Evaluation of the Alabama ticketing aggressive cars and trucks selective enforcement program
Crashes involving passenger vehicles and large commercial motor vehicles have a greater potential for being severe. A summary of crash statistics involving these types of crashes is presented and the problem is examined. Selective Traffic Enforcement Programs attempt to curb particular causes of crashes by using media campaigns and high-visibility enforcement. A background on the Click It or Ticket and Ticketing Aggressive Cars and Trucks programs is given. The focus of this research was to evaluate the effectiveness of the Alabama implementation of the Ticketing Aggressive Cars and Trucks program from January 2010-August 2015. Data was collected from various sources to evaluate the program. This included crash data as well as officer reports from each program shift completed. The crash data was mapped and evaluated for hotspots. Officer reports were evaluated for how many hours were worked, where officers focused their enforcement, how many and what types of citations were written, and the effects of varying levels of enforcement on crash rates. Analysis of Variation and Fisher’s Least Significant Difference post-hoc tests were performed to evaluate this data. In addition to the above sources of data, video observation of traffic events were also recorded and analyzed for effects of the program. Evaluators scored each car-truck interaction as safe, or unsafe, with unsafe actions including changing lanes too close to the front of a truck, remaining in a truck’s blind spot for an unsafe amount of time, or following too close to another vehicle. The results of the analysis showed that the program was effective. Officers successfully focused their enforcement on areas with the highest density of the targeted crashes. Medium levels of enforcement decreased crashes versus low levels (506 versus 555 crashes, significant at p-value 0.003.) High and medium enforcement was shown to reduce the number of crashes compared with low levels of enforcement (509 versus 555 crashes, significant at p-value 0.004.) Observational data was not able to demonstrate any noticeable effects of the program. Recommendations to improve the program include increasing public awareness of the program, increasing officer contacts per hour, concentrating officer enforcement over designated periods and designating months as enforcement or non-enforcement months for purposes of evaluation. Ways to improve the evaluation of the program included the introduction of a control corridor and automation of observational analysis.