Novel Machine Learning Methods for Accident Data Analysis

Recent advances in AI and machine learning techniques have created a demand for more applications - including traffic accident analysis.

With the recent advances in data collection, storage and archival methods, the size of accident datasets has grown significantly. This in turn has motivated research on applying data mining and complex network analysis algorithms, which are specifically designed to handle datasets with large dimensions, to traffic accident analysis. This project is exploring the potential for using a number of machine learning and data mining methods to accident data analysis, including methods such as the modularity-optimizing community detection algorithms, association rules learning algorithms, Bayesian Networks, and frequent pattern trees. The project has resulted so far in a Transportation Research Board (TRB) paper which was recently accepted for publication, and two other papers in refereed conference proceedings.