Traffic Risk Mining From Heterogeneous Road Statistics

At present, a large amount of traffic-related data is obtained manually and through sensors and social media, e.g., traffic statistics, accident statistics, road information, and users’ comments. In this paper, the authors propose a novel framework for mining traffic risk from such heterogeneous data. Traffic risk refers to the possibility of occurrence of traffic accidents. Specifically, they focus on two issues: 1) predicting the number of accidents on any road or at intersection and 2) clustering roads to identify risk factors for risky road clusters. They present a unified approach for addressing these issues by means of feature-based non-negative matrix factorization (FNMF). In particular, they develop a new multiplicative update algorithm for the FNMF to handle big traffic data. Using real-traffic data in Tokyo, they demonstrate that the proposed algorithm can be used to predict traffic risk at any location more accurately and efficiently than existing methods, and that a number of clusters of risky roads can be identified and characterized by two risk factors. In summary, their work can be regarded as the first step to a new research area of traffic risk mining.

Language

  • English

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Filing Info

  • Accession Number: 01690055
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Nov 14 2018 1:53PM