Trajectory Data Mining: An Overview

  • Yu Zheng

ACM Transaction on Intelligent Systems and Technology |

View Publication

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a roadmap from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.


A tutorial on Trajectory data mining can be found at here.

Slide decks for different parts of this survey paper.

  1. Trajectory Preprocessing (Slides)
  2. Trajectory Data Management (Slides)
  3. Trajectory Uncertainty (Slides)
  4. Trajectory Pattern Mining (Slides)
  5. Trajectory Classification (Slides)
  6. Anomaly Detection in Trajectories (Slides)
  7. Turning Trajectories into Graphs (Slides)
  8. Turning Trajectories into Matrix (Slides)
  9. Turning Trajectories into Tensors (Slides)

Publication Downloads

GeoLife GPS Trajectories

August 9, 2012

This is a GPS trajectory dataset collected in (Microsoft Research Asia) GeoLife project by 182 users in a period of over three years (from April 2007 to August 2012). A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which contains the information of latitude, longitude and altitude. This dataset contains 17,621 trajectories with a total distance of about 1.2 million kilometers and a total duration of 48,000+ hours. These trajectories were recorded by different GPS loggers and GPS-phones, and have a variety of sampling rates. 91 percent of the trajectories are logged in a dense representation, e.g. every 1~5 seconds or every 5~10 meters per point. This dataset recoded a broad range of users' outdoor movements, including not only life routines like go home and go to work but also some entertainments and sports activities, such as shopping, sightseeing, dining, hiking, and cycling. This trajectory dataset can be used in many research fields, such as mobility pattern mining, user activity recognition, location-based social networks, location privacy, and location recommendation.