Open AccessMethod of speed data fusion based on Bayesian combination algorithm and high‐order multi‐variable Markov model
Author(s) -
Zhang Weibin,
Qi Yong,
Zhou Zhuping,
Biancardo Salvatore A.,
Wang Yinhai
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5020
Subject(s) - computer science , data mining , sensor fusion , data integration , intelligent transportation system , merge (version control) , data quality , bayesian probability , data set , data type , algorithm , machine learning , artificial intelligence , engineering , metric (unit) , information retrieval , operations management , civil engineering , programming language
The variety of data collecting and communication methods used in intelligent transportation systems such as sensors, cameras, and communication networks bring about huge volumes of data that are available for numerous transportation applications and related research on smart cities. However, it is still a challenge to integrate these heterogeneous data sources into a singular data schema in practice. Compared to a single data source, higher data accuracy can be obtained through integration of the multiple data sources if the data quality from each source has been known. In this study, a data fusion method based on Bayesian fusion rules is proposed to merge traffic speed from different data sources according to their prior probability that can be inferred from a high‐order multivariable Markov model by considering the relations of multiple traffic factors in a systemic perspective. Case studies based on freeway data, such as loop data, INRIX data, and data from the National Performance Management and Research Data Set, are performed to validate the effectiveness of proposed speed fusion method.