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Study on Traffic Multi-Source Data Fusion

Study on Traffic Multi-Source Data Fusion

Suping Liu, Dongbo Zhang, Jialin Li
Copyright: © 2019 |Volume: 13 |Issue: 2 |Pages: 13
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522564577|DOI: 10.4018/IJCINI.2019040105
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MLA

Liu, Suping, et al. "Study on Traffic Multi-Source Data Fusion." IJCINI vol.13, no.2 2019: pp.63-75. http://doi.org/10.4018/IJCINI.2019040105

APA

Liu, S., Zhang, D., & Li, J. (2019). Study on Traffic Multi-Source Data Fusion. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 13(2), 63-75. http://doi.org/10.4018/IJCINI.2019040105

Chicago

Liu, Suping, Dongbo Zhang, and Jialin Li. "Study on Traffic Multi-Source Data Fusion," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 13, no.2: 63-75. http://doi.org/10.4018/IJCINI.2019040105

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Abstract

In order to alleviate urban traffic congestion, it is necessary to obtain roadway network traffic flow parameters to estimate the traffic conditions. Single-detector data may not be sufficient to obtain a comprehensive, effective, accurate and high-quality traffic flow data. Neural networks and regression analysis data fusion methods are employed to expand data sources as well as for improving data quality. The multi-source detector data can provide fundamental support for traffic management. An empirical analysis was conducted using acquisition technology employed by the Beijing urban expressway to estimate traffic flow parameters. The results show that the proposed data fusion method is feasible and provides reliable data sources.

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