Distinguishing Trajectories from Different Drivers using Incompletely Labeled Trajectories

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22 Citations (Scopus)


We consider a scenario that occurs often in the auto insurance industry. We are given a large collection of trajectories that stem from many different drivers. Only a small number of the trajectories are labeled with driver identifiers, and only some drivers are used in labels. The problem is to label correctly the unlabeled trajectories with driver identifiers. This is important in auto insurance to detect possible fraud and to identify the driver in, e.g., pay-as-you-drive settings when a vehicle has been involved in an incident. To solve the problem, we first propose a Trajectory-to-Image( T2I) encoding scheme that captures both geographic features and driving behavior features of trajectories in 3D images. Next, we propose a multi-task, deep learning model called T2INet for estimating the total number of drivers in the unlabeled trajectories, and then we partition the unlabeled trajectories into groups so that the trajectories in a group belong to the same driver. Experimental results on a large trajectory data set offer insight into the design properties of T2INet and demonstrate that T2INet is capable of outperforming baselines and the state-of-the-art method.
Original languageEnglish
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
Number of pages10
PublisherAssociation for Computing Machinery
Publication date17 Oct 2018
ISBN (Electronic)978-1-4503-6014-2
Publication statusPublished - 17 Oct 2018
Event27th ACM International Conference on Information and Knowledge Management - Torino, Italy
Duration: 22 Oct 201826 Oct 2018


Conference27th ACM International Conference on Information and Knowledge Management
Internet address


  • Deep learning
  • Multi-task learning
  • Representation learning
  • Trajectory analysis

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