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From page 33... ... 33 C h a p t e r 3 This chapter describes each of the data sets analyzed by the Penn State team along with the results of the analyses performed on those data. The chapter begins with a discussion of the VTTI data and models followed by a discussion of the data from UMTRI and the models estimated from those data. Read the entire page →
From page 34... ... 34 Accordingly, one may also consider the use of ZIP and ZINB models, which can improve model goodness of fit. Systematic Model Testing A series of count regression models were tested in groups. Read the entire page →
From page 35... ... 35 • Set B: Predictors included a series of gender interaction terms for each of the variables used in Set A The objective was to explore gender differences, which were expected from the literature. Read the entire page →
From page 36... ... 36 is gradually getting closer to the actual level of 47%, indicating a generally better fit in this important attribute. One can quickly see that the first four predictors represent variables interacting with gender. Read the entire page →
From page 37... ... 37 For example, the average event frequency li for driver i increases 0.71% if the driver is male and has less than 10 years of driving experience, compared with males with more than 10 years of driving experience, assuming the error terms are independent of xik and remain unchanged (and the model is correct) Read the entire page →
From page 38... ... 38 each individual had in a year, and the explanatory variables include individual socioeconomic characteristics at Level 1 and gender at Level 2. Models were estimated using the open-source software OpenBUGS. Read the entire page →
From page 39... ... 39 difference is that one can now assess the effect of the variable on men and women separately. Discussion of Outliers As a by-product of running the hierarchical driver-based model using a Poisson lognormal model, the team was able to observe individual drivers' random effects. Read the entire page →
From page 40... ... 40 sign and some, such as curve, change in level of significance as well. Using a Chi-square test to compare the two models results in a significant difference being found with the calculated Chisquare equal to 8.82 (resulting level of significance is p = 0.0030) Read the entire page →
From page 41... ... 41 • Distraction 5: internal distraction -- reading, a moving object in the vehicle, dealing with an insect or pet. • Distraction 6: dining -- includes eating or drinking. Read the entire page →
From page 42... ... 42 Table 3.9. Summary of Initial Estimated Binary Logit Event-Based Models Type Variable Context Only Context and Driver Attributes Fully Specified Parameter Coeff Diff (%) Read the entire page →
From page 43... ... 43 the past year. There are 42 events, including such items as personal injury or illness, change in financial state, and change in social activities. Read the entire page →
From page 44... ... 44 sought. Driver Impairment 1 (drowsy, sleepy, fatigued) Read the entire page →
From page 45... ... 45 crashes. This is the first example of a validated event that can be used as a source of a surrogate observation (e.g., the value coded for the variable exceed road or lane edge) Read the entire page →
From page 46... ... 46 tives written by data coders at VTTI during data assembly. All three events involved a driver falling asleep and nearly running off the road. Read the entire page →
From page 47... ... 47 looks away from the road to obtain the object. The vehicle drifts to the right and nearly hits a boat loaded on a trailer that is parked on the right side of the road. Read the entire page →
From page 48... ... 48 These examples are sufficient to illustrate the method. The team hopes that if a valid model is developed, the screening of valid events will help in the identification of the surrogate measured within those contexts, eliminating the need to go back to narratives for additional assurance. Read the entire page →
From page 49... ... 49 UMtrI Data: Kinematic Models Table 3.11 is a glossary of interaction term variable acronyms used in UMTRI kinematic models. They include explicit recognition of positive (e.g., PlanoffPpi as positive lane offset interacting with positive pitch) Read the entire page →
From page 50... ... 50 Table 3.12. Lateral Speed Model, Linear Regression Variable Name Coefficient Std. Read the entire page →
From page 51... ... 51 combining lane offset and yaw, lane offset and pitch, lateral speed and yaw, and lateral speed and roll; the latter two sets of terms (i.e., lateral speed and yaw and lateral speed and roll) can be directly related in fundamental kinematics. Read the entire page →
From page 52... ... 52 (recall that each observation of an alert or pseudoalert begins 5 s before the alert and continues until 5 s after the alert is extinguished) Read the entire page →
