Road traffic mortality in Iran: longitudinal trend and seasonal analysis, March 2011-February 2020.

Road traffic mortalities (RTMs) and injuries are among the leading causes of human fatalities worldwide, particularly in low-and middle-income countries like Iran. Using an interrupted time series analysis, we investigated three interventional points (two government-mandated fuel price increases and increased traffic ticket fines) for their potential relation to RTMs. Our findings showed that while the overall trend of RTMs was decreasing during the study period, multiple individual provinces showed smaller reductions in RTMs. We also found that both waves of government-mandated fuel price increases coincided with decreases in RTMs. However, the second wave coincided with RTM decreases in a smaller number of provinces than the first wave suggesting that the same type of intervention may not be as effective when repeated. Also, increased traffic ticket fines were only effective in a small number of provinces. Potential reasons and solutions for the findings are discussed in light of Iran's Road Safety Strategic Plan.

Explaining human interactions on the road by large-scale integration of computational psychological theory.

When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others' intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions-but not simpler versions of the same model-can account for a number of previously unexplained phenomena in naturalistic driver-pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

Who goes first? A distributed simulator study of vehicle-pedestrian interaction.

One of the current challenges of automation is to have highly automated vehicles (HAVs) that communicate effectively with pedestrians and react to changes in pedestrian behaviour, to promote more trustable HAVs. However, the details of how human drivers and pedestrians interact at unsignalised crossings remain poorly understood. We addressed some aspects of this challenge by replicating vehicle-pedestrian interactions in a safe and controlled virtual environment by connecting a high fidelity motion-based driving simulator to a CAVE-based pedestrian lab in which 64 participants (32 pairs of one driver and one pedestrian) interacted with each other under different scenarios. The controlled setting helped us study the causal role of kinematics and priority rules on interaction outcome and behaviour, something that is not possible in naturalistic studies. We also found that kinematic cues played a stronger role than psychological traits like sensation seeking and social value orientation in determining whether the pedestrian or driver passed first at unmarked crossings. One main contribution of this study is our experimental paradigm, which permitted repeated observation of crossing interactions by each driver-pedestrian participant pair, yielding behaviours which were qualitatively in line with observations from naturalistic studies.

Psychosocial factors associated with the self-reported frequency of cell phone use while driving in Iran.

Cell phone use while driving is a common contributing factor in thousands of road traffic injuries every year globally. Despite extensive research investigating the risks associated with cell phone use while driving, social media campaigns to raise public awareness and a number of laws banning phone use while driving, this behaviour remains prevalent throughout the world. The current study was conducted in Iran, where road traffic injuries are the leading causes of death and disability, and where drivers continue to use their cell phones, despite legislative bans restricting this behaviour. A total of 255 drivers in the city of Mashhad (male = 66.3%; mean age = 30.73 years; SD = 9.89) completed either an online or a paper-based survey assessing the self-reported frequency of using a cell phone while driving. Psychosocial factors contributing to cell phone use while driving and support for legislation restricting this behaviour, as well as the Big Five personality traits, were also measured. Overall, the results showed that almost 93% of drivers use their cell phones while driving at least once a week, with 32.5% reporting they always use their cell phones while driving. Ordinal logistic regression revealed that the presence of a child passenger, age, perceived benefits and risks of using cell phones while driving, as well as the perceived ability to drive safely while using a cell phone, were strongly associated with the frequency of cell phone use while driving. As for personality traits-extraversion, agreeableness and conscientiousness significantly predicted the frequency of cell phone use in this sample of Iranian drivers.