Traffic risk mining from heterogeneous road statistics

Lately, a large amount of traffic-related data, such as traffic statistics, accident statistics, road information, and drivers´ and pedestrians´ comments, has been collected through sensors and social media networks. In this paper, we propose a novel framework for mining traffic risk from such heterogeneous data. Traffic risk refers to the possibility of traffic accidents occurring. We specifically focus on two issues: 1) predicting the number of accidents for any road and intersection and 2) clustering roads to identify the risk factors that are common to risky road clusters. We followed a unifying approach to these issues by using feature-based non-negative matrix factorization (FNMF). More specifically, we developed a new multiplicative updating FNMF algorithm capable of processing large traffic data. Using real traffic data from Tokyo, we demonstrate that our proposed algorithm is able to predict traffic risk at any location more accurately and efficiently than existing methods. A number of clusters containing high-risk roads were identified and their risk factors were characterized. Through this study we have opened a new research area of traffic risk mining.