Feature Selection in Conditional Random Fields for Map Matching of GPS Trajectories

Map matching of the GPS trajectory serves the purpose of recovering the original route on a road network from a sequence of noisy GPS observations. It is a fundamental technique to many Location Based Services. However, map matching of a low sampling rate on urban road network is still a challenging task. In this paper, the characteristics of Conditional Random Fields with regard to inducing many contextual features and feature selection are explored for the map matching of the GPS trajectories at a low sampling rate. Experiments on a taxi trajectory dataset show that our method may achieve competitive results along with the success of reducing model complexity for computation-limited applications.

[1]  Andrew Y. Ng,et al.  On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples , 1998, ICML.

[2]  Robert B. Noland,et al.  Current map-matching algorithms for transport applications: State-of-the art and future research directions , 2007 .

[3]  Fernando Pereira,et al.  Shallow Parsing with Conditional Random Fields , 2003, NAACL.

[4]  Alexandre M. Bayen,et al.  The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data , 2014, WAFR.

[5]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields , 2010, Found. Trends Mach. Learn..

[6]  Muhammad Tayyab Asif,et al.  Online map-matching based on Hidden Markov model for real-time traffic sensing applications , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[7]  E. Horvitz,et al.  Map Matching with Travel Time Constraints , 2007 .

[8]  John Krumm,et al.  Hidden Markov map matching through noise and sparseness , 2009, GIS.

[9]  Leonidas J. Guibas,et al.  Large-scale joint map matching of GPS traces , 2013, SIGSPATIAL/GIS.

[10]  Britta Hummel,et al.  Map Matching for Vehicle Guidance , 2006 .

[11]  Xing Xie,et al.  An Interactive-Voting Based Map Matching Algorithm , 2010, 2010 Eleventh International Conference on Mobile Data Management.

[12]  Chengyang Zhang,et al.  Map-matching for low-sampling-rate GPS trajectories , 2009, GIS.

[13]  Mark W. Schmidt,et al.  Graphical model structure learning using L₁-regularization , 2010 .

[14]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[15]  Alexandre M. Bayen,et al.  The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data , 2011, IEEE transactions on intelligent transportation systems (Print).