Development of Analysis Methods Using Recent Data (2012) / Chapter Skim
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16 Analyses Using Vehicle-Based Data Data Acquisition and Preparation In May and June 2008, the Virginia Tech Transportation Institute (VTTI) provided the project team with data for 33 events observed during the 100-car in-vehicle study.
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17 Table 3.1. Data Obtained from 100-Car In-Vehicle Study Data Requested Data Delivered Notes Units Sync frames Speed Speed mile/hr GPS location lat, lon degrees/s Throttle Throttle unitless Brake brake_onoff 0 = off, 1 = on Lateral acceleration accel_x g Longitudinal acceleration accel_y g Target ID fwd_ID_n n = 1,2, .
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18 estimates for each vehicle's initial speed, the time points at which each driver changed acceleration and the corresponding accelerations were computed using WinBUGS, and the results are displayed in Table 3.3. For this case, because the data acquisition began after the leading driver had begun his or her final deceleration, the follower's reaction time was computed as the difference between when the lead vehicle came to a stop and when the following driver initiated the final deceleration.
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19 14.75 ft/s, while the driver of the leading vehicle was traveling at about 8.9 ft/s. The leading driver was decelerating at about -3.7 ft/s2 and came to a stop about 2.4 s after the acquisition.
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20 Table 3.3. WinBUGS Estimates for the Model Parameters for Case 99540 Variable Mean Standard Deviation 2.5%ile 97.5%ile Following Vehicle Initial speed (ft/s)
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21 leader braked to a stop, as did the follower, without colliding (Figures 3.9–3.11)
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22 after about 10.6 s from the start of the data series, the leader began decelerating at about -11.94 ft/s2, which continued until the vehicle stopped. About 11.15 s from the start of the data series, the follower began decelerating at about -9.47 ft/s2.
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23 leader increased the deceleration rate to about -13.5 ft/s2. The following driver initially accelerated at about 5.4 ft/s2, and after about 6.65 s, eased to about 2.0 ft/s2.
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24 Figure 3.14. Measured and modeled following vehicle speeds (Case 104119)
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25 forward radar and the range as predicted by the trajectory models. In both cases, there is a plausible reconstruction of the data series.
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26 Respective speed trajectories for the leading and following vehicles were plotted in Figure 3.33. The radar could only manage to capture the leading vehicle's information for about 5 s.
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27 Figure 3.22. Measured and modeled speedometer speeds from following vehicle (Case 73082)
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28 The piecewise model appears plausible as shown in Figures 3.35 and 3.36 fitting the observed speed of the following vehicle and range data. Also, a counterfactual model (see Figure 3.37)
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29 Figure 3.25. Speed trajectories of the leading and following vehicles (Case 104851)
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30 Table 3.6. WinBUGS Estimates for the Model Parameters for Case 104851 Variable Mean Standard Deviation 2.50% Median 97.50% Following Vehicle Initial speed (ft/s)
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31 Figure 3.29. Counterfactual model (Case 104851)
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32 Figure 3.33. Speed trajectories for the leading and following vehicles (Case 104283)
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33 Table 3.7. WinBUGS Estimates for Case 104283 Variable Mean Standard Deviation 2.50% Median 97.50% Following Vehicle Initial speed (ft/s)
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34 Figure 3.38. Initial view of the leading vehicle (Case 60289)
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35 Figure 3.39. Speed trajectories of the leading and following vehicles (Case 60289)
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36 Figure 3.41. Predicted and observed instrumented speeds (Case 60289)
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37 Figure 3.44. Initial view of the leading vehicle on a two-lane, two-way highway (Case 92660)
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38 Figure 3.47. Range and range-rate data (Case 92660)
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39 Table 3.9. WinBUGS Estimates for the Parameters for Case 92660 Variable Mean Standard Deviation 2.50% Median 97.50% Following Vehicle Initial speed (ft/s)
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