Evaluating the Long Term Effects of Saskatchewan’s Legislation Banning the Use of Hand-held Cell Phones while Driving in Reducing Distracted-Driving Related Collisions Predictive Modeling to Evaluate Carrier Safety Performance in Saskatchewan This study looked at the long-term effects of Saskatchewan’s legislation banning hand-held cell phones while driving. In the following slides, I will provide the situation leading to the coming into effect of the legislation and objectives of the research. I will talk a little bit about the data used in the study, the analytical approach and the findings. Rajib Sahaji George Eguakun Cari Donaldson Rajib Sahaji and George Eguakun Saskatchewan Government Insurance June 7, 2016 June 03, 2014

OUTLINE Problem Statement Objectives Study Data Model Specification Results Conclusions Practical Implications

BACKGROUND The use of hand-held cell phones while driving is widely accepted as one of the major distraction inducing actions Research indicate that distracted driving, such as texting while driving, has a potential collision risk that is 23.2 times as high as that for non-distracted driving Saskatchewan enacted the legislation banning the use of hand-held cell phone or any other electronic device while driving in January 2010 Assuming that hand-held cell phones are one of the major contributing factors to distracted driving, it is expected that the legislation would contribute to the reduction of collisions associated with driver distraction To date, formal evaluations of the long-term effects of the legislation banning cell phone use while driving in Saskatchewan and other Canadian jurisdictions have not been conducted Saskatchewan introduced legislation banning cell phone use while driving in Jan 2010 in response to the rising importance of distraction driving as a contributory factor to collisions.

OBJECTIVES OBJECTIVES Develop a set of predictive models to identify which past violation types, at the aggregate level (i.e., at-fault collisions, convictions, out-of-service (OOS) inspections), significantly predict a carrier’s involvement in future at-fault collisions Identify the specific types of collisions, convictions and OOS inspections that warrant attention when reviewing a carrier’s safety performance To investigate whether distracted driving-related crash risk increased or decreased in the period following implementation of the legislation relative to the period prior to implementation To investigate which age groups—young drivers versus more experienced drivers--were most impacted by the legislation over the period under evaluation. How has the legislation impacted distracted-related collisions over time? Is the effect more pronounced among any particular age group? 3

Independent Variables STUDY DATA STUDY DATA Analysis included 3,250 commercial truck operators which were in active operations between 2008 and 2011 At-fault collisions were divided into at-fault property damage only (PDO), and casualty (injury plus fatality) collisions Convictions and OOS inspections were categorized into 15 conviction types and 8 inspection types For the intervention analyses, distracted driving (DD) related collisions were tracked for a total of 113 months: 60 months (2005-2009) of observations before the law came into effect (i.e., January 2010), and 53 months (January 2010 – May 2014) of observations following the implementation of the legislation Monthly counts of DD related property damage only (PDO) and casualty collisions were used as the dependent variables in the modeling effort Independent Variables Variable Labels Description Intervention Before Before cell phone ban legislation After* After cell phone ban legislation Gender F Female drivers M* Male drivers Age Group 16-24 Drivers aged 16 to 24 25+* Drivers aged 25 and over Unemployment Rate UR Monthly unemployment rate * The asterix indicate the reference variables in the modeling effort. 4

MODEL SPECIFICATION METHODOLOGY Binary logistic regression technique Aggregate level model (one model) Dependent Variable: Involvement in a future casualty collision – a binary variable (1 or 0) Independent Variable: Numbers of all at-fault collisions, convictions, and OOS inspections Disaggregate level model (three models) Independent Variable: At-fault PDO and casualty collisions; 15 conviction types; and 8 OOS inspection types A statistical before and after approach was used to determine whether the legislation had long-term effects on the rate of distracted driving- related collisions in the Province. This study used the negative binomial regression technique, along with generalized estimating equations (GEE) to measure the impact of the legislation. The GEE technique was used to accommodate correlated observations within the study data. The models were developed to investigate the effects on All distracted driving collisions; Property Damage Only Collisions in the same category; and Casualty Collisions resulting from distracted driving. For each model, an intervention variable was introduced such that the before periods were assigned “0” and the period following implementation “1”. The parameter estimate associated with the intervention variable indicates the magnitude and direction of the effect. Natuarrly, a negative parameter will indicate a reduction in the response variable over time. In the following sections, the results of the modeling efforts will be presented first for effects on overall collions, followed by Property Damage only and the Casualty collisions. 5

