Online Traffic Flow Prediction for Edge Computing-Enhanced Autonomous and Connected Vehicles

Song, Xiaoxiang; Guo, Yan; Li, Ning; Zhang, Lixiong

doi:10.1109/tvt.2021.3057109

Cited by 32 publications

(5 citation statements)

References 27 publications

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“…Also, a multi-head attention mechanism instead of self-attention provides a better performance, especially for multi-step forecasting tasks. The suitability of implementation of such models, both in the private and public sector is wide and ranges from applications in autonomous vehicles (Song et al, 2021) or mobility management to the development of adaptive traffic signal controlling systems (Kim & Jeong, 2019). Besides, its implementation may be helpful in highway construction projects (Tong et al, 2021), dealing with climate management policies (Nejad et al, 2020), or even modelling road accidents reduction policies (Rashidi et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks

Reza

Ferreira

Machado

et al. 2022

Expert Systems with Applications

125

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Section: Discussionmentioning

confidence: 99%

A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks

Reza

Ferreira

Machado

et al. 2022

Expert Systems with Applications

125

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“…Understanding the semantic-level properties of TF is required for the establishment of ITS applications. For TF analysis, tensor-based methods have been used, but their high complexity and lack of scalability make further development difficult [7]. As a result, DL models have been considered feature learners in a number of ways.…”

Section: Related Workmentioning

confidence: 99%

Masked Token Enabled Pre-Training: A Task-Agnostic Approach for Understanding Complex Traffic Flow

Hou,

Geng,

Han

et al. 2024

IEEE Trans. on Mobile Comput.

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The conventional deep learning model performs well for traffic flow analysis by training with a large number of labeled data using a one-model-for-one-task approach, leading to huge computational complexity in dynamic intelligent transportation system (ITS) applications. To overcome this limitation, this paper propose a Token-based Self-Supervised Network (TSSN), which can learn TF features in a task-agnostic way, and provide a well bootstrapped pre-training model for a variety of tasks. TSSN tokenizes TF data into segments, each of which is named as a token and comprised of numerous consecutive points. Masked Token Prediction (MTP), a pretext task, is designed to understand the TF correlations by forecasting tokens that are randomly masked. MTP enables TSSN to capture the high-level intrinsic semantics of TF, and provide general-purpose token embeddings. Therefore, TSSN can be more generalized while keeping high performance. As a result, by replacing the final fully-connected layer with a set of untrained new layers and fine-tuning with small-scale taskspecific data, TSSN can be deployed for a variety of downstream tasks. The simulation results demonstrate that the TSSN can improve overall performance on various downstream tasks when compared to state-of-the-art models.

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“…In this case, although the resource allocation strategy can be solved in a very short time, it is easy to lead to highly unbalanced allocation, and especially after the network reconfiguration, each edge node does not pay enough attention to the quality of service for services. In addition, the existing machine learning-assisted traffic prediction is usually based on a unilateral perspective, either cloud or edge, without considering the complementary perspective of the edge and cloud [22], [23]. Prediction based on unilateral perspective may lead to inappropriate resource allocation decisions.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

et al. 2023

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By integrating communications in different domains, integrated radio and optical networks can serve a wider range of applications and services. Integrated radio and optical network scenarios will involve more weak-computation-ability network nodes, such as small-cell base stations. To pursue efficient integrated radio and optical networks, more efficient ways to conduct transmission under the demand of edge and cloud collaboration are required. The lack of forward-looking resource allocation may easily lead to a waste of network resources without an expected return. Therefore, an efficient resource allocation scheme needs to consider certain issues: 1) a comprehensive perspective of traffic prediction; 2) a release of pressure on the transmission pipeline during the prediction process; and 3) a reduction of loss of edge nodes due to the computation. In this paper, benefiting from machine learning, we propose a resource allocation with edgecloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. We maximize the utilization of limited resources to improve the awareness of network status. We present three evaluation indicators and build a network architecture to evaluate our resource allocation scheme. Through edge-cloud collaboration, our proposal can improve traffic prediction accuracy by 9.5% compared with single-point traffic prediction, and resource utilization is also improved by edge-cloud collaborative traffic prediction.INDEX TERMS Integrated radio and optical networks, resource allocation, edge-cloud collaboration, traffic prediction.

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Online Traffic Flow Prediction for Edge Computing-Enhanced Autonomous and Connected Vehicles

Cited by 32 publications

References 27 publications

A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks

A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks

Masked Token Enabled Pre-Training: A Task-Agnostic Approach for Understanding Complex Traffic Flow

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

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