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. The neural network and regression analysis data fusion methods are employed to expand data sources as well as improve data quality. The multi-source detector data can provide fundamental support for traffic management. An empirical analysis is conducted using Beijing urban expressway traffic flow parameters acquisition technology. The results show that the proposed data fusion method is feasible and can provide reliable data sources.
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Liu, S. (2018). Research on Traffic Data Fusion Based on Multi Detector. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_39
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DOI: https://doi.org/10.1007/978-981-13-1648-7_39
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