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Influence of Infrastructure Design on Distracted Driving (2022)

Chapter: Chapter 11. Conclusions and Recommendations

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Suggested Citation:"Chapter 11. Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2022. Influence of Infrastructure Design on Distracted Driving. Washington, DC: The National Academies Press. doi: 10.17226/26550.
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Suggested Citation:"Chapter 11. Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2022. Influence of Infrastructure Design on Distracted Driving. Washington, DC: The National Academies Press. doi: 10.17226/26550.
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Suggested Citation:"Chapter 11. Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2022. Influence of Infrastructure Design on Distracted Driving. Washington, DC: The National Academies Press. doi: 10.17226/26550.
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Suggested Citation:"Chapter 11. Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2022. Influence of Infrastructure Design on Distracted Driving. Washington, DC: The National Academies Press. doi: 10.17226/26550.
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Suggested Citation:"Chapter 11. Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2022. Influence of Infrastructure Design on Distracted Driving. Washington, DC: The National Academies Press. doi: 10.17226/26550.
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Suggested Citation:"Chapter 11. Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2022. Influence of Infrastructure Design on Distracted Driving. Washington, DC: The National Academies Press. doi: 10.17226/26550.
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Page 131
Suggested Citation:"Chapter 11. Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2022. Influence of Infrastructure Design on Distracted Driving. Washington, DC: The National Academies Press. doi: 10.17226/26550.
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Suggested Citation:"Chapter 11. Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2022. Influence of Infrastructure Design on Distracted Driving. Washington, DC: The National Academies Press. doi: 10.17226/26550.
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Suggested Citation:"Chapter 11. Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2022. Influence of Infrastructure Design on Distracted Driving. Washington, DC: The National Academies Press. doi: 10.17226/26550.
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125 Chapter 11. Conclusions and Recommendations 11.1 Summary With the exception of billboards and general urban clutter, very few IRDs have been researched. In some cases, an IRD occurs when a driver focuses on an infrastructure element that is not needed for the driving task. For instance, a driver may glance at an aesthetic bridge or other roadside feature to the detriment of the driving task. In other cases, an IRD occurs when a driver fixates on an infrastructure element that is needed for the driving task but that requires more visual or mental attention than usual due to complexity of the situation. For instance, a changeable message sign may be difficult to read due to sun glare, causing the driver to focus longer than usual on the sign to read the information. Similarly, a driver may be distracted by an infrastructure element that, due to its angle or placement, creates a confusing roadway scene. For instance, a visual trap can be created when horizontal and vertical curvature are superimposed in a way that visually distorts the road’s direction. Although roadway infrastructure can create driver distraction, potentially resulting in crashes or SCEs, the interaction between infrastructure elements and driver distraction is not well understood. This project evaluated various methodologies for quantifying the relationship between distraction and infrastructure elements and created a series of templates for agencies to use in assessing the impact of a particular infrastructure element on distraction. Five different Safety Frameworks were developed, each of which addresses IRD using a different data set and research question(s). Due to the available project resources, the objective in developing these frameworks was to demonstrate the utility of different approaches for investigating IRD and to present a template for agencies and researchers to use in conducting similar investigations. As a result, the frameworks show how analyses can be conducted rather than describe robust analyses of the relationships between distraction and particular infrastructure elements. To broaden the understanding of the relationships between roadway or roadside infrastructure features and distraction, this project included the following specific activities: • Literature review: Studies that have identified the safety impact of OVDs as well as studies that have assessed the safety impact of specific infrastructure elements (e.g., billboards, railroad crossings) were identified and summarized. • Identification of potential available data sets: Databases (e.g., NDS, crash record) that may be useful for investigating IRDs were identified and evaluated to assess their advantages and disadvantages for use in examining the relationship between distraction and infrastructure elements. • Development of Safety Frameworks: Five different frameworks were developed that provide a structure for using the identified data sets to evaluate the association between distracted driving and infrastructure elements. • Development of guidance: Each Safety Framework provides guidance for stakeholders interested in addressing IRD crashes using a particular type of data.

