Journal of safety research
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Little is known about how characteristics of the environment affect pedestrians' road crossing behavior. ⋯ Divided into three levels of visual load, results showed that high visual load affected children's and adults' road crossing behavior and visual attention. The main effect on participants' crossing decisions was seen in missed crossing opportunities. Children and adults missed more opportunities to cross the road when exposed to more cluttered road environments. An interaction with age was found in the dispersion of the visual attention measure. Children, 9-10 and 11-13 years old, had a wider spread of gazes across the scene when the environment was highly loaded-an effect not seen with adults. However, unexpectedly, no other indication of the deterring effect was found in the current study. Still, according to the results, it is reasonable to assume that busier road environments can be more hazardous to adult and child pedestrians. Practical Applications: In that context, it is important to further investigate the possible distracting effect of causal objects in the road environment on pedestrians, and especially children. This knowledge can help to create better safety guideline for children and assist urban planners in creating safer urban environments.
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Rear-end crashes are one of the most frequent crash types in China, leading to significant economic and societal losses. The development of active safety systems - such as Automatic Emergency Braking System (AEBS) - could avoid or mitigate the consequences of these crashes in Chinese traffic situations. However, a clear understanding of the crash causation mechanisms is necessary for the design of these systems. ⋯ Overall, the combination of short time headway with off-path glances directed toward the mirror originates visual mismatches which, associated to a rapid change in the kinematic situation, cause the occurrence of rear-end CNC. When drivers look back toward the road after an off-path glance, a fast response seems to be triggered by lower values of looming compared to previous studies, possibly because of the short time headways. Practical Application: The results have practical implications for the development of driver models, for the design of active safety systems and automated driving, and for the design of campaigns promoting safe driving.
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"If I know when you will arrive, I will let you walk to school:" The role of information technology.
Providing a sense of supervision for parents may facilitate their children's involvement in physical outdoor activities, such as walking to school. Information technology (IT) solutions could bring more parental supervision, which in turn has the potential to enhance the proportion of walking trips to school. This study aimed to examine the role of a proposed hypothetical IT solution that gives parents real-time information about children's entrance to and exit from the school, for the intention of parents letting their children walk to school. ⋯ Compared to the group of walking pupils, increased parental intention to let their children walk to school under the proposed solution could be explained by considerably more variables in the group of pupils who did not walk to school. The findings revealed that increased parental intention was higher among the walking pupils compared to the non-walking pupils. For the non-walking pupils, enhancement of walking facilities across the school area could potentiate the use of the proposed solution by the parents, which in turn may increase the proportion of walking on school trips. In addition, boy pupils, the pupils whose parents evaluated walking more favorable, those from lower socioeconomic backgrounds, and those living in close proximity to the school could more likely benefit by shifting from non-walking to walking modes of travel, after implementation of the solution.
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In this paper, we present machine learning techniques to analyze pedestrian and bicycle crash by developing macro-level crash prediction models. ⋯ The DTR models uncovered the most significant predictor variables for both response variables (pedestrian and bicycle crash counts) in terms of three broad categories: traffic, roadway, and socio-demographic characteristics. Additionally, spatial predictor variables of neighboring STAZs were considered along with the targeted STAZ in both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the model comparison results discovered that the prediction accuracy of the spatial DTR model performed better than the aspatial DTR model. Finally, the current research effort contributed to the safety literature by applying some ensemble techniques (i.e. bagging, random forest, and gradient boosting) in order to improve the prediction accuracy of the DTR models (weak learner) for macro-level crash count. The study revealed that all the ensemble techniques performed slightly better than the DTR model and the gradient boosting technique outperformed other competing ensemble techniques in macro-level crash prediction models.
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Drivers' collision avoidance performance in an impending collision situation plays a decisive role for safety outcomes. This study explored drivers' collision avoidance performances in three typical collision scenarios that were right-angle collision, head-on collision, and collision with pedestrian. ⋯ Long brake reaction time and wrong swerve direction were the main factors leading to a high collision likelihood. The swerve-toward-conflict maneuver caused a delay in brake action and degraded subsequent braking performances. The prevalent phenomenon indicated that drivers tended to use an intuitive (heuristic) way to make decisions in critical traffic situations. Practical applications: The study generated a better understanding of collision development and shed lights on the design of future advanced collision avoidance systems for semi-automated vehicles. Manufactures should also engage more efforts in developing active steering assistance systems to assist drivers in collision avoidance.