RESULTS MODEL SPECIFICATION Binary logistic regression technique Aggregate level model (one model) Dependent Variable: Involvement in a future casualty collision – a binary variable (1 or 0) Independent Variable: Numbers of all at-fault collisions, convictions, and OOS inspections Disaggregate level model (three models) Independent Variable: At-fault PDO and casualty collisions; 15 conviction types; and 8 OOS inspection types Results for Total DD Related Collisions Parameter estimates Effect Level Estimate Standard Error 95% Confidence Limits P-Value Intercept   -7.092 0.219 -8.801 -7.980 <.0001 Intervention After -0.102 0.121 -0.447 -0.064 0.4003 Age group 16-24 1.111 0.010 1.057 1.106 Age group*Intervention -0.123 0.028 -0.228 -0.060 0.0621 Gender F -0.486 0.011 -0.438 -0.375 Gender*Intervention 0.047 0.016 0.105 0.0023 Relative risk estimates The data in the upper panel indicate that there were reductions in the rate of Distracted Driving Collisions in the period following the implementation of the legislation banning cell phones while driving. However, the extend of the reduction was not statistically significant. The risk of a crash from distracted driving reduced by 10% in the years following implementation relative to the prior period. Altough there were reductions among the two different age groups considered in this study, they were also not significant---thee impact on the younger group, however, is greater. Contrast Relative Risk Ratio 95% Confidence Limits P-value Relative Change Overall (After vs. Before) 0.904 0.713 1.450 0.400 -10% 16-24 (After vs. Before) 0.811 0.641 1.026 0.081 -19% 25+ (After vs. Before) 0.920 0.730 1.160 0.482 -8% 5

RESULTS RESULTS Results for DD Related PDO Collisions Parameter estimates and model fit criteria Parameter estimates and model fit criteria Effect Level Estimate Standard Error 95% Confidence Limits P-Value Intercept   -7.092 0.219 -8.801 -7.980 <.0001 Intervention After -0.102 0.121 -0.447 -0.064 0.4003 Age group 16-24 1.111 0.010 1.057 1.106 Age group*Intervention -0.123 0.028 -0.228 -0.060 0.0621 Gender F -0.486 0.011 -0.438 -0.375 Gender*Intervention 0.047 0.016 0.105 0.0023 GEE Fit Criteria QIC 480212 QICu 480285 Effect Level Estimate Standard Error 95% Confidence Limits P-Value Intercept   -7.395 0.237 -7.860 -6.930 <.0001 Intervention After -0.051 0.143 -0.331 0.229 0.721 Age group 16-24 1.122 0.010 1.103 1.142 0.028 Age group*Intervention -0.119 -0.175 -0.064 0.113 Gender F -0.515 0.015 -0.544 -0.487 Gender*Intervention 0.048 0.019 0.012 0.085 0.054 Relative risk estimates When we consider Property Damage only collisions resulting from distracted driving, the pattern is similar to the results from the overall or total collisions. The reason being that Property Damage only collisions form the bulk of the total distracted driving collisions. Again, there have been reductions but not statistically significant, with the younger group being impacted more than the adult driver population. Relative risk estimates Contrast Relative Risk Ratio 95% Confidence Limits P-value Relative Change Overall (After vs. Before) 0.904 0.713 1.450 0.400 -10% 16-24 0.811 0.641 1.026 0.081 -19% 25+ (After vs. Before) 0.920 0.730 1.160 0.482 -8% Contrast Relative Risk Ratio 95% Confidence Limits P-vale Relative Change Overall (After vs. Before) 0.950 0.718 1.2576 0.721 -5% 16-24 (After vs. Before) 0.853 0.652 1.117 0.248 -15% 25+ (After vs. Before) 0.966 0.732 1.274 0.806 7

RESULTS RESULTS Results for DD Related PDO Collisions Results for DD Related Casualty Collisions Parameter estimates and model fit criteria Parameter estimates and model fit criteria Effect Level Estimate Standard Error 95% Confidence Limits P-Value Intercept   -7.092 0.219 -8.801 -7.980 <.0001 Intervention After -0.102 0.121 -0.447 -0.064 0.4003 Age group 16-24 1.111 0.010 1.057 1.106 Age group*Intervention -0.123 0.028 -0.228 -0.060 0.0621 Gender F -0.486 0.011 -0.438 -0.375 Gender*Intervention 0.047 0.016 0.105 0.0023 GEE Fit Criteria QIC 480212 QICu 480285 Effect Level Estimate Standard Error 95% Confidence Limits P-Value Intercept   -8.390 0.210 -8.801 -7.980 <.0001 Intervention After -0.256 0.098 -0.447 -0.064 0.0088 Age group 16-24 1.081 0.013 1.057 1.106 Age group*Intervention -0.144 0.043 -0.228 -0.060 0.0009 Gender F -0.406 0.016 -0.438 -0.375 Gender*Intervention 0.058 0.024 0.011 0.105 0.0151 Relative risk estimates The results for distracted driving casualty collision rate changes indicated that the legislation has a more significant effect in the longer-term. Both the intervention variable and agegroup/intervention variables were associated with p-values that suggest highly significant associations. Overall, casualty collisions from distracted driving significantly reduced by 23% in the longer-term following implementation of the legislation compared to the period before the legislation. Again, the program has impacted the younger cohort (16-24 year olds ) more than the adult driving population. Relative risk estimates Contrast Relative Risk Ratio 95% Confidence Limits P-value Relative Change Overall (After vs. Before) 0.904 0.713 1.450 0.400 -10% 16-24 0.811 0.641 1.026 0.081 -19% 25+ (After vs. Before) 0.920 0.730 1.160 0.482 -8% Contrast Relative Risk Ratio 95% Confidence Limits P-vale Relative Change Overall (After vs. Before) 0.774 0.639 0.938 0.0088 -23% 16-24 (After vs. Before) 0.681 0.630 0.735 0.0005 -32% 25+ (After vs. Before) 0.791 0.655 0.955 0.0149 -20% 7

