Which factors are related to crossing speed and road violations? A virtual reality study on young people’s crossing behavior.

Andrea Baldassa

European Transport / Trasporti Europei2024https://doi.org/10.48295/et.2024.97.7article
ABDC C
Weight
0.43

What the paper says

In the literature, numerous studies examined pedestrian behavior within road environments, aiming to contribute to injury prevention. Nevertheless, significant gaps remain, particularly concerning the behavior of young individuals. The present Virtual Reality experiment aimed at two main objectives: (1) understanding the factors associated with the likelihood of young people violating traffic laws and (2) identifying factors influencing their crossing speed, who faced various pedestrian crossing scenarios designed to induce violations are administered to 63 participants aged 11-17. Three main factors are considered in the analysis: socio-economic characteristics, travel patterns, and behavioral tendencies while walking. The results reveal that students who commute on foot and those residing in larger cities are less inclined to violate traffic laws. Furthermore, individuals who report frequent real-life misconduct are more likely to disregard red lights when crossing a signalized crosswalk in the virtual environment. Differences in average crossing speeds are observed between middle school and high school students, as well as between one-lane and two-lane crossing speeds.

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https://doi.org/https://doi.org/10.48295/et.2024.97.7

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@article{andrea2024,
  title        = {{Which factors are related to crossing speed and road violations? A virtual reality study on young people’s crossing behavior.}},
  author       = {Andrea Baldassa},
  journal      = {European Transport / Trasporti Europei},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.48295/et.2024.97.7},
}

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Evidence weight

0.43

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.30 × 0.4 = 0.12
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.