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23 Divided Roads, All Crashes All Crashes Single-Vehicle Crashes E [ N ]ADI = L e b - ln 12 + 0.835 ln ADT +0.657 MBAR- 0.068SW 0 (14) E [ N ]SD = where Le b0 - ln 12 + 0.597 ln ADT + 0.407 FC + 0.999 MBAR + 0.166 RSP - 0.053SW - 0.327 LTLN (6) E[N]i = expected crash frequency per year for Condition i; L = segment length (mi); Multi-Vehicle Crashes b0 = model intercept; ADT = average daily traffic (vehicles/day); E [ N ]MD = L e b - ln 12 + 1.203 ln ADT - 0.010 MW + 0.523 MBAR - 0.137 SW + 0.452 LTLN 0 RSP = right shoulder paved (no/yes); (7) SW = average right and left shoulder width (ft); MW = median width (ft); All Crashes FC = functional class principal arterial (no/yes); MBAR = median barrier (no/yes); and E [ N ]AD = L e b - ln 12 + 0.835 ln ADT + 0.781 MBAR+ 0.172 FC +0.228 RSP -0.118 SW (8) 0 LTLN = left turn lane present (no/yes). The following subscripts are used: Undivided Roads, All Crashes S = single-vehicle crashes, Single-Vehicle Crashes M = multi-vehicle crashes, A = all crashes, E [ N ]SU = L e b - ln 12 + 0.795 ln ADT + 0.379 RSA 0 (9) D = divided, U = undivided, and Multi-Vehicle Crashes I = injury crashes. E [ N ]MU = L e b - ln 12 + 1.223 ln ADT - 0.474 RSP - 0.111 SW 0 (10) No predictor variables were statistically significant for the injury models for the undivided roads; hence, these mod- els are not reported here. There are three intercepts (b0) for All Crashes the models developed because each state was used as an E [ N ]AD = L e b - ln 12 + 0.960 ln ADT - 0.067 SW 0 (11) indicator to allow for a more accurate estimation of the variables and their coefficients. The three intercepts are sim- ilar for all models and are presented in Table 12. The user Divided Roads, Injury Crashes can use any of these in the development of estimates since Single-Vehicle Crashes all will produce results of similar magnitude. An approach for predicting crashes with the models is described later in E [ N ]SDI = L e b - ln 12 + 0.571 ln ADT + 0.251FC + 0.813 MBAR- 0.053SW - 0.728 LTLN 0 this section. (12) As described above, the data were divided into two halves for the analysis: the training and validating datasets, respec- tively. The training datasets contained 1,028 divided and Multi-Vehicle Crashes 242 undivided segments. The validation datasets included E [ N ]MDJ = L e b - ln 12 + 0.981 ln ADT - 0.009 MW - 0.137 SW 0 (13) 997 divided and 243 undivided segments and were used for Table 12. Model intercepts. Highway Crash Type CA KY MN Single 3.087 3.567 3.002 Divided Multi 7.974 7.884 8.100 All 4.235 4.457 4.317 Single 4.759 4.976 5.043 Undivided Multi 7.970 7.052 7.671 All 5.105 4.758 5.054 Single, injury 3.644 4.141 4.711 Divided Multi, injury 7.217 6.764 7.900 All, injury 4.614 4.569 5.547