Smart Computing and Systems Engineering (SCSE)

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    Classification of vehicles by video analytics for unorganized traffic environments
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Arachchi, I.M.R.; Jayalal, S.; Rajapakse, C.
    Traffic monitoring is essential for infrastructure planning and transportation. The objective of traffic monitoring is to have an effective traffic management system. Traffic management systems would be effective in well-organized traffic environments, where it has very disciplinary behaviors and less in inefficiencies. But in unorganized urban environments like Sri Lanka, road traffic behaviours are varying from standard structured ways which lead to discompose the traffic management. An effective monitoring system requires short processing time, low processing cost and high reliability. The paper proposes a novel vehicle detection and classification algorithm based on background filtering and re-engineered with suitable changes in order to be applicable to challenging unorganized traffic environments. The solution is successfully classifying vehicles individually and their trajectories in unorganized traffic environments in order to monitor the behaviors of the drivers. The system gives 74.4% average accuracy in vehicle detection and 55% accuracy in vehicle classification while counting each vehicle passed by. We used OpenCV functions for implementing and testing algorithms. Data was collected through pre-recorded video clips from footbridge crossing at Colombo Fort in western province Sri Lanka, for the testing. The ultimate objective of this research was to come up with a best-suited algorithm for vehicle detection and classification (hybrid solution) in unorganized traffic environments which would help to analyze the behaviors of road users. The solution will lead to help reduce unorganized traffic congestions by enhancing the efficiency and effectiveness of traffic monitoring and analyzing systems those are used for intelligent traffic management systems and traffic simulation models.
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    Applicability of crowdsourcing for traffic-less travelling in Sri Lankan context
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Senanayake, J.M.D.; Wijayanayake, J.
    Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper way to predict the traffic and recommend the best route considering the time factor and the people’s satisfaction on various transportation methods. Therefore, in this research using crowdsourcing together with data mining techniques, data related to user mobility were collected and studied and based on the observations, an algorithm has been developed to overcome the problem. By using developed techniques, the best transportation method can be predicted. Therefore, people can choose what will be the best time slots & transportation methods when planning journeys. The algorithm correctly predict the best traffic-less traveling method for the studied area of each given day & the given time. Throughout this research it has been proven that to determine the best transportation method in Sri Lankan context, data mining concepts together with crowdsourcing can be applied. Based on a thorough analysis by extending the data set of the collection stage, it was shown that this research can be extended to predict the best transportation method with consideration of existing traffic in all the areas.