Anomaly Detection, which deals with the discovery of unusual instances in the data, has always been a challenging area of research and has intrigued many researchers over past few years. As part of our thesis work, we intend to make advances in the area of Spatial Anomaly Detection in highway traffic datasets. We present a framework, which would simplify the process of identifying and geo-locating anomalies and make the results of such anomaly detection easily identifiable and interpretable.;We first perform data preprocessing and cleansing for improving data reliability. Second we perform anomaly detection in traffic datasets by adopting sound statistical techniques. Finally, we validate the accuracy of our framework through machine learning algorithms.;As part of developing a new framework for facilitating the segregation and easy transformation of anomaly based data results, an intermediate data storage technique is developed. The new framework based interface is devised such that data gets conditionally processed and stored into a markup based format like XML; such that the data can be readily consumable by external APIs for facilitating intuitive spatial and graphical display on the framework's web Interface. We discuss results in real world traffic datasets from the Maryland State Highway Administration.
展开▼
