Research on Air Traffic Flow Forecast Based on ELM Non-Iterative Algorithm

Zhang, Zhaoyue; Zhang, An; Sun, Cong; Xiang, Shuaida; Guan, Jichen; Huang, Xuedong

doi:10.1007/s11036-020-01679-0

Cited by 18 publications

(10 citation statements)

References 27 publications

(25 reference statements)

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“…is task is with the existing historical data, acquisition methods mainly include in-road coil detection, roadside microwave measurement device detection, floating car data, and video recognition. By establishing an appropriate traffic forecast model, the traffic flow operation state in a certain time period in the future can be predicted [11][12][13][14][15]. A GRU model with one fewer gate can be utilized to create predictions, according to reference [16].…”

Section: Introductionmentioning

confidence: 99%

Urban Traffic Flow Prediction with Deep Neural Network

Yang

2022

Security and Communication Networks

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It is critical to realize accurate collecting, visualization, rule mining, and prediction analysis of the traffic flow operating state in order for the intelligent transportation system to achieve exact management and control of traffic flow. Traffic flow prediction is primarily concerned with traffic data on roadways, which has both temporal and spatial correlations. Aiming at the spatiotemporal characteristics, this paper studies two aspects and designs a traffic flow prediction model with a deep neural network. First, this work proposes a traffic flow spatial feature learning algorithm with the combination of graph convolutional neural network and attention mechanism. Distinct weights are assigned to the degree of mutual impact between different nodes, and node adaptive learning is implemented at the same time, which modifies the standard parameter sharing mode, allowing for improved expressive ability and spatial feature extraction. Secondly, a learning algorithm for temporal characteristics of traffic flow based on the temporal convolutional network is proposed, which ensures that the dimensions of input and output data are consistent through causal convolution. The dilated convolution can flexibly control the receptive field by setting the sampling interval and can also extract temporal features well for long-length spatiotemporal sequence data. Finally, a spatiotemporal graph attention-based traffic flow prediction approach is constructed. To learn features, learn parameters for multiple modes and improve the model effect, this model employs a combination of graph convolutional neural networks and an attention mechanism. It uses a temporal convolutional network to expand the receptive field, better capture temporal features, and finally add residual connections to prevent problems such as overfitting caused by too deep network layers.

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Section: Introductionmentioning

confidence: 99%

Urban Traffic Flow Prediction with Deep Neural Network

Yang

2022

Security and Communication Networks

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“…Then, the second section of this issue includes the other five papers, which focuses on the enhanced learning methods using in real application, such as forecast, localization, as well as various kinds of applilcations [11][12][13][14][15].…”

Section: Enhanced Learning Methods In Real Applicationmentioning

confidence: 99%

An Introduction to Multimedia Technology and Enhanced Learning

Xia

Liu

2020

Mobile Netw Appl

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“…Ma et al described traffic flow trends from different angles, and proposed multi-parameter chaotic prediction methods [6]. Zhang et al, based on the chaos characteristics of air traffic, proposed a high-dimensional prediction model for air traffic flow time series based on a non-iterative Phase-space Reconstruction-Extreme Learning Machine algorithm [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on prediction method of expressway section traffic flow considering anomaly detection probability

Wang,

Li,

et al. 2023

IET Intelligent Trans Sys

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Real‐time and accurate short‐term traffic flow prediction can provide a scientific basis for decision making by travellers and traffic management, and alleviate traffic congestion to a certain extent. The existing traffic flow prediction methods often encounter limitations in real‐time performance and accuracy due to the post‐processing required to rectify anomaly detection results during data pre‐processing. This paper presents a novel traffic flow prediction model, termed the With‐Anomaly Detection Probability (ADP) Attention‐Bidirectional Long‐Short Term Memory (BiLSTM) model. This model takes the probability of anomaly detection into consideration, integrating the anomaly detection outcomes as an inherent parameter into the traffic flow prediction framework. Additionally, the model incorporates an attention mechanism within the long‐short term memory network. Through a comprehensive simulation study utilizing actual measured traffic flow data from the Shanghai‐Chongqing Expressway, the effectiveness of the proposed model is rigorously evaluated. The prediction results show that the model proposed in this paper is a real‐time and accurate short‐time traffic flow prediction model compared with the basic models.

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Research on Air Traffic Flow Forecast Based on ELM Non-Iterative Algorithm

Cited by 18 publications

References 27 publications

Urban Traffic Flow Prediction with Deep Neural Network

Urban Traffic Flow Prediction with Deep Neural Network

An Introduction to Multimedia Technology and Enhanced Learning

Research on prediction method of expressway section traffic flow considering anomaly detection probability

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