126 11.2 Key Findings and Recommendations A summary of the key findings for each of the Safety Frameworks developed is provided in this section. 11.2.1 Safety Framework for the Use of Crash Narratives This Safety Framework assessed whether crash narratives can be utilized to identify IRDs. Crash narratives may supplement the data derived from a crash report form with additional information about the types of distraction that may have been present in a crash. The crash report forms for most states include a field for distractions that have a source outside the vehicle, though the cause of the OVD is not typically provided. Crash data for Michigan, which include crash narratives, are publicly available through the MTCF data query tool. Fatal and injury crashes coded as involving an OVD were obtained from this source for 2017 through 2019, resulting in 428 crash narratives that were manually reviewed to identify the type of distraction present. The majority (56%) provided no indication of the object that led to the officer to code the crash as involving an OVD and instead suggested that the crash may have been miscoded. An outside object (e.g., person or animal) was noted for 43% of the crash narratives, but no infrastructure element was indicated. Only five narratives (1%) indicated an infrastructure element (three outdoor advertising displays, one streetlight, and one fire hydrant). Although the use of crash narratives has the potential to pinpoint actual distractions, several drawbacks to this method exist. The evaluation suggests that while officers do code OVDs, they are not likely to code the contribution of infrastructure items. However, there was some evidence that officers note nonfunctioning infrastructure as a likely contributor (e.g., a broken fire hydrant). The results of the crash narrative evaluation also suggest that it is unlikely that officers consider the presence of a normal infrastructure feature to have a negative impact on driver attention. Another drawback to the use of crash narratives is that significant resources are needed to obtain a sufficient sample size. Many agencies preserve crash narratives in a format, such as scanned pdf or image, that cannot be electronically searched. In addition, crash narratives may be difficult to access due to the potential for exposing personal identifying information. As a result, the conclusion of this analysis is that use of crash narratives to identify and analyze IRD crashes is feasible but is likely to be very resource-intensive. The best use of crash narratives to identify IRD would likely be to evaluate crash narratives in the vicinity of a particular infrastructure element, particularly one that is likely to be a distractor (e.g., wind turbines, community entrance signs/art installations, sidewalk art). 11.2.2 Safety Framework for the Use of Crash Data An analysis was undertaken to determine whether existing motor vehicle crash reports are a viable source of information about the role of IRDs in crash causation. The objective was to develop a Safety Framework that agencies could use to assess the impact of an infrastructure

127 element on distraction and then test the efficacy of the approach. This Safety Framework used data from a wind turbine data set and Iowa crash and roadway data sets. The use of data from Iowa provided a unique opportunity for this analysis because nationally Iowa ranks among the top states in wind energy production and the team already had access to the Iowa crash and roadway data. Three different analyses were attempted. First, a set of rural roadways (nonfreeway) in Iowa was selected within 4 miles of a wind turbine, along with a control set of similar roadways. Lane departure crashes for 2018 were extracted because they would be the most likely to result from a distraction along a rural roadway segment, but only a total of 16 crashes over 550 miles was found. As a result, a crash analysis could not be conducted with these data. The second analysis compared segments of I-80 across Iowa that were within 4 miles of a wind turbine to segments that were not near a wind turbine. A Poisson-lognormal model was developed. The results showed that crashes were higher on segments near wind turbines, but the results were not conclusive. Only 2 years of crash data were included in the analysis. A large number of wind turbines were installed between 2017 and 2019, so obtaining additional years of previous data would not have yielded a larger sample. Moreover, because a large number of wind turbines were installed in 2019, a before-and-after analysis was not feasible due to the short after period, though such an analysis could be accomplished with a few more years of data. The analysis does show some indication that the presence of wind turbines increases crash risk, but this finding would need to be further explored. Using crash data from 2019, the third analysis geospatially linked crashes in Iowa that were coded as involving an OVD to the presence of wind turbines. Proximity matching was conducted in ArcMap, and the presence of a wind turbine within 2 miles was noted for each crash. An OVD crash was 1.3 times more likely to occur (CI = 1.01, 1.70) in locations within 2 miles of a wind turbine than in locations with no nearby wind turbines, and the results are statistically significant. Although this was a simple analysis, it suggests that officers may be more likely to code an OVD near an unusual infrastructure feature. Although the analyses conducted to demonstrate the efficacy of using crash data to identify IRDs were not strongly conclusive, the use of crash data for this purpose appears to be feasible. The results of the analyses using crash data suggest the following strengths, challenges, and recommendations regarding the use of crash data for the evaluation of IRDs. Strengths include the following: • Crash data are an actual measure of safety. • Crash data are readily available in most states, and, as a result, significant effort for data collection is not needed. • When differences in data collection and reporting are properly accounted for, data can be combined for different geographic areas (e.g., states, jurisdictions), which increases the sample size available. This is particularly useful when evaluating the impact of rare infrastructure items. Challenges include the following:

