An improved dynamic Chebyshev graph convolution network for traffic flow prediction with spatial-temporal attention

Liao, Lyuchao; Hu, Zhiyuan; Zheng, Yuxin; Zou, Fang; Huai, Qiu; Zhang, Maolin

doi:10.1007/s10489-021-03022-w

Cited by 17 publications

(7 citation statements)

References 24 publications

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“…Zhang et al [15] employed optimization-embedded reinforcement learning (OERL) to achieve adaptive decision-making at roundabout intersections. However, many existing studies often assume relatively simplistic dynamic traffic flows within the scene [16].…”

Section: Autonomous Driving Technology In Roundaboutsmentioning

confidence: 99%

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

Liao,

Xiao,

Xing

et al. 2024

CMES

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Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the model incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework, enhancing the vehicle's capability to comprehend complex roundabout environments and make decisions. Furthermore, the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios, effectively mitigating traffic bottlenecks and augmenting throughput efficiency. Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO, and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process, the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles (AVs). Generally, the proposed PE-DDPG model could be employed for autonomous driving in complex scenarios with imperfect data.

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Section: Autonomous Driving Technology In Roundaboutsmentioning

confidence: 99%

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

Liao,

Xiao,

Xing

et al. 2024

CMES

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“…As a resource allocation scheme, it uses limited computing resources to process more important information, which is the main means to solve the problem of information overload. Liao et al 21 proposed an improved dynamic Chebyshev GCN model. In this method, an attention mechanism based Laplacian matrix update method is proposed, which approximately constructs features from data of different periods.…”

Section: Related Workmentioning

confidence: 99%

A multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation

Huang,

Jiang,

Wang

et al. 2023

Sci Rep

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Accurate traffic flow prediction information can help traffic managers and drivers make more rational decisions and choices. To make an effective and accurate traffic flow prediction, we need to consider not only the spatio-temporal dependencies between data, but also the temporal correlation between data. However, most existing methods only consider temporal continuity and ignore temporal correlation. In this paper, we propose a multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation (LSTSC). In the model, we employed attention mechanisms to capture the spatio-temporal correlations of the sequences, and the model based on multiple decision forms demonstrated higher accuracy and reliability. The superiority of the model is demonstrated on two datasets, PeMS08 and PeMSD7(M), particularly for long-term predictions.

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“…Wang et al [21] used a learning position attention mechanism in a GCN and a Transformer to learn the global correlation. Liao et al [22] integrated a fusion attention mechanism into ChebNet to enhance the accuracy of the traffic flow prediction model. Lan et al [23] constructed a new graph to obtain the dynamic attributes of the spatial association among nodes by directly mining historical traffic flow data.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Information Graph Recursive Network for Traffic Forecasting

Cheng

Sun²,

Chang

et al. 2023

Electronics

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Accurate traffic forecasting is crucial for the advancement of smart cities. Although there have been many studies on traffic forecasting, the accurate forecasting of traffic volume is still a challenge. To effectively capture the spatio-temporal correlations of traffic data, a deep learning-based traffic volume forecasting model called the Enhanced Information Graph Recursive Network (EIGRN) is presented in this paper. The model consists of three main parts: a Graph Embedding Adaptive Graph Convolution Network (GE-AGCN), a Modified Gated Recursive Unit (MGRU), and a local information enhancement module. The local information enhancement module is composed of a convolutional neural network (CNN), a transposed convolutional neural network, and an attention mechanism. In the EIGRN, the GE-AGCN is used to capture the spatial correlation of the traffic network by adaptively learning the hidden information of the complex topology, the MGRU is employed to capture the temporal correlation by learning the time change of the traffic volume, and the local information enhancement module is employed to capture the global and local correlations of the traffic volume. The EIGRN was evaluated using the real datasets PEMS-BAY and PeMSD7(M) to assess its predictive performance The results indicate that the forecasting performance of the EIGRN is better than the comparison models.

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An improved dynamic Chebyshev graph convolution network for traffic flow prediction with spatial-temporal attention

Cited by 17 publications

References 24 publications

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

A multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation

Enhanced Information Graph Recursive Network for Traffic Forecasting

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