Integration of Analysis Methods and Development of Analysis Plan (2012) / Chapter Skim
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From page 21... ...
21 C h a p t e r 6 This chapter provides sample work plans for five global research areas: lane-departure crashes, intersection crashes, driver distraction, driver fatigue, and alcohol-impaired driving. Overview of Work plans Each proposed Project S08 work plan should follow this outline: 1.
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22 Proposed Surrogate Measures Because crashes are relatively rare events, other factors, such as the amount of lane deviation, will be used as a crash surrogate. While it is assumed that lane deviation is correlated to crash occurrence, the team is not aware of any studies that have proven this relationship.
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23 question. Specific tasks are not broken out, but the components could easily be divided into tasks.
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24 possible to collect eye-tracking information. Since eye-tracking data are not available in naturalistic driving studies, forward scan position will be used as a proxy.
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25 Table 6.1. Necessary Data Elements for Lane-Departure Work Plan Data Element Data Stream Minimum Vehicle Factors Latitude, longitude In-vehicle DAS ±6.6 ft Distance between vehicle and nearest strikeable object In-vehicle DAS ±6.6 ft Vehicle position from lane center DAS lane position tracking system ±0.1 ft Forward and lateral acceleration and speed In-vehicle DAS ±0.1 ft/s2 and 0.1 ft/s Pitch, roll, yaw In-vehicle DAS NA Roadway Factors Lane and shoulder widths Mobile mapping ±0.25 ft Roadway and shoulder surface types, number of lanes, presence and type of edge and centerline rumble strips Mobile mapping NA Horizontal and vertical curve lengths and radii, distance between successive curves, type and characteristics of curve spirals, curve start and end points Mobile mapping ±25 ft Superelevation, grade Mobile mapping ±0.5% Lane cross slope Mobile mapping ±0.1% Curve direction Will be extracted using DAS forward imagery NA Type and location of signage (e.g., chevrons)
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26 The sequential block method was selected for several reasons. Continuous data segmentation would represent all driving situations and would provide a high level of confidence that meaningful patterns in the data could be detected.
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27 were reduced from UMTRI's lane-departure and collisionwarning system FOT data. Odds ratios were calculated using logistic regression.
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28 of left- and right-turn lanes, signal phasing, roundabouts, pedestrian crossings, and signage. Specific Research Questions The research outlined in this proposal will answer the following specific research questions: How do intersection geometric and operational features influence driver scan behavior and response?
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29 information necessary to make the correct decision about whether to slow down or continue through the intersection during the yellow interval. The study hypothesis is that visual scanning behavior is a function of intersection geometry, operational factors, and driver factors and can be correlated to RLR crash risk.
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30 night lighted)
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31 Table 6.2. Necessary Data Elements for Intersection Work Plan Data Element Data Stream Minimum Vehicle Factors Vehicle length, center of gravity, acceleration capability, engine size Driver questionnaire NA Latitude, longitude In-vehicle DAS ±6.6 ft Distance to nearest vehicle or pedestrian crossing path In-vehicle DAS, forward video, forward or side radar ±6.6 ft Forward and lateral acceleration and speed In-vehicle DAS ±0.1 ft/s2 and 0.1 ft/s Pitch, roll, yaw In-vehicle DAS NA Distance from intersection stop line DAS, forward video ±1.0 ft Clearance interval time Forward video 0.1 s Rtime DAS and forward video 0.1 s Intersection Factors Lane width Mobile mapping ±0.25 ft Roadway and shoulder surface type, number of lanes, presence of bike lane, turn lane configuration Mobile mapping NA Approach grade Mobile mapping ±0.5% Approach speed limit Mobile mapping NA Presence, type, and condition of crosswalk Mobile mapping or forward video NA Sight distance to signal head Measured from forward video ±6.6 ft Signal head type and configuration Mobile mapping or forward video NA Clearance interval Forward video 0.1 s Exposure Factors Daily entering vehicles State databases NA Time into trip Extracted from DAS NA Traffic density Extracted from DAS forward imagery NA Intersection crash data State databases NA Percentage of time driving on four-lane arterials Extracted from DAS NA Driver Factors Age and gender Driver questionnaire NA Driver distraction Extracted from DAS driver videos NA Alcohol use Inferred from DAS NA Driver fatigue Extracted from driver video NA Driver scan behavior Inferred from driver face tracking NA
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32 approximately 15 seconds, and calculation of time into red requires 30 seconds, for a total of about 45 seconds. The above estimates result in 312 + 15 + 30 = 357 seconds (5.95 minutes)
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33 Documentation of Results Project outcomes will be presented in a report that will include a description of the data, data reduction, model formulation, analysis, results, and conclusions. The team will generate a white paper on the algorithm developed to flag RLR.
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35 of the driver and vehicle sampled for each 0.1 second of the epoch. Driver and epoch number will key a second database to this database.
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36 to relate to crash likelihood in drowsy drivers. It is likely that crashes will be rare events in the naturalistic driving data, and ROR crashes may be even rarer.
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37 assumption of data independence would not be appropriate. Thus, a repeated measures analysis of variation and conditional logistic regression will be used depending on the nature of the dependent variables.
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38 research team will gain a better understanding of the role that fatigue plays in safety incidents and ROR crashes. Such data will also help determine what types of countermeasure techno logies are most effective.
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39 Data Analysis Plan The data used for this analysis will be restricted to singleoccupant vehicles (vehicles without passengers) to avoid any potential confounders from alcohol use by other occupants of the vehicle.
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40 Pitfalls or Limitations That May Be Encountered and How to Address Them Perfumes and other substances could falsely be identified as alcohol by the current NDS alcohol-detection system. Additional data coding resources may be needed to separate single- and multiple-occupant situations.
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