Modeling the Behavior of Pedestrians in Choosing the Time to Cross Urban Intersections

Document Type : Original Article

Authors

1 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.

2 M.Sc., Grad., School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.

3 Associate Professor, Department of Civil - Transportation Planning, Imam Khomeini International University (IKIU), Qazvin, Iran.

Abstract

This paper outlines the behavior of pedestrians in crossing signalized intersections and finds the main and effective variables that influence violation behavior in Tehran. For that purpose, required data was collected by recording pedestrian behavior then asking them to complete questionnaires. A binary logit (BL) model was developed to determine the influence and significance of each collected variable in the decision-making process. A structural equation model (SEM) was used to determine the interaction between observed and hidden variables and describe how they influence behavioral intention. After collecting the questionnaires and removing the false ones, 304 valid questionnaires remained. The structural model results showed that attitude, perceived behavior control, and the presence of other pedestrians directly influence behavior intention. As a result, when a pedestrian believes that breaking the law will save time or be a convenient way out, the chance of breaking the law increases. It also increases when they perceive more control over the behavior. Subjective norms and sex, on the other hand, have an indirect influence on intention. Since men feel more control over their behavior, they are more likely to violate it. The binary logit model results showed that the violation probability is higher among men than women. It is also more probable that pedestrians aged 25 to 29 violate the law more often at intersections. The existence of other pedestrians waiting for the green light can reduce the violation probability, and the presence of other violators can increase it. Increasing the speed of the approaching vehicle will decrease the violation probability. A comparison between the two models showed that SEM could describe the impact of psychological variables better, and BL could better define traffic and condition-related variables. On the other hand, the effect of other pedestrians on behavior is significant in both models.
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References

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