128 • As noted previously, except for unique infrastructure elements, crashes coded as involving an OVD do not appear to be a good indicator of IRD, making it difficult to determine whether a crash in the vicinity of a particular roadway element is related to that element. Without indication of an OVD, a crash can only be inferred to be related to the element. • If a correlation is shown to exist between crashes and a roadway element, it is challenging to determine whether distraction was the likely cause of those crashes because other factors could be present that contributed to the increased number of crashes. For instance, a relationship between overpasses and crashes could be due to distraction or other factors, such as ice or snow lingering under overhead structures due to low sun exposure. Bridges may also attract some types of animals, which could also be a contributing factor in an elevated crash rate. Recommendations for using a crash analysis to assess the impact of infrastructure on distraction include the following: • Evaluation of IRDs using crash analyses is best suited to rural areas. Since it is difficult to attribute a crash to a particular infrastructure element, it is necessary to study areas where the number of overlapping roadway elements is minimized. • The use of crash data to assess IRDs is likely best suited for situations where an infrastructure element is either isolated or uncommon. For instance, elements such as overpasses, wind turbines, community entrance signs/art installations, and so on can be clearly isolated from other features that might also be distracting. • A before-and-after analysis is also a recommended method for evaluation of IRDs. Crashes before and after the infrastructure element is in place can be compared. In this manner, the main difference between the two sets of crashes is the presence of the feature. This method is much more likely to show a difference (if one exists) than a cross-sectional analysis. 11.2.3 Safety Frameworks for the Use of NDS Data Two different Safety Frameworks were developed to assess the use of the SHRP2 NDS data to evaluate the impact of infrastructure elements on distraction. The results could be extrapolated to the use of NDS data in general. The analyses for both frameworks used curated data sets available from the SHRP2 NDS website, InSight (https://insight.shrp2nds.us/). The first Safety Framework assessed driver glance behavior using 20 traversals collected upstream of railroad crossings. Due to the small sample size, a Wilcoxon signed rank test for paired differences was used to compare glance behavior within an influence area 200 ft from the railroad crossing to glance behavior within a section upstream that was used as a control. The model indicated that the proportion of time drivers glanced at the forward windshield was higher and statistically different within the influence area, while the results were not significant for the proportion of time glancing at the left or right windshield. The difference identified for the proportion of time drivers spent glancing at the forward windshield suggests that drivers focus more on the forward roadway when approaching railroad crossings. The second Safety Framework evaluated the impact of overhead DMS on distraction. The data set used in the analysis included traversals on freeway sections featuring overhead DMS that were at times active and at other times inactive. Twenty traversals (10 for each sign status

