Using Neuro-fuzzy Models to Benchmark Road Safety Management Systems

Road related deaths and injuries continue to be one of the highest incidents recorded in organizations. Road Safety has become a major concern worldwide. Therefore the United Nations has introduced a new movement: UN Decade of Action for Road Safety 2011-2020, which aims to reduce road deaths and injuries worldwide. An effective Safety Management System (SMS) can help in reducing risk of incidents, injuries and fatalities. The National Safety Council defines the three performance areas to benchmark SMS to be Leadership - Management, Technical - Operational and Cultural - Behavioral. This paper proposes a systematic way of finding relationships between Technical - Operational factors, Cultural - Behavioral and Safety Management Systems through the use of Neural networks-fuzzy. A sample SMS is simulated using critical factors (environmental and road conditions). Moreover, neural networks are used to predict the next outcome given historical information of various parameters such as road and weather conditions. Fuzzy logic is used to fuzzily the membership functions. The model helps us understand the effects of factors such as snow, rain and Mean Temperature as well as the events reported by In Vehicle Monitoring System (IVMS) on the number of incidents recorded and the "Road Safety Score". To illustrate the methodology in this paper, the neural networks-fuzzy model is fed with environmental factors to see how they affect the overall number of incidents recorded on a daily basis in the City of Calgary.