Pedestrian Gap Acceptance Logit Model in Unsignalized Crosswalks Conflict Zone

Document Type: Research Paper

Authors

1 Assistant Professor, Department of Civil and Environmental Engineering, , Tarbiat Modares University, Tehran, Iran

2 M.Sc., Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Pedestrians are the most vulnerable road users. For evaluating and modifying pedestrian safety in unsignalized crosswalks,
the first important issue is to identify and explore factors affecting the interaction behavior of pedestrians and
vehicles in conflict areas. By analyzing those factors and determining how they affect road user's behavior, we can
represent the plans and procedures to promote awareness and safety of both pedestrians and drivers. The goal of this
article is to study pedestrian decision making behavior in unsignalized crosswalks and to determine factors affecting
the crossing behavior in conflict areas. The supposed goal of this study was assessing how each factor can influence
pedestrian-vehicle conflict behavior by means of developing logistic regression models. This work explores a variety
of factors that may impact the gap acceptance behavior of pedestrian to provide a promising decision model. Discrete
choice (probit) models of the gap acceptance decision are estimated from observations of pedestrians behavior when
crossing at conflict zone.
Analysis results show that variables like vehicle speed change (VSC), pedestrian distance to vehicle lane (PDV), pedestrian
age (PA) and vehicle position to the start point of pedestrian (Vp) are effective in Pedestrian gap Acceptance
(PGA). Modeling decision making behavior by logit models, resulted in neglected R Square of 0.882 and correct
classification of 94.9 pair wise cases. Area under ROC curve resulted in 0.98 that means the reliability of models is
extracted. The results also showed that some variables like vehicle type (VT), waiting time (WT), number of pedestrians
walking in a group (PN) and Gap or Lag are not effective in decision making logit models.

Keywords


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