This study intends to provide insight into pedestrian accidents by uncovering their patterns in order to design preventive measures and to allocate resources for identifiable problems. Kohonen neural networks are applied to a database of pedestrian fatal accidents occurred during the four-year period between 2003 and 2006. Results show the existence of five pedestrian accident patterns: (i) elderly pedestrians crossing on crosswalks far from intersection in metropolitan areas; (ii) pedestrians crossing suddenly or from hidden places and colliding with two-wheel vehicles on urban road sections; (iii) male pedestrians crossing at night and being hit by four-wheel vehicles on rural road sections; (iv) young male pedestrians crossing at night wide road sections in both urban and rural areas; (v) children and teenagers crossing road sections in small rural communities. From the policy perspective, results suggest the necessity of designing education campaigns for parents, promoting information campaigns for road users and allocating resources for infrastructural interventions and law enforcement in order to address the identified major problems.
Proceedings of the Vru Conference, 2010
Main Research Area:
International Conference on Safety and Mobility of Vulnerable Road Users: Pedestrians, Motorcyclists, and Bicyclists, 2010