Influence of Infrastructure Design on Distracted Driving (2022) / Chapter Skim
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Pages 96-109
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From page 96... ...
96 Chapter 9. Safety Framework to Assess the Impact of Overhead Dynamic Message Signs on Distraction Using the SHRP2 NDS Data 9.1 Introduction 9.1.1 Background Similar to the Safety Framework described in Chapter 8, this Safety Framework utilized the SHRP2 NDS data.
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97 Prior studies have examined effects of DMS on driver behavior. One study using eye tracking software found that drivers fixated on DMS more than on standard road signs (Anttila et al.
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98 9.2.1 Curated SHRP2 NDS Data Sets This Safety Framework utilized SHRP2 NDS data. More information about this data set is provided in Section 4.1 and 8.2.1.
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99 9.3 Data Request and Data Reduction IRB approval was obtained through Iowa State University for the use of the SHRP2 NDS data, after which a data request was made to VTTI. However, it was found through the request to VTTI that neither the second data set nor the cell phone data for the first data set were currently available.
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100 • Location of lane change: Timestamp where driver finished changing lanes, reduced from forward traversal video. • Driver ID: Driver information was not provided; the forward video was utilized to determine if the same car and therefore same driver was used in multiple traces; a unique identifier was given to each driver.
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101 timestamp within the time series data. As a result, the number of times a driver glanced at a particular location and the length of each glance could be determined.
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102 Independent variables included the following: • Time of day (day, night)
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103 The expected outcome is a difference in glance location when the sign is on compared to when it is off. As a result, the statistical model utilized needed to detect those differences as well as the impact of other covariates.
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104 Mixed-effects models are usually used to consider dependency introduced by countable groupings of variables, such as driver ID and Road ID. Regular (fixed effects)
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105 status. The estimates of the model are shown in Table 11 and demonstrate that standard deviation of speed is expected to be 1.1 times higher when the sign is active than when it is inactive or blank.
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106 Feasibility of the Data Set Utilized This analysis used a reduced data set from the SHRP2 NDS. Naturalistic driving studies are well suited for this type of analysis because driver behavior can be compared for similar roadway environments or, in some cases, across multiple traces from the same driver on the same roadway near and away from the infrastructure element of interest (and, as in the case of a DMS, when the element of interest is active or inactive)
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107 Sample Size As noted in Section 9.3, only 20 traversals were available for the glance location analysis and approximately 174 traces were available to model the other surrogates (i.e., average speed, standard deviation of speed, and standard deviation of lateral position)
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108 As noted in Section 9.3, lane position and steering wheel position variables were only available for a subset of the data used in this analysis and are only reliably available for a small subset of the SHRP2 NDS data as a whole. As a result, if lane position, standard deviation of lane position, or deviation in steering wheel position are used as surrogates for distraction, it would be necessary to request only those traces for which these variables are present and accurate.
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109 9.5.4 Discussion The objective of this Safety Framework was to assess the efficacy of using SHPR2 NDS data to evaluate distraction due to an overhead DMS. Although the sample size used for this analysis was small, the results indicate that drivers do glance differently and tend to vary their speed more when a DMS is active compared to when it is inactive.
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