Exploration of ground truth from raw GPS data

  • Authors:

  • Huajian Mao;Wuman Luo;Haoyu Tan;Lionel M. Ni;Nong Xiao

  • Affiliations:

  • National University of Defense Technology, Hunan, China;Hong Kong University of Science and Technology, Hong Kong;Hong Kong University of Science and Technology, Hong Kong;Hong Kong University of Science and Technology, Hong Kong;National University of Defense Technology, Hunan, China

  • Venue:
  • Proceedings of the ACM SIGKDD International Workshop on Urban Computing
  • Year:
  • 2012

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Abstract

To enable smart transportation, a large volume of vehicular GPS trajectory data has been collected in the metropolitan-scale Shanghai Grid project. The collected raw GPS data, however, suffers from various errors. Thus, it is inappropriate to use the raw GPS dataset directly for many potential smart transportation applications. Map matching, a process to align the raw GPS data onto the corresponding road network, is a commonly used technique to calibrate the raw GPS data. In practice, however, there is no ground truth data to validate the calibrated GPS data. It is necessary and desirable to have ground truth data to evaluate the effectiveness of various map matching algorithms, especially in complex environments. In this paper, we propose truthFinder, an interactive map matching system for ground truth data exploration. It incorporates traditional map matching algorithms and human intelligence in a unified manner. The accuracy of truthFinder is guaranteed by the observation that a vehicular trajectory can be correctly identified by human-labeling with the help of a period of historical GPS dataset. To the best of our knowledge, truthFinder is the first interactive map matching system trying to explore the ground truth from historical GPS trajectory data. To measure the cost of human interactions, we design a cost model that classifies and quantifies user operations. Having the guaranteed accuracy, truthFinder is evaluated in terms of operation cost. The results show that truthFinder makes the cost of map matching process up to two orders of magnitude less than the pure human-labeling approach.