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

In order to improve offline map matching accuracy of low-sampling-rate GPS, 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...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Xu, Ming, Yi-man, Du, Wu, Jian-ping, Zhou, Yang
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.11.2014
Subjects
Algorithms
Computer Science - Learning
Computer Science - Networking and Internet Architecture
Conditional random fields
Matching
Replenishment
Route selection
Sampling
Trajectories
Online AccessGet full text

Cover

More Information
Summary:In order to improve offline map matching accuracy of low-sampling-rate GPS, 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 CRF model, which can utilize the advantages of integrating the context information into features flexibly. When the sampling rate is too low, it is difficult to guarantee the effectiveness using temporal-spatial context modeled in CRF, and route preference of a driver is used as replenishment to be superposed on the temporal-spatial transition features. The experimental results show that this method can improve the accuracy of the matching, especially in the case of low sampling rate.
ISSN:2331-8422
DOI:10.48550/arxiv.1410.4461