Traffic climate scale: Comparing samples from Turkey and Sweden

Traffic climate is a recent and one of the essential topics in traffic and transportation research. Various studies have examined the relations of traffic climate with driving outcomes such as accidents by using different versions of the Traffic Climate Scale (TCS). In a recent attempt, 16-items and 38-items versions of the TCS were examined in different countries. With respect to that, the present study aims to investigate the psychometric properties of 16-items and 38-items versions in samples from Turkey and Sweden and to test the traffic climate differences of these two countries. A total of 309 participants from Turkey and 357 participants from Sweden completed a questionnaire including a demographic information form and the TCS. Confirmatory factor analyses showed that the short TCS had better fit indexes with acceptable reliability. Moreover, the traffic system in Turkey was perceived to be more internally and externally demanding and less functional compared to the traffic system in Sweden. The results suggest that the short TCS is a reliable and user-friendly measurement to understand the perception of road users

Automation Preferences by Traffic Climate and Driver Skills in Two Samples From Countries with Different Levels of Traffic Safety

Automated systems present great capabilities with a wide range of options. In this respect, vehicle preferences and factors affecting these preferences are important for the future of automated systems. While automated systems offer varied features and improvements for drivers and general traffic safety, the relationship between drivers’ perceptions of traffic systems and driver skills have not been studied. The present study, therefore, focuses on country differences and the relationships between traffic climate and driver skills and their impact on the preferred level of vehicle automation for drivers in Turkey and Sweden. The study was conducted with 318 drivers (age: mean [M] = 22.41, standard deviation [SD] = 2.77) from Turkey and 312 drivers (age: M = 28.80, SD = 8.53) from Sweden in 2020. A questionnaire package asking for demographic information and preferred levels of vehicle automation—Traffic Climate Scale (TCS) and the Driver Skill Inventory (DSI)—was completed. A series of analyses of covariance (ANCOVA), hierarchical regression, and moderated moderation analyses were conducted. Drivers from Turkey preferred higher automation levels than drivers from Sweden. Drivers with higher perceived safety skills, with lower perceived perceptual-motor skills or perceiving the traffic system as more externally demanding preferred higher automation levels. Drivers’ automation preferences were affected by various individual and country-level factors. For the first time, drivers’ automation preferences were elaborated in relation to traffic climate and driver skills in two countries with different levels of traffic safety. Theoretical and practical implications of the findings are discussed in the light of the literature

Development of an in-depth European accident causation database and the driving reliability and error analysis method, DREAM 3.0

- The SafetyNet project was formulated in part to address the need for safety oriented European road accident data. One of the main tasks included within the project was the development of a methodology for better understanding of accident causation together with the development of an associated database involving data obtained from on-scene or “nearly onscene” accident investigations. Information from these investigations was complemented by data from follow-up interviews with crash participants to determine critical events and contributory factors to the accident occurrence. A method for classification of accident contributing factors, known as DREAM 3.0, was developed and tested in conjunction with the SafetyNet activities. Collection of data and case analysis for some 1 000 individual crashes have recently been completed and inserted into the database and therefore aggregation analyses of the data are now being undertaken. This paper describes the methodology development, an overview of the database and the initial aggregation analyses