FDM: Effective and efficient incident detection on sparse trajectory data

Xiaolin Han, Tobias Grubenmann, Chenhao Ma, Xiaodong Li, Wenya Sun, Sze Chun Wong, Xuequn Shang, Reynold Cheng

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Incident detection (ID), or the automatic discovery of anomalies from road traffic data (e.g., road sensor and GPS data), enables emergency actions (e.g., rescuing injured people) to be carried out in a timely fashion. Existing ID solutions based on data mining or machine learning often rely on dense traffic data; for instance, sensors installed in highways provide frequent updates of road information. In this paper, we ask the question: can ID be performed on sparse traffic data (e.g., location data obtained from GPS devices equipped on vehicles)? As these data may not be enough to describe the state of the roads involved, they can undermine the effectiveness of existing ID solutions. To tackle this challenge, we borrow an important insight from the transportation area, which uses trajectories (i.e., moving histories of vehicles) to derive incident patterns. We study how to obtain incident patterns from trajectories and devise a new solution (called Filter-Discovery-Match (FDM)) to detect anomalies in sparse traffic data. We have also developed a fast algorithm to support FDM. Experiments on a taxi dataset in Hong Kong and a simulated dataset show that FDM is more effective than state-of-the-art ID solutions on sparse traffic data, and is also efficient.

Original languageEnglish
Article number102418
JournalInformation Systems
Volume125
DOIs
StatePublished - Nov 2024

Keywords

  • Sparsity
  • Traffic incident detection
  • Trajectory data mining

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