Influence of Infrastructure Design on Distracted Driving (2022) / Chapter Skim
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Pages 82-95
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82 Chapter 8. Safety Framework to Assess the Impact of Railroad Crossings on Distraction Using the SHRP2 NDS Data 8.1 Introduction 8.1.1 Background This Safety Framework utilized SHRP2 NDS data.
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83 Tung (2014) conducted through the windshield observations at railroad crossings in Nebraska and found that approximately one-third (33.6%)
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84 • Rail-Highway Grade Crossings –Phase 3 (doi:10.15787/VTT1/3YVSY4)
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85 The team reviewed crashes and near-crashes in the InSight database, and 18 were found in the vicinity of railroads. However, none of the crashes/near-crashes appeared to be related to the railroad crossing.
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86 o Forward and rear video. o A limited number of distractions coded for several thousand traversals but not including glance location or type of distraction.
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87 8.4 Analysis 8.4.1 Variables Included The use of several dependent variables was investigated. Various dependent variables were considered because they can provide different information.
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88 proportion of time was used rather than actual time because the time each driver was within the influence area of the railroad crossing or the upstream control area differed. Other metrics to approximate distraction were also considered.
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89 area, and 20 observations were available for the control area. As the figure shows, drivers are glancing forward approximately 80% of the time in the influence area compared to around 74% in the control section.
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90 When the model was applied to the proportion of time drivers spent glancing at the left/right windshield within the influence area compared to upstream control area, the results were not statistically significant (a p-value of 0.1259)
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91 The results suggest that a difference between glance behaviors can be detected in the vicinity of railroad crossings. It should be noted that no trains or indications of an approaching train were present in any of the traversals included in the analysis.
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92 Roadway elements other than those specific to railroad crossings were obtained via Google Street View, which was sufficient for identifying general roadway characteristics. The number of railroad crossings included was limited, and, as a result, it was reasonably simple to locate the crossing and then confirm the accuracy of the information using forward video in the SHRP2 NDS traversals.
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93 Additionally, ArcGIS maintains a spatially located grade crossing inventory that originates from the FRA inventory. The ArcGIS data set also contains Amtrak stations and Amtrak routes (ArcGIS 2020)
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94 crossings, there would be more than sufficient additional locations that could be mapped to the SHRP2 NDS data to identify additional traversals. As a result, sufficient data are available to increase the sample size through the collection and reduction of additional data.
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95 • Driver behavior can be compared for roadway segments with and without the infrastructure element of interest (for instance, a segment upstream of an overhead changeable message sign and a segment in the vicinity of the sign)
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