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From page 76... ... C H A P T E R 6 Analytical Tools and Initial Analysis of Lane Departure Research QuestionsThis chapter outlines several exploratory analytical approaches that were used to evaluate the existing naturalistic driving study data and that may be appropriate for analyzing the data that will result from the full-scale naturalistic driving study data to answer a variety of lane departure research questions. The ﬁrst is a data mining approach (classiﬁcation and regression tree analysis) Read the entire page →
From page 77... ... 77informed decisions about improved highway design during initial design and during reconstruction and rehabilitation. The information can also be used to select appropriate roadway countermeasures and guide policy decisions. Read the entire page →
From page 78... ... 780.1-s interval would require a tremendous amount of resources. Therefore, data applications using continuous data would likely need to reduce driver data at a lower resolution (e.g., once per minute) Read the entire page →
From page 79... ... 79Figure 6.1. Example of data set showing data segmented at the continuous level.provided in most cases at 2 Hz (two rows per second, or one image per ﬁve rows of vehicle trace data) Read the entire page →
From page 80... ... 80Figure 6.2. Example of data set showing data segmented at the sequential block level.All of the data elements that the team determined were important from the literature and could be obtained from one of the available databases (vehicle data, aerial imagery, roadway data, forward imagery, and crash database) Read the entire page →
From page 81... ... 81Figure 6.3. Example of data set showing data segmented at the sample level.control or making sudden evasive maneuvers. Read the entire page →
From page 82... ... 82Variable Source Description Variable Type Driver Variables Age Gender OvrSpd5 OvrSpd10 OvrAdvSpd5 OvrAdvSpd10 Vehicle Variables Spd LatSpd Ax Ay RollRate PitchRate YawRate Following Environmental Variables Time EnvCond RoadSurf Provided with data set Calculated from speed and posted speed limit Calculated from speed and posted speed limit Provided with data set Extracted from forward video Extracted from forward video and time Extracted from forward video Extracted from forward video and wiper status Age of driver 1 = male, 2 = female Fraction of time driver exceeds the posted speed limit by 5 mph on rural, two-lane roads Fraction of time driver exceeds the posted speed limit by 10 mph on rural, two-lane roads Fraction of time driver exceeds the advisory curve speed by 5 mph on rural, two-lane roads Fraction of time driver exceeds the advisory curve speed by 10 mph on rural, two-lane roads Vehicle forward speed (m/s) Vehicle side speed (m/s) Read the entire page →
From page 83... ... 83Variable Source Description Variable Type Roadway Variables Radius CurveType LaneWidth ShldWidth ShldType PvMCond DwyDen Other Variables AADT OnDen Conflict Angle MaxOff CrshDen Extracted from aerial imagery Extracted from forward video Provided Extracted from forward video Provided Extracted from forward video Extracted from forward video and vehicle data Extracted from vehicle data Extracted from Michigan crash database and aerial imagery Curve radius in m Direction of curve from perspective of driver 0: No curve 1: Right curve 2: Left curve Lane width in m Shoulder width in m Type of shoulder present 1: Paved 3: Gravel 4: Earth 6: No shoulder 7: Partially paved Pavement marking condition 0: Highly visible 1: Visible 2: Obscure Density of driveways to the right (driveways/m) Annual average daily traffic for roadway segment in vehicles per day Density of on-coming vehicle (vehicles/m) Read the entire page →
From page 84... ... 84Figure 6.4. Begin and end point for event. Read the entire page →
From page 85... ... 85Sampling Approach A sample-based approach using a sampling interval of 0.1 s was used to model the data. As a result, every 10th observation (0.1-s frame) Read the entire page →
From page 86... ... 86However, for application purposes, it is desirable to create an end product that balances the model's ability to explain the maximum amount of deviation with a simpler model that is easy to interpret and apply. To simplify the ﬁnal model, the user can set values such as the minimum number of observations present before a split occurs or minimum deviance allowed at each node. Read the entire page →
