TY - JOUR
T1 - FDM
T2 - Effective and efficient incident detection on sparse trajectory data
AU - Han, Xiaolin
AU - Grubenmann, Tobias
AU - Ma, Chenhao
AU - Li, Xiaodong
AU - Sun, Wenya
AU - Wong, Sze Chun
AU - Shang, Xuequn
AU - Cheng, Reynold
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/11
Y1 - 2024/11
N2 - 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.
AB - 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.
KW - Sparsity
KW - Traffic incident detection
KW - Trajectory data mining
UR - http://www.scopus.com/inward/record.url?scp=85195172526&partnerID=8YFLogxK
U2 - 10.1016/j.is.2024.102418
DO - 10.1016/j.is.2024.102418
M3 - 文章
AN - SCOPUS:85195172526
SN - 0306-4379
VL - 125
JO - Information Systems
JF - Information Systems
M1 - 102418
ER -