The importance of area, which is identifying cognitive understanding of community on road traffic accident has become the main goal to reduce damage caused. Traffic is a social issue of vast economic and environmental importance, which need a dynamic and advanced analytic tools for data analysis. Data mining is an essentially determine common kinds of behavioral variation of human to make proper decision for the purpose of traffic accident control. However, road accident and traffic jam is a big challenge vicious circle problem among road facilities, car conditions, drivers concern,people’s traffic awareness, care and feelings. Therefore, in this thesis paper, we proposed Decision support system (DSS)basedcriticalanalysisofhumancognitivebehavior. DSSistheintellectualresource of entities, which is the capability of computer to improve quality of road safety based critical analysis of human cognitive behavior to define the most probable car accident factors. We also introduced new way of a traffic management strategies by knowing the prominent factors, analyzed cognitive knowledge based data,study internal and external factors of human cognitive behaviors for individuals on crossing and moving traffic road. Asperourproposedapproach, dataminingisatechniqueandalgorithmofcomplexdata analysis to extract valuable information and knowledge through iterative and interactive steps that adopted to extract significant patterns from a dataset which contained 14,400 records under three categories and four locations. The data used for this study is collected as primary and secondary data methods. We use WEKA 3.6.13 tools clustering and classification models for designing and implementing successful traffic management. Under a classification model, we use decision tree stump, J48 tree, PART algorithms and for clustering, we use simple k-means algorithms. Using the selected application for analysis of collected data, we did various experiments that interactively by making adjustment of parameters and using different number of attributes to come up with a meaningful output. As of the experiment and consequent computationalanalysis,themajorfactorsofcaraccidentsareidentifiedandrulesareproduced using J48 decision tree and PART rule induction. As the result, we define the determinant factor that need to be caused for improving road traffic accident. Thus are the influential factors that needed to be considered road safety strategy policy development implementation circumstances. The comparison of models using WEKA’s experimenter present that decision stump slightly outperform than J48 and PART algorithms.Finally, the determinant factors of traffic accidents and human cognitive behaviors are identified based on the cognitive understanding of communities, and also we defined an extended research work up on this finding.