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 andvehicles in conflict areas. By analyzing those factors and determining how they affect road user's behavior, we canrepresent the plans and procedures to promote awareness and safety of both pedestrians and drivers. The goal of thisarticle is to study pedestrian decision making behavior in unsignalized crosswalks and to determine factors affectingthe crossing behavior in conflict areas. The supposed goal of this study was assessing how each factor can influencepedestrian-vehicle conflict behavior by means of developing logistic regression models. This work explores a varietyof factors that may impact the gap acceptance behavior of pedestrian to provide a promising decision model. Discretechoice (probit) models of the gap acceptance decision are estimated from observations of pedestrians behavior whencrossing at conflict zone.Analysis results show that variables like vehicle speed change (VSC), pedestrian distance to vehicle lane (PDV), pedestrianage (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 correctclassification of 94.9 pair wise cases. Area under ROC curve resulted in 0.98 that means the reliability of models isextracted. The results also showed that some variables like vehicle type (VT), waiting time (WT), number of pedestrianswalking in a group (PN) and Gap or Lag are not effective in decision making logit models.
[1]
Shahram Azadi,et al.
Towards a Decision-Making Algorithm for Automatic Lane Change Manoeuvre Considering Traffic Dynamics
,
2016
.
[2]
Michael Sivak,et al.
Mortality from road crashes in 193 countries: a comparison with other leading causes of death
,
2014
.
[3]
Andras Varhelyi,et al.
Dynamic speed adaptation based on information technology - a theoretical background
,
1997
.
[4]
Ji Hai,et al.
Towards the pedestrian delay estimation at intersections under vehicular platoon caused conflicts
,
2010
.
[5]
Ross H Day,et al.
Crossing roads safely: an experimental study of age differences in gap selection by pedestrians.
,
2005,
Accident; analysis and prevention.
[6]
Viola Cavallo,et al.
Age-related differences in street-crossing decisions: the effects of vehicle speed and time constraints on gap selection in an estimation task.
,
2007,
Accident; analysis and prevention.
[7]
Tak-Shing Harry So,et al.
The Use and Interpretation of Logistic Regression in Higher Education Journals: 1988–1999
,
2002
.
[8]
Hani S. Mahmassani,et al.
Models of pedestrian crossing behavior at signalized intersections
,
1994
.
[9]
S. Wong,et al.
A Potential Based Many-Particle Model for Pedestrian Flow
,
2015
.
[10]
Tak-Shing Harry So,et al.
STATISTICAL SOFTWARE APPLICATIONS & REVIEW Modeling Strategies In Logistic Regression With SAS, SPSS, Systat, BMDP, Minitab, And STATA
,
2002
.
[11]
B. Tabachnick,et al.
Using Multivariate Statistics
,
1983
.