Road traffic flow prediction based on neural Network
DOI:
https://doi.org/10.54097/zp8ch042Keywords:
Traffic flow forecasting ; neural networks ; data preprocessing ; correlation.Abstract
The key to the implementation of urban traffic flow guidance system is to forecast the road traffic flow. This thesis mainly studies the prediction of traffic flow by neural network and the logical prediction of traffic flow. When the traffic flow information is regarded as a time series, the traffic flow can be regarded as a random time series, using the correlation between the data and the internal connection between the adjacent data. BP algorithm has a strong ability to deal with nonlinear problems, imitate bionic learning and self-organization, and occupies a certain advantage in dealing with nonlinear and uncertain traffic information. This paper mainly uses BP algorithm to build a traffic flow prediction model. In this paper, the traffic flow data of Minneapolis from 2012 to 2018 are preprocessed, and the traffic flow prediction model is used to make a better prediction of the traffic flow data of Minneapolis.
References
Zhao Ping, Li Xin, Zhu Shaowu. Traffic congestion and anomaly prediction based on spatiotemporal graph attention neural network [J]. Science, Technology and Engineering, 2022, 22(03): 1271-1278.
Pitakrat T ,Okanovit D ,Hoorn V A , et al.Hora: Architecture-aware online failure prediction[J].The Journal of Systems Software,2018,137669-685.
Cao Jie, Shen Jun'er, Zhang Hong, et al. Short-term prediction of traffic flow using BP neural network based on wavelet and multi-dimensional reconstruction [J]. Computer Applications and Software, 2018, 35(12): 61-.
Zhu Yongqiang, Wang Xiaofan. Traffic flow prediction model based on complementary ensemble empirical mode decomposition and genetic least squares support vector machine [J]. Science, Technology and Engineering, 2020, 20(17): 7088-7092.
Xie Haihong, Dai Xuhao, Qi Yuan. Improved K nearest neighbor algorithm for short-term traffic flow prediction [J]. Transactions of Transportation Engineering, 2014, 14(03): 87-94
Zhao Ping, Li Xin, Zhu Shaowu. Traffic congestion and anomaly prediction based on spatiotemporal graph attention neural network [J]. Science, Technology and Engineering, 2022, 22(03): 1271-1278
Li Juan, Shao Chunfu. Traffic accident prediction model based on BP neural network [J]. Transportation and Computers, 2006, (02): 34-37.
Zhan Wei, Lv Qing, Shang Yuequan. Grey Markov Prediction of Traffic Accidents in Highway Tunnel Groups [J]. Journal of Jilin University (Engineering Edition), 2014, 44 (01): 62-67. DOI: 10.13229/j.cnki. jdxbgxb201401011
Yan Xu, Fan Xiaoliang, Zheng Chuanpan, Zang Yu, Wang Cheng, Cheng Ming, Chen Longbiao. Urban traffic situation prediction algorithm based on graph convolutional neural network [J]. Journal of Zhejiang University (Engineering Edition), 2020, 54(06) :1147-1155.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
