International Conference on Advances in Computing and Technology (ICACT)

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    MLP Model Approach for Driver Fault Identification
    (4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Ariyathilake, S.N.; Rathnayaka, R.M.K.T.
    The issue of the traffic accident has gain attention of the globe which has been a major challenge for the sustainable development of transportation and traffic. Crashes are events which occurred by involving different components: Driver, road, environment. Driver identification is directly connected to taking advanced actions on the road accident. Prevention of the road accident is the primary concern and necessary legal actions must be taken for the responsible party of the accident. In order to accurately predict the driver fault regarding an accident, this study aims to identify whether the driver is fault for the accident or not, by using a Multilayer Perceptron (MLP) model. The proposed model accurately predicts the driver fault while ensuring the accuracy of the decision. Proposed Multilayer perceptron model has achieved an accuracy of 97.77% with the accident data. To compare the results of the model, Decision Tree, Linear classifier and DNN classifier has used. Comparative results revealed that the most accurate model as the Multilayer perceptron approach. Necessary sensitivity analysis regarding the MLP was performed to find the best MLP model. Results revealed that by using 500 epochs with RMSprop accuracy was increased. T – Test was performed with 0.05 accuracy level for the selected methods and MLP method outperformed the other techniques. The research will provide the information needed to guide the relevant decision-makers in adopting suitable measures to prevent and to reduce the accident rate.
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    Analysis of Road Traffic Accidents Using Data Mining
    (Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Liyanaarachchi, K.L.P.P.; Charles, E.Y.A.
    Accident happens unexpectedly and unintentionally, typically resulting in damage or injury or in fatalities. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data collected for various purposes. The main objective of this research is to identify more accurate and useful patterns that would exists in the road traffic accident data using data mining techniques. It is believed that these patterns can be utilized to take measures to reduce the number of accidents or the severity of the accidents. As part of this research work, details of accidents occurred in Colombo district in the year 2015 were collected from the Traffic Headquarters, Colombo, Sri Lanka. A data set with 9487 accident incidents each detailed with 55 features was created from the collected data. This data consists four types of accidents, namely, Fatal (154), Grievous (877), Non-Grievous (2028) and Vehicle damage only (6428). There are a quite a few published studies on traffic accident analysis using data mining methods. In most of these studies, J48 classifier has produced higher accuracy than other methods. So far no such study has been reported on accidents occurred in Sri Lankan roads. A correlation analysis was performed on the data set and as a result 10 attributes were removed. In this study, the J48 decision tree classifier was usedin two ways. In the first one all four type of accidents were considered. The decision tree built with 70% of the data was able to achieve an average accuracy of 71.4687%. In the second analysis, three types Fatal, Grievous and nongrievous types were combined into one class and named as Injured. This approach was taken to reduce the effect of the vehicle damage only class, which is around 68% of the total data. The decision tree built with this merged classes was able to achieve an accuracy of 78.7288 % using a tenfold cross validation. The decision tree was converted into 20 rules, which can predict the type of accident based on the identified attribute values. The results were found to be helpful to identify the factors influencing traffic accidents and can be further analyzed to find more subtle reasons or situations that are causing accidents.