Modeling the influence of safety aid market penetration on traffic safety: Case of collision warning system for powered two-wheelers.

Every year, several thousand powered two-wheeler (PTW) i.e., motorcycle, moped and scooter drivers and passengers die in traffic accidents in the EU. Despite the much higher risk of death and injuries for PTW vs. car users, there is a three-fold lack regarding collision warning technologies for PTWs: lack of research, lack of regulation and lack of availability in the market. Many injuries occur in rear-end collisions, when PTW is struck from the rear by another vehicle. In this paper we present a hybrid, multi-method simulation model that allows simulation of various situations in which a vehicle may collide with the rear end of a PTW. We have used this model to estimate the potential impact of market penetration of a novel PTW ESS + RECAS system, named MEBWS (Motorcycle Emergency Braking Warning System), within the EU on the number of traffic accidents and their consequences, which would contribute to the EU "Vision Zero" goal: "reduce road deaths to almost zero by 2050". MEBWS has been developed at the Faculty of Information Studies in Novo mesto and patented. Simulation results using EU traffic accident data show that with 100% market penetration of the MEBWS system in the EU, the total number of PTW rear-end collisions would decrease by 29.50%. This reduction would result in fewer injuries and a decrease in economic crash costs by €43,145,172, according to the standard EU methodology. With the MEBWS system enabled, the number of traffic accidents in the standard rear-end collision emergency braking scenarios Moto, normal drive, Moto, emergency stop and Moto, not moving decreased by 33.15%, 27.76% and 28.76%, respectively. In cases where the collision could not be prevented due to slow response of the following driver or very high relative speed of the vehicles, MEBWS reduced the relative speed at impact, resulting in a reduction of injury severity by up to 11.198%, as estimated by the amount of kinetic energy released at collision.

Understanding quit patterns from a randomized clinical trial: Latent classes, predictors, and long-term abstinence.

OBJECTIVE: Tobacco dependence treatment is recognized as a dynamic, chronic process comprised of several specific phases. Of these phases, the Cessation phase is the most critical as it has demonstrated the strongest relation to quit success. Yet, little is understood about smoking trajectories during this period. The current study aimed to address gaps in the smoking research literature and advance understanding of the dynamic quit process unique to completing an integrated smoking treatment by evaluating quit behavior during the Cessation phase. METHOD: Two hundred and sixty-seven treatment seeking smokers enrolled in a clinical trial to evaluate the efficacy of a novel, integrated smoking cessation treatment (46.1% male; Mage = 39.25, SD = 13.70) were included in the present study. Repeated-measure latent class analysis was employed to evaluate quit patterns from quit day through day 14 post-quit. RESULTS: Results supported a four-class solution: Consistent Quitters, Non-Quitters, Relapsers, and Delayed Quitters. Predictors of class membership included age, number of prior quit attempts, motivation to quit smoking, and quit day smoking urges. Moreover, class membership was significantly associated with 6-month abstinence. CONCLUSION: Results suggest that there are four relevant classes of quit behavior, each with specific predictor variables including age, motivation to quit, smoking urges, and number of quit attempts, and that these classes relate to long-term abstinence. These results have the potential to inform manualized smoking cessation treatment interventions based on relevant subgroups of quit behavior.