Measuring Minnesota's Traffic Safety Culture
Measuring Minnesota’sTraffic Safety CultureNicholas Ward, Principal InvestigatorWestern Transportation InstituteMontana State UniversityApril 2015Research ProjectFinal Report 2015-13
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Technical Report Documentation Page1. Report No.2.3. Recipients Accession No.MN/RC 2015-134. Title and Subtitle5. Report DateMeasuring Minnesota's Traffic Safety CultureApril 20156.7. Author(s)8. Performing Organization Report No.Nicholas Ward, Jay Otto, Steve Swinford, and John Borkowski9. Performing Organization Name and Address10. Project/Task/Work Unit No.Western Transportation InstituteMontana State University2327 University Way, Suite 6Bozeman, MT 5971711. Contract (C) or Grant (G) No.(c) 99006 (wo) 112. Sponsoring Organization Name and Address13. Type of Report and Period CoveredMinnesota Department of TransportationResearch Services & Library395 John Ireland Boulevard, MS 330St. Paul, Minnesota 55155-1899Final Report14. Sponsoring Agency Code15. Supplementary Noteshttp://www.lrrb.org/pdf/201513.pdf16. Abstract (Limit: 250 words)The goal of this study was to develop a survey methodology for Minnesota to measure state-level traffic-safety culture withthree objectives:1. Index traffic-safety culture as a performance indicator.2. Identify culture-based strategies to achieve safety targets.3. Assess receptivity of social environment for planned strategies.The survey was designed around a definition of safety culture as “the socially constructed abstract system of meaning, norms,beliefs, and values.” (Myers et al., 2014; Reiman & Rollenhagen, 2014) Given that the majority of cases in which driverbehavior is associated with fatal crashes can be presumed to be deliberate, these cognitions determine the intention to behavein either a safe or risky manner. This definition emphasizes that cognitions – shared by a group of people – influence thebehavioral choices of the individual group members.The survey was designed to be implemented using a paper instrument mailed to households in Minnesota. The sample wasbased on a random sample of 10,000 Minnesota household addresses selected in November 2013 to cover all AreaTransportation Partnership (ATP) boundary areas.The results indicate that many aspects of Minnesota are predictive of the level of engagement in both risky and protectivebehaviors. Often, the misperception that risk-taking is common and accepted increased the probability that individuals woulddecide to engage in risky behaviors themselves.The results are used to recommend strategies to increase concern about traffic safety overall and to reduce the incidence ofrisky behaviors in favor of safer choices.17. Document Analysis/Descriptors18. Availability StatementTraffic safety, Culture (Social sciences), Seat belts, Speeding, DrunkdrivingNo restrictions. Document available from:National Technical Information Services,Alexandria, Virginia 2231219. Security Class (this report)20. Security Class (this page)21. No. of PagesUnclassifiedUnclassified11822. Price
Measuring Minnesota's Traffic-Safety CultureFinal ReportPrepared by:Nicholas Ward, Principal InvestigatorJay OttoSteve SwinfordJohn BorkowskiWestern Transportation InstituteMontana State UniversityApril 2015Published by:Minnesota Department of TransportationResearch Services & Library395 John Ireland Boulevard, MS 330St. Paul, Minnesota 55155-1899This report represents the results of research conducted by the authors and does not necessarily represent the viewsor policies of the Minnesota Department of Transportation, Montana State University, or the Western TransportationUniversity. This report does not contain a standard or specified technique.The authors, the Minnesota Department of Transportation, and Montana State University do not endorse products ormanufacturers. Trade or manufacturers’ names appear herein solely because they are considered essential to thisreport.
AcknowledgmentsOur project team would like to thank Sue Groth and Brad Estochen for their support in makingthis research possible, Katie Fleming for her leadership and understanding, and the members ofthe Technical Advisory Panel (TAP), Donna Berger, Peter Buchen, Kristine Hernandez, JeffreyO’Brien, Karla Rains, Alan Rindels, Shirlee Sherkow for their insights and support. We are alsograteful to the independent reviewers who provided valuable feedback on this report PeterKissinger, Wes Lum, and Barry Watson.
