Research Article
Short-Term Traffic Flow Prediction of Airspace Sectors Based on Multiple Time Series Learning Mechanism
@INPROCEEDINGS{10.1007/978-3-030-19086-6_47, author={Zhaoning Zhang and Kexuan Liu and Fei Lu and Wenya Li}, title={Short-Term Traffic Flow Prediction of Airspace Sectors Based on Multiple Time Series Learning Mechanism}, proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings}, proceedings_a={ADHIP}, year={2019}, month={5}, keywords={Traffic flow prediction Learning mechanism Airspace sectors Flight scheduling}, doi={10.1007/978-3-030-19086-6_47} }- Zhaoning Zhang
Kexuan Liu
Fei Lu
Wenya Li
Year: 2019
Short-Term Traffic Flow Prediction of Airspace Sectors Based on Multiple Time Series Learning Mechanism
ADHIP
Springer
DOI: 10.1007/978-3-030-19086-6_47
Abstract
Firstly, by analyzing the original radar data of the aircraft in the airspace system, the historical operation information of each sector is extracted, and the traffic flow correlation between different routes of the same sector is considered. According to the characteristics of busy sector traffic flow data, a multi-dimensional data model of traffic flow with multiple related routes in the sector is constructed. Secondly, based on the data model, a traffic flow forecasting algorithm based on multi-time series machine learning is proposed. The main core idea of the algorithm is to use the time series clustering method to reduce the dimensionality of multi-dimensional traffic flow data, and then introduce the machine learning method for concurrent training. The training result obtains the optimal classifier group through competition. Finally, the multi-optimal machine learning integrated prediction method is designed to predict traffic flow. Taking the typical busy sector in China as an example, the proposed prediction method is verified. The research results show that the prediction results are better than the traditional single time series machine learning method, and the stability of the prediction results is good, which can fully reflect the dynamics and uncertainty of short-term traffic flow between sectors in each airspace, in line with the actual situation of air traffic.