Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories

Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories

In order to improve offline map matching accuracy of uncertain GPS trajectories, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are us...

Full description

Bibliographic Details
Main Authors: Ming Xu, Yiman Du, Jianping Wu, Yang Zhou
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/717095
id doaj-eca5b0777ece43a8902314fdcbfae303
record_format Article
spelling doaj-eca5b0777ece43a8902314fdcbfae3032020-11-24T23:13:05ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/717095717095Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain TrajectoriesMing Xu0Yiman Du1Jianping Wu2Yang Zhou3School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Civil Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Civil Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Civil Engineering, Tsinghua University, Beijing 100084, ChinaIn order to improve offline map matching accuracy of uncertain GPS trajectories, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are used as features of GPS trajectory in a CRF model, which integrates the temporal-spatial context information flexibly. The driver route preference is also used to bolster the temporal-spatial context when a low GPS sampling rate impairs the resolving power of temporal-spatial context in CRF, allowing the map matching accuracy of uncertain GPS trajectories to get improved significantly. The experimental results show that our proposed algorithm is more accurate than existing methods, especially in the case of a low-sampling-rate.http://dx.doi.org/10.1155/2015/717095
collection DOAJ
language English
format Article
sources DOAJ
author Ming Xu
Yiman Du
Jianping Wu
Yang Zhou
spellingShingle Ming Xu
Yiman Du
Jianping Wu
Yang Zhou
Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories
Mathematical Problems in Engineering
author_facet Ming Xu
Yiman Du
Jianping Wu
Yang Zhou
author_sort Ming Xu
title Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories
title_short Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories
title_full Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories
title_fullStr Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories
title_full_unstemmed Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories
title_sort map matching based on conditional random fields and route preference mining for uncertain trajectories
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2015-01-01
description In order to improve offline map matching accuracy of uncertain GPS trajectories, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are used as features of GPS trajectory in a CRF model, which integrates the temporal-spatial context information flexibly. The driver route preference is also used to bolster the temporal-spatial context when a low GPS sampling rate impairs the resolving power of temporal-spatial context in CRF, allowing the map matching accuracy of uncertain GPS trajectories to get improved significantly. The experimental results show that our proposed algorithm is more accurate than existing methods, especially in the case of a low-sampling-rate.
url http://dx.doi.org/10.1155/2015/717095
work_keys_str_mv AT mingxu mapmatchingbasedonconditionalrandomfieldsandroutepreferenceminingforuncertaintrajectories
AT yimandu mapmatchingbasedonconditionalrandomfieldsandroutepreferenceminingforuncertaintrajectories
AT jianpingwu mapmatchingbasedonconditionalrandomfieldsandroutepreferenceminingforuncertaintrajectories
AT yangzhou mapmatchingbasedonconditionalrandomfieldsandroutepreferenceminingforuncertaintrajectories
_version_ 1725599387630960640

Similar Items