Table of ContentsChapter 1 Introduction . 11.1 Traffic-safety culture . 11.2 Overview of Report. 2Chapter 2 Survey Development and Implementation . 32.1 Survey Development . 32.1.1 Section One: General Attitudes and Beliefs about Traffic Safety . 32.1.2 Section Two: Specific Attitudes and Beliefs as Determinants of Selected RiskyBehaviors. . 42.2 Survey Implementation . 92.3 Survey Sample . 92.4 Survey Validation . 102.4.1 Concurrent Validity: Comparison to Other Measures . 102.4.2 Criterion Validity: Integrated Behavioral Model (IBM) . 11Chapter 3 Analysis . 153.1 Overview of Analyses . 153.2Frequency Response Analysis . 153.2.1 Overview . 153.2.2 Process . 163.2.3 Results . 163.3 Predictive Models . 173.3.1 Overview . 173.3.2 Processes . 183.3.3 Results . 233.3.4 Summary . 253.4 Actual and Perceived Norms . 293.4.1 Overview . 293.4.2 Process . 303.4.4 Results . 303.4.5 Summary . 333.5 Unified Metric . 34
3.5.1 Overview . 343.5.2 Process . 343.5.3 Summary . 38Chapter 4 Conclusion and Recommendations . 394.1Conclusion. 394.2Recommendations . 404.2.1General Strategies . 404.2.2Driving after Drinking . 424.2.3Seat Belt Use . 434.2.4Speeding. 444.3Next Steps . 45References . 47Appendix A: SurveyAppendix B: Survey LettersAppendix C: Frequency ResponseAppendix D: Detailed Overview of CART AnalysisAppendix E: Comparison of Mean Values of Responses Based on Number of Risky DrivingBehaviorsAppendix F: Cultural Factors: Family Rules and Workplace Policies and Risky Behaviors
List of FiguresFigure 1.1 Traffic safety performance in Minnesota measures in terms of traffic fatalities. . 1Figure 2.1 Illustration of Integrated Behavior Model (IBM) that was used as framework fordevelopment of survey (adapted from Glanz, Rimer, & Viswanath, 2008). . 5Figure 2.2. Distribution of selected demographics in final sample of returned surveys. . 10Figure 3.1. Percentage of respondents indicating perceptions of "very concerned" about trafficsafety. . 17Figure 3.2. Interpreted classification tree for drinking and driving (Q12a). 24Figure 3.3. Interpreted classification tree for seat belt use (Q25a). . 26Figure 3.4. Interpreted classification tree for speeding (Q40a). . 28Figure 3.5. Actual Norms and Percentage of Respondents Misperceiving the Norm. . 32Figure 3.6. Risk ratios based on perception of descriptive norms (* .05). . 33Figure 3.7. Cultural Factors Related to Driving After Drinking. 35Figure 3.8. Cultural Factors Related to Seat Belt Use . 35Figure 3.9. Cultural Factors Related to Speeding . 36Figure 3.10. Concern for Traffic Safety Based on Engagement in Risky Behaviors . 37Figure 4.1. Example of using positive images to promote safe behaviors. . 42Figure 4.2. Example of positive messaging to communicate actual norm of safe behavior. 43Figure 4.3. Example of leveraging cultural values with desired behavior. 44Figure 4.4 Depiction of “Speed Lottery” representation of automatic speed enforcement(thefuntheory.com, 2009). 45Figure 4.5. A program of integrated strategies across the social network (based on Allegrante,Hanson, Sleet, & Marks, 2010). 46List of TableTable 2.1 List of Survey Questions for each Specific Behavior to Measure the Contribution ofeach Component of the Integrated Behavior Model (IBM). . 7Table 2.2. Comparison of Survey Self-Reported Behaviors and Other Independent Sources ofReported and Observational Data. . 11Table 2.3. Correlations of IBM Components and Criterion Behavior of Driving after Drinking:“During the past 60 days, on how many occasions did you drive within two hours of drinkingany alcohol? (0, 1, 2, 3, 4, 5, 6 or more times).”. 12Table 2.4. Correlations of IBM Components and Criterion Behavior of Wearing Seat belt:. 13Table 2.5. Correlations of IBM Components and Criterion Behavior of Speeding: . 14Table 3.1. Reporting of Top Three Ranked Transportation Issues. . 16Table 3.2.Common Set of Predictor Variables for Classification Tree Analyses. . 19
Table 3.3. Specific Set of Predictor Variables for Drinking and Driving Classification Tree. . 19Table 3.4.Specific Set of Predictor Variables for Seatbelt Classification Tree. . 20Table 3.5.Specific Set of Predictor Variables for Speeding Classification Tree. . 21Table 3.6.Correlation of Important Variables with Driving and Drinking Behavior (Q12a). . 24Table 3.7.Correlation of Important Variables with Seat Belt Behavior (Q25a). . 26Table 3.8.Correlation of Important Variables with Speeding Behavior (Q40a). . 28Table 3.9. Strength of Relationship (Correlation) between Own Response (Actual Norm) andPerceived Response of Others (Perceived Norm). . 31Table 3.10. Percentage of Respondents Classified as Engaging in each Risk Behavior (N 3204). . 34Table 3.11. Percentage of Respondents Classified as Engaging in Multiple Risky Behaviors (N 3204). . 36Table 3.12. Categorical Representation of Survey Sample based on Concern for Traffic Safety. 37