129 [on/off]) coded for glance location and visual distractions 800 ft upstream of the DMS were reduced, and glance location was used as a surrogate for distraction. Due to the limited sample size, a gamma regression model was used to model the percentage of time a driver glanced at the forward roadway within 800 ft of the DMS. The model indicated that the proportion of time drivers spent glancing at the forward windshield was higher and statistically different when the sign was on compared to when the sign was off. The difference suggests that drivers focus more on the forward roadway when an overhead DMS is active. Additional data for the second Safety Framework were available in the vicinity of the signs, and models of average speed and lateral position were also created but were unable to capture the influence of DMS status. This may have been due to the small sample size as well as confounding factors that could not be controlled for due to the data set utilized, such as congestion and movement (entering, exiting, through). It was found that the presence of an active DMS correlated to an increase in driver speed variation. Although not conclusive, the results suggest a difference between glance behavior and a potential increase in speed variation when an overhead DMS is active compared to when it is inactive. The main advantages of using the SHRP2 or other NDS data for the evaluation of IRD include the following: • Driver behavior can be linked to actual events (normal or safety critical). • Some curated SHRP2 NDS data sets are available that may reduce costs in accessing data. • Various surrogate measures can be used, including glance location, lateral position, and speed. • 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). The main disadvantages include the following: • Access to the SHRP2 NDS data is costly if the use of curated data sets is not feasible. • Only passenger vehicles are included in the SHRP2 NDS data. • Finding events of interest can be a “needle in a haystack” problem. Use of the SHRP2 NDS data for an analysis of IRD is likely best suited to infrastructure elements that are easily identified and available in an existing database such as the RID, a state roadway database, or another data source. Such infrastructure elements include bridges, overpasses, directional signing, and, when sufficient information is available, railroad crossings or wind turbines. Finding other elements of interest would be more challenging. The SHRP2 NDS data could also be used to assess the impact of roadway design (such as combinations of horizontal and vertical curvature) on distraction by comparing glance behavior in different scenarios. 11.2.4 Safety Framework for the Use of Simulator Data A large archival database of driving simulator data from the University of Iowa’s NADS was utilized to develop a Safety Framework for evaluating the relationship between distraction and

130 infrastructure features. Eye tracking and driving performance data from a sample of 72 drivers were analyzed to determine whether evidence exists linking five external roadway features to visual attention and driving performance. The infrastructure features included four billboards (aggregated together), a highway exit sign, an overhead sign, an overpass sign, and a route sign. First, glances made toward each of the five external roadway features were evaluated. The results of the analysis suggest that drivers, on average, made few glances to these roadway features. Furthermore, these glances tended to fall below established thresholds for distraction, both in terms of the average glance length and the total glance duration. Drivers were most likely to glance at the billboards and the highway exit sign. Additionally, the overpass sign received the longest average glance time of 2.21 seconds, followed by 2.0 seconds for the highway exit sign. A second set of analyses focused on comparing measures of visual attention and driving performance between epochs that contained the external roadway features and baseline epochs absent of the external roadway features. PRC gaze was statistically different for only the highway exit sign. Horizontal gaze dispersion was statistically different for all of the features except billboards. Vertical gaze dispersion was statistically different for the overpass sign and route sign. No differences in standard deviation of speed were noted for any of the signs, while standard deviation of lane position was statistically different for the highway exit sign. While the differences identified in these two analyses were small, they do indicate that differences in driver behavior for different roadway features can be determined. For instance, drivers spent more time looking at and had more frequent glances toward certain features. Other metrics such as PRC gaze and gaze dispersion also showed differences between some features, though the differences were small. However, this result was not unexpected because drivers may have focused on the locations of certain features to guide route planning decisions within the drive. Further, a reduction in gaze dispersion alone does not represent evidence in favor of visual distraction. The results of this exploratory analysis suggest that the five roadway features evaluated for this Safety Framework have limited distraction potential, especially when viewed in comparison with in-vehicle, visual-manual distractions such as texting on a cell phone. The features were all common roadway signing; with the exception of billboards, it is not unusual that strong evidence was not found. As noted in Chapter 10, other infrastructure elements that may have yielded different results could not be evaluated due to their overlap with other features within the roadway environment and the complexity of the driving environment in the urban driving segment. Nevertheless, the results do indicate that simulator studies can show differences in behavior. A planned simulator study may be the best method to determine distraction for a particular infrastructure element. The advantage to a simulator study is that it can be designed to specifically include the element, and then distraction measures such as glance location can be collected. For instance, the analyses conducted using SHRP2 NDS data indicated that drivers fixated glances toward the center roadway when encountering railroad crossings. However, it was not possible to determine specifically what the driver was looking at. In a simulator study, scenarios with and without railroad crossings could be included, and, using eye tracking