From page 53... ... 53 Table 3.18. Model 4: Longitudinal Speed, TwoRegime, Week 1, 5 s Before Alert, Linear Regression Variable Name Coefficient t-statistic p-value Dark 1.2521 10.04 0.0000 Positive yaw -3.1605 -85.18 0.0000 Negative yaw -1.6041 -36.75 0.0000 Positive roll 0.6950 19.74 0.0000 Negative roll 1.7320 54.31 0.0000 Positive pitch -0.6966 -10.04 0.0000 Negative pitch -0.5804 -8.70 0.0000 Positive lane offset -1.7872 -11.22 0.0000 Negative lane offset -1.8011 -13.66 0.0000 Positive lateral speed 1.8389 8.24 0.0000 Negative lateral speed 0.7882 4.40 0.0000 Road class: Unknown 2.8306 0.83 0.4060 Road class: Major surface -18.7711 -117.72 0.0000 Road class: Minor surface -16.1927 -99.69 0.0000 Road class: Local -17.5117 -79.02 0.0000 Road class: Ramp -9.1532 -63.32 0.0000 Constant 62.0448 443.54 0.0000 Number of obs = 34,700; Prob > F = 0.0000; R-squared = 0.4026; Adj R-squared = 0.4024. Read the entire page →
From page 54... ... 54 Table 3.22. Model 8: Longitudinal Speed, TwoRegime, Week 1, 5 s Before Alert, Add Measurement Duration, Linear Regression Variable Name Coefficient t-statistic p-value Dark 1.2525 10.04 0.0000 Positive yaw -3.164436 -85.22 0.0000 Negative yaw -1.609261 -36.83 0.0000 Positive roll 0.6985 19.82 0.0000 Negative roll 1.7355 54.38 0.0000 Positive pitch -0.6952267 -10.02 0.0000 Negative pitch -0.5789266 -8.67 0.0000 Positive lane offset -1.772985 -11.12 0.0000 Negative lane offset -1.785805 -13.53 0.0000 Positive lateral speed 1.8408 8.25 0.0000 Negative lateral speed 0.7782 4.35 0.0000 Road class: Unknown 2.6442 0.78 0.4380 Road class: Major surface -18.76191 -117.64 0.0000 Road class: Minor surface -16.18095 -99.58 0.0000 Road class: Local -17.49641 -78.93 0.0000 Road class: Ramp -9.127808 -63 0.0000 Measurement duration -0.0925925 -2.57 0.0100 Constant 62.2482 387.28 0.0000 Number of obs = 34,700; Prob > F = 0.0000; R-squared = 0.4727; Adj R-squared = 0.4725. Read the entire page →
From page 55... ... 55 Adding measurement duration affected the models slightly, but there were no drastic changes in coefficients and goodness of fit. Lateral speed had little effect on longitudinal speed for any two-regime model. Read the entire page →
From page 56... ... 56 Cohort-Based approach The cohort design can be used to formulate an exposurebased model relating potential risk factors to several possible outcomes. The cohort design is well-suited to account for measures of exposure such as time at risk or distance traveled under specific driving conditions. Read the entire page →
From page 57... ... 57 Table 3.27. Model 12: Longitudinal Speed, Three-Regime, 5 s Before Alert, Linear Regression Model 12a Week 1 Model 12b Weeks 2–4 Variable Name Coefficient t-statistic p-value Variable Name Coefficient t-statistic p-value Dark 1.2517 9.11 0.0000 Dark 0.8362 10.60 0.0000 Positive yaw -3.1645 -53.23 0.0000 Positive yaw -2.2566 -80.07 0.0000 Negative yaw -1.6091 -21.79 0.0000 Negative yaw -2.2454 -41.04 0.0000 Positive roll 0.6983 13.46 0.0000 Positive roll 1.1166 30.34 0.0000 Negative roll 1.7354 37.36 0.0000 Negative roll 1.0614 44.56 0.0000 Positive pitch -0.6950 -10.68 0.0000 Positive pitch -0.6801 -17.40 0.0000 Negative pitch -0.5788 -9.32 0.0000 Negative pitch -0.5676 -15.12 0.0000 Positive lane offset -1.7731 -12.07 0.0000 Positive lane offset -2.3857 -23.22 0.0000 Negative lane offset -1.7836 -14.11 0.0000 Negative lane offset -2.6475 -32.64 0.0000 Positive lateral speed 1.8396 7.84 0.0000 Positive lateral speed 2.1194 13.21 0.0000 Negative lateral speed 0.7765 4.90 0.0000 Negative lateral speed 1.7488 14.84 0.0000 Road class: Major surface -18.7643 -119.89 0.0000 Road class: Major surface -17.7460 -178.11 0.0000 Road class: Minor surface -16.1835 -94.49 0.0000 Road class: Minor surface -15.4426 -136.77 0.0000 Road class: Local -17.4991 -85.12 0.0000 Road class: Local -19.3209 -159.24 0.0000 Road class: Ramp -9.1299 -58.20 0.0000 Road class: Ramp -9.1206 -91.98 0.0000 Measurement duration -0.0932 -2.58 0.0100 Measurement duration 0.0001 0.00 0.9960 Constant 62.2522 376.14 0.0000 Constant 61.4875 561.09 0.0000 Number of obs = 34,700; Prob > F = 0.0000; R-squared = 0.4727; Adj R-squared = 0.4725. Read the entire page →
From page 58... ... 58 level specification was applied to cluster driver attributes at a second, separate level. Note that road class was used, but additional dimensions beyond road class could have been specified, Table 3.30. Read the entire page →
From page 59... ... 59 frequency based on the fact that the wet variable has a negative coefficient. Overdispersion results show that the NB regression is a better choice than the Poisson regression. Read the entire page →
From page 60... ... 60 paired with and tested against relevant surrogates (such as lateral accelerations) to obtain more targeted evaluations. Read the entire page →
From page 61... ... 61 bachelor's degree or above, last year's miles driven, and years of driving experience, are shown after the second equation. Specifically, it is a random intercept and random slope model formulation (i.e., both the intercept and the slope vary randomly across the subjects) Read the entire page →
From page 62... ... 62 structure. Thus, these models were structured as event-based models using homogeneous trip segment data. Read the entire page →
From page 63... ... 63 maximum and minimum speed are similar to their effects in the first CSW logit model, but the OR for brake applications is slightly higher (but is still insignificant) Read the entire page →

From page 33...

... 33 C h a p t e r 3 This chapter describes each of the data sets analyzed by the Penn State team along with the results of the analyses performed on those data. The chapter begins with a discussion of the VTTI data and models followed by a discussion of the data from UMTRI and the models estimated from those data.