Executive SummaryCurrently, MnDOT analyzes performance in terms of outcome variables such as number oftraffic fatalities. This project supplements this approach by providing a survey tool to measurethe traffic-safety culture of Minnesota drivers that is presumed to underlie the behaviors thatresult in these crash fatalities.The goal of this study was to develop a survey methodology for Minnesota to measure state-leveltraffic-safety culture with three objectives:1. Index traffic-safety culture as a performance indicator2. Identify culture-based strategies to achieve safety targets3. Assess receptivity of social environment for planned strategiesThe survey was designed around a definition of safety culture as “the socially constructedabstract system of meaning, norms, beliefs, and values” (Myers et al, 2014; Reiman &Rollenhagen, 2014). Given that the majority of cases in which the driver behavior associatedwith fatal crashes can be presumed to be deliberate, these cognitions determine the intention tobehave in either a safe or risky manner. This definition emphasizes these cognitions – shared bya group of people – influence the behavioral choices of the individual group members.The integrated behavior model (IBM) was used to operationalize this cognition-based definitionof traffic-safety culture and guide development of the survey to measure the culturaldeterminants of each risky driving behavior. As shown in Figure 0.1, this model integratesseveral theories that predict intentional behaviors as well as other important concepts such asknowledge and skills, salience, environmental constraints, and habits (Glanz, Rimer, &Viswanath, 2008).Figure 0.1. Illustration of Integrated Behavior Model (IBM) that was used as framework fordevelopment of survey (adapted from Glanz, Rimer, & Viswanath, 2008).
The survey was implemented using a paper instrument mailed to households in Minnesota. Thesample was based on a random sample of 10,000 Minnesota household addresses selected inNovember 2013 to cover all Area Transportation Partner (ATP) Boundary areas.In an effort to better understand traffic-safety culture in Minnesota, four separate analyses wereperformed on the survey data:1. Frequency Response: This includes an analysis of the frequency and distribution ofsurvey responses about general traffic safety in general.2. Predictive Models: This analysis examines the best predictors of the self-reported riskybehaviors.3. Actual and Perceived Norms: This analysis examines the particular misperceptionsabout cultural norms and their association with an increase in reported propensity toengage in risky driving behaviors.4. Unified Metric: This analysis sought to identify a single score metric of traffic-safetyculture. The intent of this analysis was to provide an intuitive metric that could be usedto track traffic-safety culture along with measuring trends in traffic fatalities inMinnesota.A fundamental challenge for Minnesota to reach its goal of zero traffic fatalities is the fact that asignificant percentage of its population does not perceive traffic safety to be the most importanttransportation issue. While some (34%) ranked traveler safety in the top three most importanttransportation issues, two-thirds did not. Moreover, nearly 40% of respondents did not agreethat zero was the only acceptable number of fatalities on Minnesota roads. Fortunately, there isstill concern among Minnesota residents about traffic safety. Indeed, one-third of respondentssaid they were “extremely concerned” about traffic safety. Driving after drinking was reported less frequently by those who: Made plans to not drive after drinking Felt that it was wrong to drive after drinking Said that other people important to them felt it was wrong to drive after drinking Recognized that impairment begins with the first sip of alcoholSeat belt use was more frequent among those who: Felt responsible as a driver to ensure all passengers wore seat belts Want the people they care about to wear a seatbelt Had people they cared about who want them to wear a seat belt Habitually think about the need to wear a seat belt Believed it was important to protect themselves.Speeding was reported less often among those who: Did not feel speeding to be enjoyable Felt people should not speed and believed most other people felt the same way F
culture. The intent of this analysis was to provide an intuitive metric that could be used to track traffic-safety culture along with measuring trends in traffic fatalities in Minnesota. A fundamental challenge for Minnesota to reach its goal of zero traffic fatalities is the fact that a
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It's more than just a slogan Kristine Hernandez, MBA . 2013‐2015: Minnesota Baseline established for Traffic Safety Culture & Update Strategic Highway Safety Plan 2016‐2019: Applying Traffic Safety Culture in Minnesota & Imp
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(National Highway Traffic Safety Administration, 2016) Up from 32,675 in 2014 (National Highway Traffic Safety Administration, 2016) Traffic fatalities among older adults (age 65 and older) in 2012: 5,560 killed 214,000 injured (National Highway Traffic Safety Administration, 2013a) Traffic Fatalities and Injuries .
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