Predictors of safe road-crossing behavior among female high school students of Shahr-e Kord: an application of the theory of planned behavior

Objectives Traffic accidents in pedestrians is one of the most important causes of death. Understanding the status quo and identification of effective factors are necessary for the management and planning of efficient training interventions in the prevention of traffic accidents for pedestrians. Hence, the present study aimed to determine the predictors of safe road-crossing behavior among female high school students of Shahr-e Kord. Methods The present research was a descriptive-analytical study which was conducted on 347 female high school students of Shahr-e Kord in the academic year 2016–2017. The participants were selected using random sampling method and the required data were collected through a standard questionnaire based on the theory of planned behavior. The obtained data were statistically analyzed using Pearson correlation test and regression analysis. Results The mean score of participants on the adoption of safe road-crossing behavior was equal to 57.06 ± 14.74. Among the independent variables of this study, the lowest and the highest scores were related to behavioral intention and outcome expectancy, respectively. The results of multiple regression test showed that behavioral beliefs, outcome expectancy, compliance motivation and behavioral intention are predictors of the adoption of safe road-crossing behavior. In total, these constructs were able to predict 25.8% of behavioral changes. Conclusions Based on the study findings, the theory of planned behavior can be considered an appropriate framework for designing training interventions in order to improve students’ road-crossing behavior.

Comparison of the Effect of Traffic Park-Based and School-Based Educational Interventions on Safe Road Crossing in Yazd Elementary School Students

Introduction Traffic accidents are one of the most important health problems that cause many deaths every year. Scientific-practical interventions are needed to prevent traffic accidents. This study aimed to compare the effectiveness of traffic-based and school-based interventions on the safe road crossing in Yazd elementary school students. Methods This interventional study was conducted on 132 students (66 males and 66 females) in two groups of school-based and traffic park-based intervention. A questionnaire was used to determine the safe crossing behavior score. Then, an educational intervention was administered to both groups. Two months after the intervention, safe crossing behavior was evaluated in both groups. Finally, the data were analyzed using SPSS software version 22. Results In both groups, the score of safe crossing behavior was significantly increased after the intervention. The school-based group had significantly better behaviors compared to the traffic park-based group (P=0.001). There was a significant difference in the mean score of behavior between males and females and the intervention had a greater effect on female students (P=0.017). Conclusion Educational interventions and especially school-based intervention can be effective in improving the students' safe crossing behaviors.

Self-explaining roads: What does visual cognition tell us about designing safer roads?

In 1995, Theeuwes and Godthelp published a paper called “self-explaining roads,” in which they argued for the development of a new concept for approaching safe road design. Since this publication, self-explaining roads (SER) became one of the leading principles in road design worldwide. The underlying notion is that roads should be designed in such a way that road users immediately know how to behave and what to expect on these roads. In other words, the environment should be designed such that it elicits adequate and safe behavior. The present paper describes in detail the theoretical basis for the idea of SER and explains why this has such a large effect on human behavior. It is argued that the notion is firmly rooted in the theoretical framework of statistical learning, subjective road categorization and the associated expectations. The paper illustrates some successful implementation and describes recent developments worldwide.

Analysis of Impact of Rain Conditions on ADAS

Various technologies are being developed to support safe driving. Among them, ADAS, including LDWS, is becoming increasingly common. This driver assistance system aims to create a safe road environment while compensating for the driver’s carelessness. The driver is affected by external environmental factors such as rainfall, snowfall, and bad weather conditions. ADAS is designed to recognize the surrounding situation and enable safe driving by using sensors, but it does not operate normally in bad weather conditions. In this study, we quantitatively measured the effect of bad weather conditions on the actual ADAS function. Additionally, we conducted a vehicle-based driving experiment to suggest an improvement plan for safer driving. In the driving experiment, when the vehicle driving speed was changed in four stages of rainfall, it was confirmed that it affected the View Range value, where the primary variable is the visibility of ADAS. As a result of the analysis, we demonstrated that when the rainfall exceeded a precipitation of 20 mm, the ADAS sensor did not operate, regardless of the vehicle speed. This means that a problem affecting safe driving may occur due to functionality in bad weather situations in which the driver requires ADAS assistance. Therefore, it is necessary to develop a technology that can maintain the minimum ADAS functionality under rainfall conditions and other bad weather conditions.