Path prediction of moving objects on road networks through analyzing past trajectories

  • Authors:

  • Sang-Wook Kim;Jung-Im Won;Jong-Dae Kim;Miyoung Shin;Junghoon Lee;Hanil Kim

  • Affiliations:

  • School of Information and Communications, Hanyang University, Korea;School of Information and Communications, Hanyang University, Korea;School of Information and Communications, Hanyang University, Korea;School of Electrical Engineering and Computer Science, Kyungpook National University, Korea;Dept. of Computer Science and Statistics, Cheju National University, Korea;Dept. of Computer Science and Statistics, Cheju National University, Korea

  • Venue:
  • KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
  • Year:
  • 2007

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Abstract

This paper addresses a series of techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus on the objects moving over Euclidean space. A variety of applications such as telematics, however, require us to handle the objects that move over road networks. In this paper, we propose a novel method for predicting a future path of an object in an efficient way by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. For this purpose, we devise a new function for measuring a similarity between trajectories by considering the characteristics of road networks. By using this function, we search for candidate trajectories whose subtrajectories are similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories. Also, we suggest a method that improves the accuracy of path prediction by grouping those moving paths whose differences are not significant.