A comprehensive study of speed prediction in transportation system: From vehicle to traffic

Zhou, Zewei; Yang, Ziru; Zhang, Yuanjian; Huang, Yanjun; Chen, Hong; Yu, Zhongliang

doi:10.1016/j.isci.2022.103909

Cited by 28 publications

(9 citation statements)

References 166 publications

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“…Accurate vehicle speed prediction plays a significant role in real‐time energy management strategies and online optimization control of vehicles. [ 34 ] The conversion of hydraulic accumulator energy depends on the proper release and storage of hydraulic energy. The proper functioning of the accumulator can enhance the performance and efficiency of MSEHV.…”

Section: Time Series Forecasting Modelsmentioning

confidence: 99%

Research on Multistate Variable Prediction of a Master‐Slave Electric‐Hydraulic Hybrid Vehicle Based on CNN‐LSTM Model with Attention Mechanism

Chen,

Zhang,

Zhang

et al. 2023

Energy Tech

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This paper utilizes a convolutional neural network and long short‐term memory with attention mechanism (CLA) prediction model for time series forecasting. An innovative master‐slave electric‐hydraulic hybrid vehicle (MSEHV) is introduced. It simplifies the drivetrain structure by utilizing planetary gears for energy transfer and coupling to transform electrical, mechanical, and hydraulic energy. This paper conducts simulation verification using existing control strategies. It combines the velocity time series information with the hydraulic accumulator pressure time series information to create a unified time series. Some of the more popular machine learning and neural network algorithms are considered, and the mean square error, mean absolute error, and coefficient of determination evaluation metrics are calculated for validation. The research presents a developed battery state of charge regression prediction model based on genetic algorithm‐backpropagation by combining the predicted results of composite time series with the current discharge depth of the battery. The validation results indicate that the CLA time series prediction model proposed in this paper for MSEHV has higher prediction accuracy compared to other prediction models.

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Section: Time Series Forecasting Modelsmentioning

confidence: 99%

Research on Multistate Variable Prediction of a Master‐Slave Electric‐Hydraulic Hybrid Vehicle Based on CNN‐LSTM Model with Attention Mechanism

Chen,

Zhang,

Zhang

et al. 2023

Energy Tech

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“…More specifically, speed information at the segment level, when compared to the predicted (expected) speed, can potentially reveal problematic congestion in real-time and inform the mitigation actions of TMCs. While most of the early studies only considered speed with respect to time (Vanajakshi and Rilett, 2004;Guo and Williams, 2010), there is a recent ongoing effort to incorporate the underlying spatial dependencies into the models (Dai et al, 2020;Zhou et al, 2022).…”

Section: Speedmentioning

confidence: 99%

Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

Shaygan¹,

Meese²,

Li³

et al. 2022

Transportation Research Part C: Emerging Technologies

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“…The development of deep learning has enabled the application of several deep-learning-based forecast models in traffic and transport fields [ 3 , 4 ]. However, traffic conditions have complex, irregular, and nonlinear spatial and temporal relationships [ 5 , 6 ]. The urban road network is complex, irregular, and topological and is challenging to manage conventionally.…”

Section: Introductionmentioning

confidence: 99%

ADSTGCN: A Dynamic Adaptive Deeper Spatio-Temporal Graph Convolutional Network for Multi-Step Traffic Forecasting

Cui

Noh

Park

2023

Sensors

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Multi-step traffic forecasting has always been extremely challenging due to constantly changing traffic conditions. Advanced Graph Convolutional Networks (GCNs) are widely used to extract spatial information from traffic networks. Existing GCNs for traffic forecasting are usually shallow networks that only aggregate two- or three-order node neighbor information. Because of aggregating deeper neighborhood information, an over-smoothing phenomenon occurs, thus leading to the degradation of model forecast performance. In addition, most existing traffic forecasting graph networks are based on fixed nodes and therefore need more flexibility. Based on the current problem, we propose Dynamic Adaptive Deeper Spatio-Temporal Graph Convolutional Networks (ADSTGCN), a new traffic forecasting model. The model addresses over-smoothing due to network deepening by using dynamic hidden layer connections and adaptively adjusting the hidden layer weights to reduce model degradation. Furthermore, the model can adaptively learn the spatial dependencies in the traffic graph by building the parameter-sharing adaptive matrix, and it can also adaptively adjust the network structure to discover the unknown dynamic changes in the traffic network. We evaluated ADSTGCN using real-world traffic data from the highway and urban road networks, and it shows good performance.

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A comprehensive study of speed prediction in transportation system: From vehicle to traffic

Cited by 28 publications

References 166 publications

Research on Multistate Variable Prediction of a Master‐Slave Electric‐Hydraulic Hybrid Vehicle Based on CNN‐LSTM Model with Attention Mechanism

Research on Multistate Variable Prediction of a Master‐Slave Electric‐Hydraulic Hybrid Vehicle Based on CNN‐LSTM Model with Attention Mechanism

Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

ADSTGCN: A Dynamic Adaptive Deeper Spatio-Temporal Graph Convolutional Network for Multi-Step Traffic Forecasting

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