Severity of Pedestrian Crashes in Developing Countries: Analysis and Comparisons Using Decision Tree Techniques

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 tech-niques, rather than using statistical regression models that have particular predefined causal rela-tionships 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 consid-ered 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.