Reliability analysis of pedestrian safety crossing in urban traffic environment

Safety Science 04/2012; 50(4):968–973. DOI: 10.1016/j.ssci.2011.12.027

ABSTRACT This paper describes the pedestrian safety crossing behaviour at signalized crosswalks in an urban traffic environment based on human reliability analysis. In our research, pedestrians’ waiting durations are modelled by a non-parametric and two parametric reliability models that recognize the effects of covariates. The covariates include pedestrian personal characteristics and urban traffic conditions in order to reflect the effects of human factors and internal environment comprehensively. The results indicate that most pedestrians show distinct time-dependent reliability but a few pedestrians are too impatient to wait for the lights changes.

1 Bookmark
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: When crossing the road, pedestrians have to make a trade-off between saving time and avoiding any risk of injuries. Here, we studied how culture influences an individual's perception of risks when crossing a street, using survival analysis. This study is the first to use this analysis to assess cognitive mechanisms and optimality of decisions underlying road crossing behaviour. We observed pedestrian behaviour in two city centres: Inuyama (Japan) and Strasbourg (France). In each city, observations were made at a safe site consisting of a crosswalk and a street light and at an unsafe site (i.e. no crosswalk or street light). At the unsafe site, we measured the time needed by a pedestrian to take a decision (Tdec). During Tdec, a pedestrian estimates whether he can (Tsafe) or cannot (Trisk) cross the road. Using survival analysis, we studied the distributions of these three time variables and showed that French pedestrians took more risks than Japanese pedestrians, and that males took more risks than females, but only in Japan. More studies would considerably broaden our understanding on how culture may affect decision-making processes under risky circumstances.
    Accident Analysis & Prevention 05/2013; · 1.87 Impact Factor