Mining Large-Scale, Sparse GPS Traces for Map Inference: Comparison of Approaches
Liu, Xuemei; Biagioni, James; Eriksson, Jakob; Wang, Yin; Forman, George; Zhu, Yanmin
Keyword(s): spatial data mining; GPS; map inference; road maps
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 b2 sand 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.
External Posting Date: June 06, 2012 [Fulltext]. Approved for External Publication
Internal Posting Date: June 06, 2012 [Fulltext]