RESULTS CONCLUSIONS Results for DD Related PDO Collisions This study found sufficient evidence indicating a long-term impact of the legislation; however, the significance of the effects depended on the severity of the DD related collisions and driver age group The legislation had greater impact on DD related casualty collisions than the less severe PDO collisions Safety impact of the legislation was significantly greater among younger drivers (aged 16-24 years) than more experienced older drivers (aged 25 years and over) A policy implication from this study is that stricter measures could be required to reduce the risk of DD related collisions among experienced drivers to the level observed for younger drivers Parameter estimates and model fit criteria Effect Level Estimate Standard Error 95% Confidence Limits P-Value Intercept   -7.092 0.219 -8.801 -7.980 <.0001 Intervention After -0.102 0.121 -0.447 -0.064 0.4003 Age group 16-24 1.111 0.010 1.057 1.106 Age group*Intervention -0.123 0.028 -0.228 -0.060 0.0621 Gender F -0.486 0.011 -0.438 -0.375 Gender*Intervention 0.047 0.016 0.105 0.0023 GEE Fit Criteria QIC 480212 QICu 480285 Relative risk estimates Contrast Relative Risk Ratio 95% Confidence Limits P-value Relative Change Overall (After vs. Before) 0.904 0.713 1.450 0.400 -10% 16-24 0.811 0.641 1.026 0.081 -19% 25+ (After vs. Before) 0.920 0.730 1.160 0.482 -8% 7

DISCUSSIONS AND FUTURE SCOPE RESULTS DISCUSSIONS AND FUTURE SCOPE Results for DD Related PDO Collisions The use of distracted driving related collisions as surrogate measure to determine the long-term effects of the legislation instead of actual collisions resulting from cell-phones is an issue Caution should be exercised when generalizing the findings in this study. The model findings presented did not include factors such as, the levels of safety education, effects of newer technologies in motor vehicles, and increased road safety initiatives due to data limitations Future research should endeavor to include appropriate controls to account for changes in other traffic safety legislations introduced during the study period, and any other unobserved variables that could influence crash trends Future research could also investigate the incremental impact of introducing tougher penalties for distracted driving following the introduction of the legislation Parameter estimates and model fit criteria Effect Level Estimate Standard Error 95% Confidence Limits P-Value Intercept   -7.092 0.219 -8.801 -7.980 <.0001 Intervention After -0.102 0.121 -0.447 -0.064 0.4003 Age group 16-24 1.111 0.010 1.057 1.106 Age group*Intervention -0.123 0.028 -0.228 -0.060 0.0621 Gender F -0.486 0.011 -0.438 -0.375 Gender*Intervention 0.047 0.016 0.105 0.0023 GEE Fit Criteria QIC 480212 QICu 480285 Relative risk estimates This study used distracted driving related collisions as surrogate measure to determine the long-term effects of the legislation, which aimed at reducing general distracted driving-related collisions through enforcement of the ban on the use of cell phones or any electronic device while driving. This is an issue since cell phones are a form of distraction induced activity among others. Caution should be exercised when projecting the findings from this study to other jurisdictions due to different enforcement challenges, driving environments and road networks in other jurisdictions. The model findings presented did not include factors such as road construction, the levels of safety education, effects of newer technologies in motor vehicles, and increased road safety initiatives due to data limitations. These could impact the results. Future research should endeavor to include appropriate controls to account for changes in other traffic safety legislations introduced during the study period, and any other unobserved variables that could influence crash trends. Future research can also investigate the incremental impact of introducing tougher penalties for distracted driving following the introduction of the legislation. Do tougher measures enhance deterrence? Contrast Relative Risk Ratio 95% Confidence Limits P-value Relative Change Overall (After vs. Before) 0.904 0.713 1.450 0.400 -10% 16-24 0.811 0.641 1.026 0.081 -19% 25+ (After vs. Before) 0.920 0.730 1.160 0.482 -8% 7

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