From page 87... ... 87Figure 6.6. Initial tree model for left-side lane departures.Figure 6.7. Read the entire page →
From page 88... ... 88Figure 6.8. Final tree model for right-side lane departures.large amount of data, which would be extremely resourceintensive. Read the entire page →
From page 89... ... 89be correlated, only one variable was evaluated. For instance, ambient conditions and roadway surface condition are highly correlated. Read the entire page →
From page 90... ... 90were 3.8 for left-hand curves and 6.6 for right-hand curves. Weather and time of day did not appear to be relevant because the odds ratio was close to 1.0. Read the entire page →
From page 91... ... 91Intercept Criterion Only Intercept and Covariates AIC 35815.581 29956.217 SC 35825.176 30023.384 −2 Log L 35813.581 29942.217 Association of Predicted Probabilities and Observed Responses Percent 80.0 Somers' D 0.624 concordant Percent 17.6 Gamma 0.640 discordant Percent tied 2.4 Tau-a 0.047 Pairs 442473680 c 0.812 R-Square R-square 0.0526 Max-rescaled R-square 0.1873 Hosmer and Lemeshow Goodness-of-Fit Test Chi-square DF Pr > ChiSq 445.9524 8 <.0001 Table 6.4. Model Fit Statistics for Left-Side Lane DepartureVariable Condition Estimate Std Error p-value OR 95% Lower OR Estimate OR 95% Upper Age 1 −0.0105 0.00123 <.0001 0.987 0.990 0.992 Gender 1 vs. Read the entire page →
From page 92... ... 92Intercept Criterion Only Intercept and Covariates AIC 17921.931 11629.157 SC 17931.503 11734.452 −2 Log L 17919.931 11607.157 Association of Predicted Probabilities and Observed Responses Percent 94.0 Somers' D 0.884 concordant Percent 5.6 Gamma 0.887 discordant Percent tied 0.3 Tau-a 0.029 Pairs 183668320 c 0.942 R-Square R-square 0.0578 Max-rescaled R-square 0.3717 Hosmer and Lemeshow Goodness-of-Fit Test Chi-square DF Pr > ChiSq 1510.8238 8 <.0001 Table 6.6. Model Fit Statistics for the Right-Side Lane Departure ModelVariable Condition Estimate Std Error p-value OR 95% Lower OR Estimate OR 95% Upper Age 1 0.0427 0.00286 <.0001 1.038 1.044 1.050 Radius 1 −0.00025 6.192E-6 <.0001 1.000 1.000 1.000 LaneWidth 1 −1.4042 0.1140 <.0001 0.196 0.246 0.307 ShldType 3 vs. Read the entire page →
From page 93... ... 93τ is the effect of X at the mean level of the other predictors. For example, to determine the necessary sample size for detecting that the effect of a one standard deviation increase in lane width results in a 50% increase in the odds of left lane departure, with all other continuous variables at their mean values, then τ = log(1.5) Read the entire page →
From page 94... ... 94The likelihood function is given by Li(θYi) Read the entire page →
From page 95... ... 95Estimated using generalized estimating equations (GEE) (R: geepack: geeglm) Read the entire page →
From page 96... ... 96Analysis Approach 4: Time Series Analysis Different from more common case-control study, the naturalistic data provides more than just counts of events. The purpose of using a dynamic model that puts interests on each 0.1 s includes modeling the pattern of driving and providing information "on" (while) Read the entire page →
From page 97... ... 97Moving Average Model of order p and q (ARMA(p,q) Read the entire page →
From page 98... ... 98displayed. Because the residuals are standardized, we expect that about 99% of them will be within three standard deviations of their mean zero. Read the entire page →
From page 99... ... 9939200 39400 39600 39800 40000 40200 -3 -1 1 3 Some smooth functions for CURVE ( Driver= 51 ) Time C U R V E Figure 6.11. Read the entire page →
From page 100... ... 100negligible, and the autocovariance function in the bottom panel suggests that the autoregressive and the moving average structures in the residual account for the residual time dependence. Sample Size Several methods were considered to estimate the sample size needs for conducting a time series analysis in the full-scale study. Read the entire page →
From page 101... ... 101lane departures and samples of normal driving. Methods 1 and 2 ([1] Read the entire page →

From page 76...

... C H A P T E R 6 Analytical Tools and Initial Analysis of Lane Departure Research QuestionsThis chapter outlines several exploratory analytical approaches that were used to evaluate the existing naturalistic driving study data and that may be appropriate for analyzing the data that will result from the full-scale naturalistic driving study data to answer a variety of lane departure research questions. The ﬁrst is a data mining approach (classiﬁcation and regression tree analysis)