131 equipment, the amount of time drivers spent glancing at the crossing features could be determined. 11.2.5 Distraction Surrogates The topic of distracted driving within the human factors community has coalesced on visual distraction, which is an activity that requires a driver to divert his or her gaze from the road to acquire information from the distracting source (Campbell et al. 2016, NHTSA 2014). A glance duration of 2 seconds away from the driving task has been shown to have a correlation to crash risk by several researchers. This duration is primarily related to internal distractions such as texting. However, applying traditional measures of distraction to identifying IRDs is challenging. One challenge is that the amount of time that a driver is able to focus on a particular infrastructure element is limited. For instance, the MUTCD states that a static sign, such as a railroad crossing sign, is visible to approaching drivers at around 180 ft. At 40 mph, the sign is only visible to drivers for 3 seconds. It is difficult in any data set to identify long glances away from the driving task when the window of opportunity to view the sign is so short. It is similarly difficult to identify longer glances in simulator data sets due to the short length of driving activity as well as the inability for simulators to fully replicate scenarios that result in driver distraction. As a result, it is unlikely that glances of 2 or more seconds could be used as a threshold to indicate distraction. Another challenge in assessing distractions related to infrastructure is that, in most cases, drivers glance at infrastructure features in the same location as they look when performing regular driving tasks. This makes it difficult to differentiate when a driver is glancing at the roadway in order to attend to the driving task or fixating on an infrastructure element. Moreover, some researchers have noted that cognitive distraction can cause drivers to concentrate their gaze in the center of the driving scene. This was reinforced in the present study by both analyses of SHRP2 NDS data sets. Drivers approaching both railroad crossings and DMS had a higher PRC gaze compared to the respective baseline epochs. Due to the challenges of using common visual distraction metrics, it may be necessary in some cases to utilize surrogate measures in order to assess the impact of infrastructure on distraction. These measures include the following: • Glances at forward roadway scene (may suggest fixation on an infrastructure element in the forward roadway scene): o PRC gaze: Percentage of time when gaze is directed to the forward roadway within the epoch. o Number of glances away: Number of glances away from the forward roadway within an epoch. • Glances toward specific infrastructure element (when infrastructure elements can be differentiated from the forward roadway scene): o Number of glances: Total number of glances to each element. o Average glance time: Average duration of glances to the element. o Total glance time: Total eyes-off-road time associated with each element.

132 o Horizontal gaze dispersion: Horizontal dispersion of gaze (in degrees). o Vertical gaze dispersion: Vertical dispersion of gaze (in degrees). • Lateral control (which indicates cognitive workload): o Standard deviation of lane position: Indicates variation within lane. o Lane excursions: Number of times vehicle crosses lane line. • Speed control (which indicates cognitive workload): o Mean speed: Average speed for vehicle within an epoch. o Standard deviation of speed: Indicates variations in speed. o Acceleration/deceleration: Measure of acceleration or deceleration that exceeds threshold of normal behavior. 11.2.6 General Recommendations Recommendations specific to the various data sets evaluated in this research are provided in Chapters 6 through 10. The following are general recommendations and an overall summary: • Analyses conducted in rural areas may be more likely to detect differences in driver behavior due to a specific infrastructure feature than analyses conducted in urban areas. This is because in urban areas it is difficult to isolate external features for the purpose of measuring glances in data sets such as NDS data. Comparisons of speed and lateral position metrics are similarly difficult in urban areas because it is difficult to select a baseline or isolate the impact of one element versus all of the other features present. • A controlled simulator experiment is likely the most effective way to assess the impact of infrastructure elements on distraction. A simulator experiment allows features to be placed so that their impact can be isolated and allows more specific measures of glance location to be identified than in other types of studies. A simulator study can also allow classical measures of cognitive workload to be tested, such as asking drivers to complete tasks while in the vicinity of the infrastructure element being evaluated. • NDS data sets are also useful for assessing impacts of infrastructure elements on distraction because they can be used to evaluate drivers’ natural reactions to infrastructure elements. An analysis of this type of data set is most likely to yield good results when specific infrastructure elements can be isolated (e.g., railroad crossings or wind turbines) or when a segment upstream of a feature of interest (e.g., a changeable message sign) can be used to compare driver behavior within and outside the feature’s influence area. 11.3 Future Research Needs The objective of BTS-09 was to develop Safety Frameworks that could be used by other researchers and practitioners to assess the impact of infrastructure elements on distraction. Resources were not available and the objective was not to conduct a full investigation for a particular infrastructure element. As a result, available data sets were assessed to determine whether they were sufficient for such a task, and preliminary analyses were conducted to assess the efficacy of statistical approaches. Several of the preliminary analyses suggested that IRDs were present, but due to low sample sizes the results were not conclusive. The following future research needs were formulated based on project outcomes:

