Traffic Risk Mining Using Partially Ordered Non-Negative Matrix Factorization
Taito Lee, Shin Matsushima, Kenji Yamanishi · 2016
A large amount of traffic-related data, including traffic statistics, accident statistics, road information, and drivers' and pedestrians' comments, is being collected through sensors and social media networks. We focus on the issue of extracting traffic risk factors from such heterogeneous data and ranking locations according to the extracted factors. In general, it is difficult to define traffic risk. We may adopt a clustering approach to identify groups of risky locations, where the risk factor is extracted by comparing the groups. Furthermore, we may utilize prior knowledge about partially ordered relations such that a specific location should be more risky than others. In this paper, we propose a novel method for traffic risk mining by unifying the clustering approach with prior knowledge with respect to order relations. Specifically, we propose the partially ordered non-negative matrix factorization (PONMF) algorithm, which is capable of clustering locations under partially ordered relations among them. The key idea is to employ the multiplicative update rule as well as the gradient descent rule for parameter estimation. Through experiments conducted using synthetic and real data sets, we show that PONMF can identify clusters that include high-risk roads and extract their risk factors.