Analysis of road traffic speed in Kunming plateau mountains: a fusion PSO-LSTM algorithm

Mao, Yao; Qin, Guojin; Ni, Pingan; Liu, Qian

doi:10.1080/12265934.2021.1882331

Cited by 24 publications

(10 citation statements)

References 50 publications

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“…The traffic data characteristic analysis is helpful to study the change rule of the data itself, according to which we can choose a more appropriate algorithm to study the traffic prediction to achieve better analysis and results. The general data characteristic analysis includes the data similarity analysis and the periodicity analysis [37][38] [39].…”

Section: ) Multivariate Data Characteristic Analysismentioning

confidence: 99%

Attention-based bidirectional long short-term memory for urban traffic prediction using multivariate data

Yang,

Wang,

Yang

et al. 2023

Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023)

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With the continuous expansion of urban and population size, the external cost of traffic congestion is increasing daily. Urban traffic prediction using multivariate data is significant in solving these problems. However, traditional prediction methods are mostly based on merely statistical theory, which only includes a single variable, resulting in the precision accuracy not being guaranteed. Therefore, based on machine learning theory, this paper collects multivariate traffic data from the main sections of Shenzhen, China. With the help of the advantages of deep learning model in data analysis, urban traffic prediction using multivariate data is proposed based on considering the characteristics of multi-parameters, which solves the problem that the traffic prediction method is mainly based on a single variable and the auxiliary information is not considered enough. To fully consider the spatial-temporal characteristics of the traffic dataset, an ABi-LSTM prediction method concerning the influence of upstream and downstream road sections is proposed, solving the problem of low prediction accuracy caused by imperfect consideration of spatial-temporal characteristics in previous traffic predictions. The results provide an important reference for traffic management departments to alleviate traffic congestion effectively, moreover, it's convenient for residents to understand road information more clearly and make reasonable travel choices.

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Section: ) Multivariate Data Characteristic Analysismentioning

confidence: 99%

Attention-based bidirectional long short-term memory for urban traffic prediction using multivariate data

Yang,

Wang,

Yang

et al. 2023

Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023)

View full text Add to dashboard Cite

show abstract

“…Finally, PSO is used to optimize the hyperparameters of LSTM because of its simplicity and ease of implementation 61 . The core idea of the PSO algorithm is to first initialize a set of random solutions and then iteratively find the optimal solution 62 .…”

Section: Proposed Air Pollution Forecasting Modelmentioning

confidence: 99%

A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks

Zhu

et al. 2022

Sci Rep

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In recent years, air pollution has become a factor that cannot be ignored, affecting human lives and health. The distribution of high-density populations and high-intensity development and construction have accentuated the problem of air pollution in China. To accelerate air pollution control and effectively improve environmental air quality, the target of our research was cities with serious air pollution problems to establish a model for air pollution prediction. We used the daily monitoring data of air pollution from January 2016 to December 2020 for the respective cities. We used the long short term memory networks (LSTM) algorithm model to solve the problem of gradient explosion in recurrent neural networks, then used the particle swarm optimization algorithm to determine the parameters of the CNN-LSTM model, and finally introduced the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) decomposition to decompose air pollution and improve the accuracy of model prediction. The experimental results show that compared with a single LSTM model, the CEEMDAN-CNN-LSTM model has higher accuracy and lower prediction errors. The CEEMDAN-CNN-LSTM model enables a more precise prediction of air pollution, and may thus be useful for sustainable management and the control of air pollution.

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“…Structure of LSTM Neural Network. The LSTM neural network structure is based on the recurrent neural network (RNN) structure [21][22][23], which is shown in Figure 2. U, V, and W in Figure 2 are the connection weights of neurons in each layer.…”

Section: Financial Crisis Prediction Based On Lstmmentioning

confidence: 99%

Financial Crisis Prediction Based on Long-Term and Short-Term Memory Neural Network

Ling

Yin-ying

2022

Wireless Communications and Mobile Computing

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Enterprise financial crisis prediction analysis can predict the business process of enterprises, so that enterprises can take corresponding strategies in time. The financial crisis prediction of listed companies can effectively reflect the business situation, so as to give investors reasonable investment advice. In order to supervise the sustainable management ability of enterprises efficiently and accurately, this paper proposed a financial crisis prediction method based on long-term and short-term memory neural network, so as to provide valuable information for decision-makers. Firstly, the data in the enterprise financial system is analyzed and extracted, and the original data is cleaned and dimensionalized by normalization and feature selection. Then, the long-term and short-term memory neural network is used to build the financial early warning model, and the wolf pack algorithm is used to optimize the initial weight and bias parameters, so as to improve the efficiency of parameter optimization. Finally, the financial data of 20 large- and medium-sized enterprises from 2019 to 2021 are verified and analyzed. The experimental results show that compared with other common machine learning methods, the constructed wolf pack-optimized long-term and short-term memory neural network has the highest prediction performance in terms of root mean square error and goodness of fit, with the goodness of fit reaching 94.2%.

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Analysis of road traffic speed in Kunming plateau mountains: a fusion PSO-LSTM algorithm

Cited by 24 publications

References 50 publications

Attention-based bidirectional long short-term memory for urban traffic prediction using multivariate data

Attention-based bidirectional long short-term memory for urban traffic prediction using multivariate data

A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks

Financial Crisis Prediction Based on Long-Term and Short-Term Memory Neural Network

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