SW-BiLSTM: a Spark-based weighted BiLSTM model for traffic flow forecasting

Xia, Dawen; Yang, Nan; Jian, Shunying; Hu, Yang; Li, Huaqing

doi:10.1007/s11042-022-12039-3

Cited by 18 publications

(7 citation statements)

References 46 publications

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“…It is worth noting that in recent academic research, the Bidirectional Long Short-Term Memory (BILSTM) architecture has garnered Each RSU hosts the "Real-Time Vehicle Speed Calculation" module, which estimates the average vehicle speed as the autonomous vehicle progresses through the managed segments within specific time intervals. It is worth noting that in recent academic research, the Bidirectional Long Short-Term Memory (BILSTM) architecture has garnered significant attention for its effectiveness in predicting vehicle traffic flow across various road segments, as highlighted in previous studies [29][30][31][32]. Given the substantial impact of traffic flow patterns on the driving speed of autonomous vehicles, this module utilizes the BILSTM model introduced in [27] to compute the average vehicle speed.…”

Section: Autonomous Vehiclementioning

confidence: 99%

Application of Bidirectional Long Short-Term Memory to Adaptive Streaming for Internet of Autonomous Vehicles

Huang,

Hu,

Cheng

2023

Biomimetics

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It is expected that interconnected networks of autonomous vehicles, especially during peak traffic, will face congestion challenges. Moreover, the existing literature lacks discussions on integrating next-generation wireless communication technologies into connected vehicular networks. Hence, this paper introduces a tailored bandwidth management algorithm for streaming applications of autonomous vehicle passengers. It leverages cutting-edge 6G wireless technology to create a network with high-speed transmission and broad coverage, ensuring smooth streaming application performance. The key features of bandwidth allocation for diverse streaming applications in this work include bandwidth relay and pre-loading of video clips assisted by vehicle-to-vehicle communication. Through simulations, this research effectively showcases the algorithm’s ability to fulfill the bandwidth needs of diverse streaming applications for autonomous vehicle passengers. Specifically, during periods of peak user bandwidth demand, it notably increases the bandwidth accessible for streaming applications. On average, users experience a substantial 55% improvement in the bandwidth they can access. This validation affirms the viability and promise of the proposed approach in efficiently managing the intricate complexities of bandwidth allocation issues for streaming services within the connected autonomous vehicular networks.

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Section: Autonomous Vehiclementioning

confidence: 99%

Application of Bidirectional Long Short-Term Memory to Adaptive Streaming for Internet of Autonomous Vehicles

Huang,

Hu,

Cheng

2023

Biomimetics

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“…Xia et al. presented a spark‐based weighted bidirectional LSTM model to improve the robustness and accuracy of traffic flow prediction [25]. Li et al.…”

Section: Related Workmentioning

confidence: 99%

“…Mirzahossein et al constructed a hybrid deep and machine learning model that combined the gated recurrent unit (GRU) and LSTM bilayer network with wavelet transform (WL) noise reduction algorithm to analyse raw traffic volume data [24]. Xia et al presented a spark-based weighted bidirectional LSTM model to improve the robustness and accuracy of traffic flow prediction [25]. Li et al utilized SARIMA and SVM to build a passenger flow prediction model for the subway in Beijing [26].…”

Section: Related Workmentioning

confidence: 99%

Hybrid models of subway passenger flow prediction based on convolutional neural network

Lai

Wang

et al. 2022

IET Intelligent Trans Sys

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Accurate and stable short‐term passenger flow prediction is an indispensable part of current intelligent transportation systems. This paper proposes two deep learning prediction models based on convolutional neural networks (CNN) and long short‐term memory neural network (LSTM). Combining the CNN characteristics and the LSTM, the ConvXD‐LSTM extracts passenger flow features through CNN and then inputs the time series into the LSTM. The ConvLSTM converts the weight calculation of the LSTM into convolution operation to realize short‐term passenger flow prediction. Fuzhou Metro Line 1 passenger flow data was used for verification. The models were used to predict the passenger flow of subway stations and cross‐sections and compared with the traditional prediction models. In terms of prediction accuracy, ConvLSTM has the highest accuracy, followed by ConvXD‐LSTM. In terms of running time, ConvXD is the fastest and LSTM takes the longest. ConvXD‐LSTM and ConvLSTM are in the middle of the two models, achieving a good balance between accuracy and efficiency. Compared with ConvXD‐LSTM, ConvLSTM has a relatively simple network structure, which reduces the computational burden and improves the prediction accuracy.

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“…Inspired by bidirectional RNN networks, a bidirectional LSTM (BiLSTM) network structure is formed by combining forward LSTM and backward LSTM, which performs bidirectional data learning and effectively improves prediction accuracy ( Siami-Namini, Tavakoli & Namin, 2019 ). Applications for the BiLSTM-based prediction include wind speed prediction for wind farms ( Moharm, Eltahan & Elsaadany, 2020 ), short-term electricity load prediction ( Sekhar & Dahiya, 2023 ), daily precipitation data prediction ( Arsenault et al, 2019 ), and traffic flow prediction ( Xia et al, 2022 ). Many studies have shown that artificial intelligence-based models outperform traditional predictive models.…”

Section: Introductionmentioning

confidence: 99%

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM

Qi,

Wang,

et al. 2024

PeerJ Computer Science

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As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.

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SW-BiLSTM: a Spark-based weighted BiLSTM model for traffic flow forecasting

Cited by 18 publications

References 46 publications

Application of Bidirectional Long Short-Term Memory to Adaptive Streaming for Internet of Autonomous Vehicles

Application of Bidirectional Long Short-Term Memory to Adaptive Streaming for Internet of Autonomous Vehicles

Hybrid models of subway passenger flow prediction based on convolutional neural network

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM

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