Pedestrian Road Crossing Behavior

Pedestrians have become a major safety concern all over the world, particularly in developing countries, as they face difficult situations while crossing, especially at intersections. Among the road crashes, the majority of pedestrian casualties occur while crossing roads. Globally, pedestrians constitute 22% of all road deaths, and in some countries, this proportion could be as high as two thirds. Bangladesh is no different from this. The Accident Research Institute (ARI) of the Bangladesh University of Engineering and Technology (BUET) found that, in the period 1998–2013, out of the 84,174 traffic-related casualties that occurred in Bangladesh, 28,244 were pedestrians (34%). Of these 28,244 casualties, 13.3% occurred in Dhaka, and 31% occurred while the pedestrian was crossing the road. Risky behaviors of pedestrians make the situation even worse, as a large portion of the pedestrian crashes occurred due to their exhibition of risky crossing behaviors. As a result, risky pedestrian behaviors have been a serious concern for alleviating traffic safety problems. To make a safe crossing environment for the pedestrians by reducing the exhibition of risky road crossing behaviors, this study: 

• Evaluated crash risk associated with various pedestrian groups and their road crossing behaviors at intersections in Dhaka using multi-criteria decision-making approach. 

• Explored pedestrian, intersection, and traffic characteristics-related factors influencing pedestrian compliance behavior with signals and crosswalks using binary and multinomial logistic regression, respectively.

• Identified pedestrian, intersection, and traffic characteristics-related factors influencing pedestrians' decisions to engage in risky rolling gap and running crossing behaviors using binary logistic regression model and association rules mining technique, respectively.

• Analyzed traffic signal design parameters, including pedestrian crossing speed and waiting time at intersections, using linear regression models.

• Modeled driver yielding behavior towards pedestrians using binary logistic regression to identify pedestrian characteristics and behaviors-related factors influencing driver yielding.