133 • Conduct further analyses to select the most relevant measures of distraction for IRD: As noted throughout this report, classical measures of distraction, such as glances of 2 or more seconds, are not entirely applicable to the evaluation of distraction related to infrastructure elements. As a result, additional evaluation to identify the measures that yield the best results could be undertaken. • Distinguish normal and distracted fixation: In several analyses, it was noted that drivers focused their attention in the direction of a certain roadway feature (e.g., changeable message sign). In the classical definition of distraction, focusing on the roadway task would be positive. However, focusing on a single element of the roadway may, in reality, be distracting if it takes attention away from other aspects of the driving task. For instance, drivers focusing on a changeable message sign may not be mindful of other vehicles around them. As a result, research is needed to differentiate between paying the proper amount of attention to a roadway element versus overfocusing on an element. • Further investigate the impacts of wind turbines on distraction: Although not conclusive, the results of the analysis described in Chapter 7 suggest that wind turbines may create distractions. Additional research into wind turbines could provide information that can be used to site wind turbines safely, particularly along high-speed facilities. • Further investigate the impacts of DMS on distraction: The results of the analyses described in Chapter 9 also suggest that DMS status (on/off) may impact the way drivers focus their attention. Additional research, potentially in a simulator, could be used to detect whether different message combinations, amounts of text, or message contents have different impacts on driver distraction. Placement of DMS (overhead versus side mounted) could also be evaluated to determine whether one or the other is more distracting. • Address the place of external distraction in the continuum of distractions: Research to address where external distraction ranks among the continuum of distractions may also be useful. For instance, determining the relative danger of external distraction versus the danger of internal distraction (e.g., texting while driving) can be used to determine how much focus should be placed on studying external distraction. • Identify the specific needs of agencies: Research involving a focus group is suggested to identify concerns agencies have in terms of external distractions. Identifying the primary concerns can assist in focusing available resources. • Examine the relationship between internal/external distractions: Characterizing the relationship between external and internal distractions may be useful. In this regard, the research question is whether drivers are more or less inclined to engage in internal distractions in the presence of certain infrastructure elements (e.g., narrow, curvy roads; bridges; tunnels). For instance, drivers encountering a confusing directional roadway sign may utilize their GPS to check their location. Alternatively, drivers focused on an unusual feature may be less likely to text.

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While many studies have focused on driver distractions such as cell-phone use, the impact of infrastructure elements on distraction and the extent to which they may cause distraction has not been well studied. Examples include objects that are unusual (such as aesthetic bridges) or confusing (signage or markings) or that require an unusual amount of time to locate (like a specific wayfinding sign among multiple roadside objects).

The TRB Behavioral Traffic Safety Cooperative Research Program's BTSCRP Web-Only Document 1: Influence of Infrastructure Design on Distracted Driving provides an opportunity to develop a better understanding of the interaction between the built environment and driver distraction.

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