Mining large-scale, sparse GPS traces for map inference: comparison of approaches

  • Authors:

  • Xuemei Liu;James Biagioni;Jakob Eriksson;Yin Wang;George Forman;Yanmin Zhu

  • Affiliations:

  • Shanghai Jiao Tong University, Shanghai, China;University of Illinois at Chicago, Chicago, IL, USA;University of Illinois at Chicago, Chicago, IL, USA;Hewlett-Packard Labs, Palo Alto, CA, USA;Hewlett-Packard Labs, Palo Alto, CA, USA;Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2012

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Abstract

We address the problem of inferring road maps from large-scale GPS traces that have relatively low resolution and sampling frequency. Unlike past published work that requires high-resolution traces with dense sampling, we focus on situations with coarse granularity data, such as that obtained from thousands of taxis in Shanghai, which transmit their location as seldom as once per minute. Such data sources can be made available inexpensively as byproducts of existing processes, rather than having to drive every road with high-quality GPS instrumentation just for map building - and having to re-drive roads for periodic updates. Although the challenges in using opportunistic probe data are significant, successful mining algorithms could potentially enable the creation of continuously updated maps at very low cost. In this paper, we compare representative algorithms from two approaches: working with individual reported locations vs. segments between consecutive locations. We assess their trade-offs and effectiveness in both qualitative and quantitative comparisons for regions of Shanghai and Chicago.