Using visual data mining in highway traffic safety analysis and decision making
Yao‐Te Tsai, Huw D. Smith, Stephen M. Swartz, Fadel M. Megahed · Journal of Transportation Management · 2015
An ongoing, two-fold challenge involves extracting useful information from the massive amounts of highway crash data and explaining complicated statistical models to inform the public about highway safety. Highway safety is critical to the trucking industry and highway funding policy. One method to analyze complex data is through the application of visual data mining tools. In this paper, we address the following three questions: a) what existing data visualization tools can assist with highway safety theory development and in policy-making?; b) can visual data mining uncover unknown relationships to inform the development of theory or practice? and c) can a data visualization toolkit be developed to assist the stakeholders in understanding the impact of publicpolicy on transportation safety? To address these questions, we developed a visual data mining toolkit that allows for understanding safety datasets and evaluating the effectiveness of safety policies.