to download project abstract/base paper of framework


The road accident data analysis use data mining and machine learning techniques, framework focusing on identifying factors that affect the severity of an accident. There are a variety of reasons that contribute to accidents. Some of them are internal to the driver but many are external. For example, adverse weather conditions like fog, rainfall or snowfall cause partial visibility and it may become difficult as well as risky to drive on such roads. It is expected that the findings from this paper would help civic authorities to take proactive actions on likely crash prone weather and traffic conditions.

Road safety becomes a major public health concern when the statistics show that more than 3000 people around the world succumb to death daily due to road traffic injury. In addition, road crashes lead to the global economic losses as estimated in road traffic injury costs to US$518 billion per year. The huge economic losses are an economic burden for developing countries. The road data are necessary not only for statistical analysis in setting priority targets but also for in-depth study in identifying the contributory factors to have a better understanding of the chain of events. There are a lot of Data Mining algorithms which are available to find out the association between independent variables in a huge data. The most popular and commonly used algorithm is Association rule mining. This can be used to detect the significant associations between the data stored in the large database. Apriori, predictive Apriori and FP-growth algorithm are the most common association rule mining methods which are used. The results obtained from these data mining approach can help understand the most significant factors or often Repeating patterns. The generated pattern identifies the most dangerous roads in terms of road accidents and necessary measures can be taken to avoid accidents in those roads.

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