Short‐term traffic flow prediction based on <scp>whale optimization algorithm</scp> optimized BiLSTM_Attention

Xu, Xing; Liu, Chengxing; Zhao, Yun; Lv, Xiaoshu

doi:10.1002/cpe.6782

Cited by 15 publications

(5 citation statements)

References 39 publications

(45 reference statements)

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“…The RMSE can be computed using Eq (8) . These equations are taken from [ 56 ]. where TT_i denotes the actual travel time and denotes the predicted travel time.…”

Section: Resultsmentioning

confidence: 99%

An attention-based recurrent learning model for short-term travel time prediction

2022

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With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).

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“…The RMSE can be computed using Eq (8) . These equations are taken from [ 56 ]. where TT_i denotes the actual travel time and denotes the predicted travel time.…”

Section: Resultsmentioning

confidence: 99%

An attention-based recurrent learning model for short-term travel time prediction

2022

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“…Consequently, numerous scholars have begun employing this algorithm in practical applications. For instance, WOA_BiLSTM_Attention [40] uses WOA to optimize the BiLSTM_Attention network model for traffic flow prediction to obtain its four optimal parameters, including the learning rate, the number of training sessions, and the number of nodes in both hidden layers. The accuracy of traffic flow prediction is thus improved.…”

Section: Swarm Intelligence Optimization Algorithmmentioning

confidence: 99%

Adaptive Graph Convolutional Recurrent Network with Transformer and Whale Optimization Algorithm for Traffic Flow Prediction

Zhang,

Wu,

Shen

et al. 2024

Mathematics

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Accurate traffic flow prediction plays a crucial role in the development of intelligent traffic management. Despite numerous investigations into spatio-temporal methods, achieving high accuracy in traffic flow prediction remains challenging. This challenge arises from the complex dynamic spatio-temporal correlations within the traffic road network and the limitations imposed by the selection of hyperparameters based on experiments and manual experience, which can affect the performance of the network architecture. This paper introduces a novel transformer-based adaptive graph convolutional recurrent network. The proposed network automatically infers the interdependencies among different traffic sequences and incorporates the capability to capture global spatio-temporal correlations. This enables the dynamic capture of long-range temporal correlations. Furthermore, the whale optimization algorithm is employed to efficiently design an optimal network structure that aligns with the requirements of the traffic domain and maximizes the utilization of limited computational resources. This design approach significantly enhances the model’s performance and improves the accuracy of traffic flow prediction. The experimental results on four real datasets demonstrate the efficacy of our approach. In PEMS03, it improves MAE by 2.6% and RMSE by 1.4%. In PEMS04, improvements are 1.6% in MAE and 1.4% in RMSE, with a similar MAPE score to the best baseline. For PEMS07, our approach shows a 4.1% improvement in MAE and 2.2% in RMSE. On PEMS08, it surpasses the current best baseline with a 3.4% improvement in MAE and 1.6% in RMSE. These results confirm the good performance of our model in traffic flow prediction across multiple datasets.

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“…To address this issue, this paper introduces a bidirectional temporal prediction network that deeply explores various hidden information in the historical sequence. The structure of the BiLSTM model [11] is shown in…”

Section: Bilstm Networkmentioning

confidence: 99%

Ultra-Short-term Electric Load Forecasting Based on VMD-BiLSTM Model

Chen,

Ma,

Chen

2023

AETR

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Fast and accurate ultra-short-term load forecasting is beneficial for building an efficient and modern smart grid. This paper proposes an ultra-short-term load forecasting model based on Variational Mode Decomposition (VMD) and improved Bi-directional Long Short-Term Memory (BiLSTM) network. The model first decomposes the complex load historical sequence using VMD, reducing the non-stationarity and complexity of the load sequence to facilitate the prediction task using neural networks. Then, the improved BiLSTM network is used to allow input data to skip some transformations in the network, thereby deeply and bidirectionally mining various hidden information from historical sequences. Finally, this paper selects temperature as the factor most correlated with power load forecasting and incorporates it into the prediction process to achieve effective ultra-short-term load forecasting. Compared with existing models such as LSTM, SVM, LeNet, DRNet4-7, and unimproved VMD-BiLSTM based on example data, the simulation results show that the proposed model can predict the load changes within one hour in the future, achieving ultra-short-term load forecasting, and has good prediction accuracy and algorithm robustness.

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Short‐term traffic flow prediction based on whale optimization algorithm optimized BiLSTM_Attention

Cited by 15 publications

References 39 publications

An attention-based recurrent learning model for short-term travel time prediction

An attention-based recurrent learning model for short-term travel time prediction

Adaptive Graph Convolutional Recurrent Network with Transformer and Whale Optimization Algorithm for Traffic Flow Prediction

Ultra-Short-term Electric Load Forecasting Based on VMD-BiLSTM Model

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