As pedestrians are among the most critical road users, this research analyzes
their vulnerability characteristics and predicts the injury severity of
pedestrian crashes through decision tree techniques, rather than using
statistical regression models that have particular predefined causal
relationships between dependent and independent variables. Five years have been
studied in Nablus Governorate/Province (2012–2016), one of 16 governorates in
Palestine, as a case study based on reported crash frequencies for developing
countries. Tree techniques (CART [Classification and Regression Tree] and CHAID
[Chi-Square Automatic Interaction Detector]) were applied to extract the main
impacting factors on injury severity for pedestrian crashes. The main
contributions considered a small regional context in developing countries and
found differences between the results of various methods in injury severity.
Fourteen independent variables have been analyzed. A CART model with Gini
splitting has produced the most accurate model. The most important variables
were the victim’s gender, followed by area classification as rural, and the age
categories of pedestrians older than 65 and younger than 15 years. The least
important variables were found to be the driver’s gender, land use, and pavement
conditions. Results also showed that the proximity of crashes to schools is
relatively high; therefore, some policies were suggested regarding children’s
awareness, school zones, and driver behavior. It was found that the majority of
factors influencing pedestrian crashes are related to human characteristics such
as age, gender, or attitude whereas, in developed countries, they were related
to vehicles and infrastructure. Based on the results of the study, tree
techniques were considered effective in the analysis of injury severity of
pedestrians in the context of developing countries to identify the main factors
of vulnerability.
