Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network Model
Accurate, efficient, and reliable traffic flow prediction is pivotal for highway operation and management. However, traffic flow series present nonlinear, heterogeneous, and stochastic characteristics, posing significant challenges to precise prediction. To address this issue, this paper proposes a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Modelling |
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| Online Access: | https://www.mdpi.com/2673-3951/6/2/39 |
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| author | Yongjun Wu Hongyun Kang Weipin Wang Shuli Zhao Xuening He Jingyao Chen |
| author_facet | Yongjun Wu Hongyun Kang Weipin Wang Shuli Zhao Xuening He Jingyao Chen |
| author_sort | Yongjun Wu |
| collection | DOAJ |
| description | Accurate, efficient, and reliable traffic flow prediction is pivotal for highway operation and management. However, traffic flow series present nonlinear, heterogeneous, and stochastic characteristics, posing significant challenges to precise prediction. To address this issue, this paper proposes a novel wavelet-LNN model, integrating the strengths of wavelet decomposition and liquid neural networks (LNNs). Initially, multi-scale wavelet decomposition is applied to the original traffic flow data to yield approximation components and detailed components. Subsequently, each component is trained using the LNN. Ultimately, the predicted results of all components of the LNN models are aggregated to derive the final traffic flow prediction. The experiments conducted on four highway datasets demonstrate that the proposed wavelet-LNN model surpasses SVR, LSSVM, LSTM, TCN, and transformer models in prediction performance across R2, MSE, and MAE metrics. Notably, the wavelet-LNN model features the fewest parameters (<2% of typical deep learning models). |
| format | Article |
| id | doaj-art-ec2ce84f882d41e89adce6d86e062c07 |
| institution | DOAJ |
| issn | 2673-3951 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Modelling |
| spelling | doaj-art-ec2ce84f882d41e89adce6d86e062c072025-08-20T03:16:21ZengMDPI AGModelling2673-39512025-05-01623910.3390/modelling6020039Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network ModelYongjun Wu0Hongyun Kang1Weipin Wang2Shuli Zhao3Xuening He4Jingyao Chen5School of Traffic & Transportation, Chonqqing Jiaotong University, Chongqing 400074, ChinaSchool of Traffic & Transportation, Chonqqing Jiaotong University, Chongqing 400074, ChinaChongqing Expressway Network Management Co., Ltd., Chongqing 401120, ChinaChongqing Expressway Network Management Co., Ltd., Chongqing 401120, ChinaChongqing Expressway Network Management Co., Ltd., Chongqing 401120, ChinaChongqing Expressway Network Management Co., Ltd., Chongqing 401120, ChinaAccurate, efficient, and reliable traffic flow prediction is pivotal for highway operation and management. However, traffic flow series present nonlinear, heterogeneous, and stochastic characteristics, posing significant challenges to precise prediction. To address this issue, this paper proposes a novel wavelet-LNN model, integrating the strengths of wavelet decomposition and liquid neural networks (LNNs). Initially, multi-scale wavelet decomposition is applied to the original traffic flow data to yield approximation components and detailed components. Subsequently, each component is trained using the LNN. Ultimately, the predicted results of all components of the LNN models are aggregated to derive the final traffic flow prediction. The experiments conducted on four highway datasets demonstrate that the proposed wavelet-LNN model surpasses SVR, LSSVM, LSTM, TCN, and transformer models in prediction performance across R2, MSE, and MAE metrics. Notably, the wavelet-LNN model features the fewest parameters (<2% of typical deep learning models).https://www.mdpi.com/2673-3951/6/2/39traffic flow predictionwavelet decompositionliquid neural networkintelligent transportation systems |
| spellingShingle | Yongjun Wu Hongyun Kang Weipin Wang Shuli Zhao Xuening He Jingyao Chen Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network Model Modelling traffic flow prediction wavelet decomposition liquid neural network intelligent transportation systems |
| title | Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network Model |
| title_full | Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network Model |
| title_fullStr | Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network Model |
| title_full_unstemmed | Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network Model |
| title_short | Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network Model |
| title_sort | short term highway traffic flow prediction via wavelet liquid neural network model |
| topic | traffic flow prediction wavelet decomposition liquid neural network intelligent transportation systems |
| url | https://www.mdpi.com/2673-3951/6/2/39 |
| work_keys_str_mv | AT yongjunwu shorttermhighwaytrafficflowpredictionviawaveletliquidneuralnetworkmodel AT hongyunkang shorttermhighwaytrafficflowpredictionviawaveletliquidneuralnetworkmodel AT weipinwang shorttermhighwaytrafficflowpredictionviawaveletliquidneuralnetworkmodel AT shulizhao shorttermhighwaytrafficflowpredictionviawaveletliquidneuralnetworkmodel AT xueninghe shorttermhighwaytrafficflowpredictionviawaveletliquidneuralnetworkmodel AT jingyaochen shorttermhighwaytrafficflowpredictionviawaveletliquidneuralnetworkmodel |