Short-Term Traffic Flow Prediction Based on Ga-Bp Neural Network
DOI: 10.23977/acss.2022.060110 | Downloads: 12 | Views: 680
Author(s)
Xinlei Wang 1, Jinfeng Xiao 1, Minghui Fan 1, Cheng Gang 1, Yuhan Tang 2
Affiliation(s)
1 College of Engineering, Tibet University, Lhasa 850000, Xizang, China
2 College of Marxism, Tibet University, Lhasa 850000, Xizang, China
Corresponding Author
Cheng GangABSTRACT
In response to the short-term traffic flow prediction, it is more sensitive in the short-time traffic flow prediction, and a short-time traffic flow prediction method optimized by genetic algorithm optimizes BP Neural Network is proposed. The weight and threshold of the BP Neural Network are processed by the genetic algorithm, and the BP Neural Network optimization is applied to adjust the real-time data predictive value. The experimental results show that the average absolute error of the algorithm is reduced by 9.1998% compared to the BP Neural Network, and the mean square error is reduced by 0.092. The average absolute error of the algorithm is reduced by 3.7229% compared to the wavelet Neural Network algorithm, and the mean square error is reduced by 0.0573. Compared to the above two algorithms, the GA-BP Neural Network algorithm has a better predictive effect, and provides a certain reference value for short-term traffic flow forecasting.
KEYWORDS
Transportation engineering, Traffic flow forecasting, Ga-bp neural network, Traffic flow dataCITE THIS PAPER
Xinlei Wang, Jinfeng Xiao, Minghui Fan, Cheng Gang, Yuhan Tang, Short-Term Traffic Flow Prediction Based on Ga-Bp Neural Network. Advances in Computer, Signals and Systems (2022) Vol. 6: 75-82. DOI: http://dx.doi.org/10.23977/acss.2022.060110.
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