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Short-Term Traffic Flow Prediction Based on Ga-Bp Neural Network

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DOI: 10.23977/acss.2022.060110 | Downloads: 5 | Views: 61

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 Gang

ABSTRACT

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 data

CITE 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.

REFERENCES

[1] Van Lint J W C, Hoogendoorn S P, van Zuylen H J. "Accurate freeway travel time prediction with state-space Neural Networks under missing data". Transportation Research Part C: Emerging Technologies, vol.13, no.5-6, pp.347-369, 2005.
[2] Castro-Neto M, Jeong Y S, Jeong M K, et al. "Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions". Expert systems with applications, vol.36, no.3, pp.6164-6173, 2009.
[3] Kumar S V, Vanajakshi L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data[J]. European Transport Research Review, vol.7, no.3, pp.1-9, 2015.
[4] Lippi M, Bertini M, Frasconi P. "Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning". IEEE Transactions on Intelligent Transportation Systems, vol.14, no.2, pp.871-882, 2013.
[5] Ghosh B, Basu B, O'Mahony M. "Multivariate short-term traffic flow forecasting using time-series analysis". IEEE transactions on intelligent transportation systems, vol.10, no.2, pp.246-254, 2009.
[6] Okutani I, Stephanedes Y J. "Dynamic prediction of traffic volume through Kalman filtering theory". Transportation Research Part B: Methodological, vol.18, no.1, pp.1-11, 1984.
[7] Jeong Y S, Byon Y J, Castro-Neto M M, et al. "Supervised weighting-online learning algorithm for short-term traffic flow prediction". IEEE Transactions on Intelligent Transportation Systems, vol.14, no.4, pp.1700-1707, 2013.
[8] Zargari S A, Siabil S Z, Alavi A H, et al. "A computational intelligence‐based approach for short‐term traffic flow prediction". Expert Systems, vol.29, no.2, pp.124-142, 2012.
[9] Cetin M, Comert G. "Short-term traffic flow prediction with regime switching models". Transportation Research Record, vol.1965, no.1, pp.23-31, 2006.
[10] Koesdwiady A, Soua R, Karray F. "Improving traffic flow prediction with weather information in connected cars: A deep learning approach". IEEE Transactions on Vehicular Technology, vol.65, no.12, pp.9508-9517, 2016.
[11] Vlahogianni E I, Karlaftis M G, Golias J C. "Optimized and meta-optimized Neural Networks for short-term traffic flow prediction: A genetic approach". Transportation Research Part C: Emerging Technologies, vol.13, no.3, pp.211-234, 2005.

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