As urbanization accelerates, children’s safety when crossing urban streets has become an increasingly prominent concern. However, current street designs and visual guidance facilities are largely configured around adult users and tend to overlook children’s distinct cognitive and perceptual characteristics. In this study, we used seven virtual reality (VR) street-crossing scenarios and combined questionnaires, eye tracking, and motion capture to evaluate how five types of visual guidance elements—Footprint (stop) markings and Traffic bollard, Color-Coded Arrows, Look left markings, Tactile Paving Patterns, and Stop line—affect children’s street-crossing behavior. The results show that Footprint (stop) markings and Traffic bollard clearly enhance children’s Stopping–Scanning Awareness, prompting them to slow down and briefly pause within the decision zone. The Look left markings provide only limited cues for Left–Right Scanning in both adults and children. Tactile Paving Patterns and Color-Coded Arrows effectively attract children’s visual attention, but may weaken their judgement of street-crossing risk. The Stop line strengthens the visual boundary and increases environmental monitoring awareness among all participants; however, this study did not observe a clear improvement in Gait variability. By extending theories of children’s traffic behavior, this study also highlights that some facilities labeled as “child-friendly” may be over-designed or cognitively misaligned with children’s actual perceptual and decision-making processes. These findings provide empirical evidence for optimizing street facilities and for developing related technical standards and public policies.
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Wenjie Peng
Chinese Academy of Medical Sciences & Peking Union Medical College
Xinyu Zhang
Beijing University of Civil Engineering and Architecture
Bingmiao Zhu
China Architecture Design & Research Group (China)
Applied Sciences
Tsinghua University
Beijing University of Civil Engineering and Architecture
China Architecture Design & Research Group (China)
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Analyzing shared references across papers
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Peng et al. (Mon,) studied this question.
synapsesocial.com/papers/69401f062d562116f28f9e96 — DOI: https://doi.org/10.3390/app152412919