An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data

An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information...

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Vydáno v:PloS one Ročník 12; číslo 12; s. e0188796
Hlavní autoři: Tang, Jinjun, Zhang, Shen, Zou, Yajie, Liu, Fang
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 05.12.2017
Public Library of Science (PLoS)
Témata:
Accuracy
Adaptive algorithms
Adaptive systems
Algorithms
Analysis
Biology and Life Sciences
Case studies
Computer and Information Sciences
Computing time
Data collection
Driving
Earth Sciences
Engineering
Engineering and Technology
Fuzzy logic
Fuzzy sets
Fuzzy systems
Geographic information systems
Geography
Global Positioning System
Global positioning systems
GPS
Inference
International conferences
Kalman filters
Laboratories
Machine learning
Matching
Methods
Navigation systems
Physical Sciences
Research and Analysis Methods
Researchers
Roads
Roads & highways
Sensors
Shape recognition
Social Sciences
Traffic congestion
Traffic control
Trajectories
Trajectories (Physics)
Transportation planning
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0188796