Journal
2020 IEEE 5TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING (IEEE ICITE 2020)
Volume -, Issue -, Pages 98-101Publisher
IEEE
DOI: 10.1109/icite50838.2020.9231500
Keywords
component; machine learning; traffic forecast; LSTM; time feature
Funding
- Guilin Science and Technology Research and Development Project [20170220]
- Guangxi Innovation Driven Development Project (Major Project in Science and Technology) [2018AA13005]
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As urbanization intensifies, the status of the traffic situation predict is becoming more and more prominent. The urban traffic flow is influenced by many factors and is characterized by strong randomness. This paper combines MSE and Adam to construct a linear LSTM to realize the prediction of short-term traffic flow based on time series. The experiment result shows that LSTM can gain the periodic features of the traffic flow. It has small error and high precision for the short-term prediction of the traffic flow based on time series, which verifies the validity of LSTM.
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