Traffic Incident Detection: A Trajectory-based Approach

Han, Xiaolin; Grubenmann, Tobias; Cheng, Reynold; Wong, Sze Chun; Li, Xiaodong; Sun, Wenya

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. 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.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2020 IEEE 36th International Conference on Data Engineering (ICDE)
• Start Date: Apr 20, 2020
• End Date: Apr 24, 2020
• Online Publication Date: May 27, 2020
• Publication Date: 2020
• Deposit Date: Jun 8, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Series ISSN: 2375-026X
• Book Title: 2020 IEEE 36th International Conference on Data Engineering (ICDE)
• DOI: https://doi.org/10.1109/icde48307.2020.00190
• Keywords: Data Mining, Traffic Incident Detection, Sparsity