Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks

Zhou, Jianshan; Qu, Guixian; Tian, Daxin; Sheng, Zhengguo; Duan, Xuting; Guan, Yong Liang; Leung, Victor C. M.

doi:10.1109/tmc.2023.3300311

IEEE Trans. on Mobile Comput.

2024

DOI: 10.1109/tmc.2023.3300311

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Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks

Jianshan Zhou,

Guixian Qu,

Daxin Tian

et al.

Abstract: Joint energy-efficiency communication optimization and perimeter traffic flow control for multi-region LTE-V2V networks (Version 1).

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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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

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Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks

Abstract: Joint energy-efficiency communication optimization and perimeter traffic flow control for multi-region LTE-V2V networks (Version 1).

Cited by 1 publication

References 57 publications

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

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

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