Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this ...Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this data offers. A free to use, scalable traffic crash hot spot detection tool was developed to aid law enforcement agency decision makers, statewide to the local municipality level. The tool was developed to identify crash hot spots algorithmically with </span><span style="font-family:Verdana;">a range of customizable parameters based on location, date and time, and</span><span style="font-family:Verdana;"> crash factors, enabling quick, dynamic queries. These capabilities provide the ability for law enforcement agencies to conduct “what if” analyses and make data-driven allocation decisions, placing officer resources where they are most needed. The two-step algorithm first identifies potential hot spots based on </span><span style="font-family:Verdana;">crash density and then ranks each hot spot using a standardized z-score </span><span style="font-family:Verdana;">measure of relative significance. To test the viability of the tool, a pilot was conducted identifying 27 hot spots across Wisconsin where targeted enforcement was then deployed. Despite officer skepticism, results from the pilot found officers at sites targeted for speeding and seatbelt violations were nearly twice as likely to initiate traffic stops compared to non-targeted hot spots. Empirical Bayes before-and-after crash analyses found fatal and injury crashes reduced significantly by nearly 11% during the months with targeted enforcement, while property damage crashes and total crashes were unchanged. Overall, the results show the algorithm can identify hotspots where, coupled with targeted enforcement, traffic safety improvements can be made.展开更多
The contemporary traffic safety research comprises little information on quantifying the simultaneous association between drink driving and speeding among fatally injured drivers. Potential correlation between driver...The contemporary traffic safety research comprises little information on quantifying the simultaneous association between drink driving and speeding among fatally injured drivers. Potential correlation between driver＇s drink driving and speeding behavior poses a substantial methodological concern which needs investigation. This study therefore focused on investigating the simultaneous impact of socioeconomic factors, fatalities, vehicle ownership, health services and highway agency road safety policies on enforce- ment levels of speed limit and drink driving laws. The effectiveness of enforcement levels of speed limit and drink driving laws has been investigated through development of bivariate ordered probit model using data extricated from WHO＇s global status report on road safety in 2013. The consistent and intuitive parameter estimates along with statisti- cally significant correlation between response outcomes validates the statistical suprem- acy of bivariate ordered probit model. The results revealed that fatalities per thousand registered vehicles, hospital beds per hundred thousand population and road safety pol- icies are associated with a likely medium or high effectiveness of enforcement levels of speed limit and drink driving laws, respectively. Also, the model encapsulates the effect of several other agency related variables and socio-economic status on the response outcomes. Marginal effects are reported for analyzing the impact of such factors on in- terrnediate categories of response outcomes. The results of this study are expected to provide necessary insights to elemental enforcement programs. Also, marginal effects of explanatory variables may provide useful directions for formulating effective policy countermeasures for overcoming driver＇s speeding and drink driving behavior.展开更多
文摘Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this data offers. A free to use, scalable traffic crash hot spot detection tool was developed to aid law enforcement agency decision makers, statewide to the local municipality level. The tool was developed to identify crash hot spots algorithmically with </span><span style="font-family:Verdana;">a range of customizable parameters based on location, date and time, and</span><span style="font-family:Verdana;"> crash factors, enabling quick, dynamic queries. These capabilities provide the ability for law enforcement agencies to conduct “what if” analyses and make data-driven allocation decisions, placing officer resources where they are most needed. The two-step algorithm first identifies potential hot spots based on </span><span style="font-family:Verdana;">crash density and then ranks each hot spot using a standardized z-score </span><span style="font-family:Verdana;">measure of relative significance. To test the viability of the tool, a pilot was conducted identifying 27 hot spots across Wisconsin where targeted enforcement was then deployed. Despite officer skepticism, results from the pilot found officers at sites targeted for speeding and seatbelt violations were nearly twice as likely to initiate traffic stops compared to non-targeted hot spots. Empirical Bayes before-and-after crash analyses found fatal and injury crashes reduced significantly by nearly 11% during the months with targeted enforcement, while property damage crashes and total crashes were unchanged. Overall, the results show the algorithm can identify hotspots where, coupled with targeted enforcement, traffic safety improvements can be made.
文摘The contemporary traffic safety research comprises little information on quantifying the simultaneous association between drink driving and speeding among fatally injured drivers. Potential correlation between driver＇s drink driving and speeding behavior poses a substantial methodological concern which needs investigation. This study therefore focused on investigating the simultaneous impact of socioeconomic factors, fatalities, vehicle ownership, health services and highway agency road safety policies on enforce- ment levels of speed limit and drink driving laws. The effectiveness of enforcement levels of speed limit and drink driving laws has been investigated through development of bivariate ordered probit model using data extricated from WHO＇s global status report on road safety in 2013. The consistent and intuitive parameter estimates along with statisti- cally significant correlation between response outcomes validates the statistical suprem- acy of bivariate ordered probit model. The results revealed that fatalities per thousand registered vehicles, hospital beds per hundred thousand population and road safety pol- icies are associated with a likely medium or high effectiveness of enforcement levels of speed limit and drink driving laws, respectively. Also, the model encapsulates the effect of several other agency related variables and socio-economic status on the response outcomes. Marginal effects are reported for analyzing the impact of such factors on in- terrnediate categories of response outcomes. The results of this study are expected to provide necessary insights to elemental enforcement programs. Also, marginal effects of explanatory variables may provide useful directions for formulating effective policy countermeasures for overcoming driver＇s speeding and drink driving behavior.
