This study aims at highlighting the most contributing and influential factors to accident occurrence at hazardous locations of local urban roads, and correlating accident characteristics to different factors including geometric elements, traffic speed, pavement type, lighting conditions, type of collisions, pedestrian facilities existence and traffic conditions. The study was conducted using twenty-eight hazardous locations at Amman-Jordan roads. Each of the hazardous locations had at least twenty accidents and two fatality records per year. Data were collected through different sources that included Great Amman Municipality, Traffic Institute, Police Traffic Department and field studies. Different stepwise statistical regression models were developed to correlate accident characteristics with the studied variables. It was found that the logarithmic and linear models were the most significant and realistic models that can be used to predict the relationship between the accident characteristics as a dependent variable and the other studied variables as independent variables. The developed models were strong and predictable because the coefficient of multiple determinations was very close to the adjusted coefficient of multiple determinations. The following variables were found to be the most significant contributors to traffic accidents at hazardous locations: average running speed, posted speed, maximum and average degree of horizontal curves, number of vertical curves, median width, type of road surface, lighting (day or night), number of vehicles per hour, number of pedestrian crossing facilities and percentage of trucks. The study could open the door for planners and traffic engineers to overcome the problems associated with traffic accident occurrence at hazardous locations and enhance their safety. The developed models showed that accident characteristics such as number of accidents, number of fatalities, number of injuries, type of accident and number of vehicles involved in accidents, and statistics of hazardous locations could be predicted if the traffic conditions, geometric elements and environmental variables are known. The study could also be enhanced if incorporated with drivers’ characteristics including socio-economic data.
معالي الأستاذ الدكتور محمد طالب عبيدات | HE PROF. Moh’d Taleb Obaidat | 7024