Type Required number of target crashes to detect a 5% change in safety 2-lane roads 50,000 Multi-lane highway 200,000 Multi-lane freeway 200,000 Given a typical given crash rate, the total number of crashes can also be expressed in terms of miles of road and years of data (Table 31)
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98 Table 30. HSIS volume and crash information for six states, and the values used in the simulation Simulation Values Road Class Variable MN CA NC IL UT OH Average Minimum Maximum Traffic Volumea 472,233 1,719,700 954,718 1,523,788 821574 1323218 Average ADTb 1,294 4,712 2,616 4,175 2,251 3,625 3112 1212 5012 Crash Rate (All day)
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99 Thus, if 26,364 miles × years are divided by 2 years of data, it means that 13,182 miles of 2-lane rural roads over two years would be needed to detect a 5% change in the safety effect of pavement markings. Based on similar calculations, Table 31 was developed.
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