Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
- URL: http://arxiv.org/abs/2407.08047v2
- Date: Mon, 15 Jul 2024 02:31:15 GMT
- Title: Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
- Authors: Jianzhe Xue, Dongcheng Yuan, Yu Sun, Tianqi Zhang, Wenchao Xu, Haibo Zhou, Xuemin, Shen,
- Abstract summary: We introduce a novel framework that utilizes sparse IoV data to achieve cost-effective traffic state estimation (TSE)
Particularly, we propose a novel spatial-temporal attention model called the convolutional retentive network (CRNet) to improve the TSE accuracy.
The model employs the convolutional neural network (CNN) for spatial correlation aggregation and the retentive network (RetNet) based on the attention mechanism to extract temporal correlations.
- Score: 23.524936542317842
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The growing number of connected vehicles offers an opportunity to leverage internet of vehicles (IoV) data for traffic state estimation (TSE) which plays a crucial role in intelligent transportation systems (ITS). By utilizing only a portion of IoV data instead of the entire dataset, the significant overheads associated with collecting and processing large amounts of data can be avoided. In this paper, we introduce a novel framework that utilizes sparse IoV data to achieve cost-effective TSE. Particularly, we propose a novel spatial-temporal attention model called the convolutional retentive network (CRNet) to improve the TSE accuracy by mining spatial-temporal traffic state correlations. The model employs the convolutional neural network (CNN) for spatial correlation aggregation and the retentive network (RetNet) based on the attention mechanism to extract temporal correlations. Extensive simulations on a real-world IoV dataset validate the advantage of the proposed TSE approach in achieving accurate TSE using sparse IoV data, demonstrating its cost effectiveness and practicality for real-world applications.
Related papers
- Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehicles [48.32593099620544]
Traffic flow estimation (TFE) is crucial for intelligent transportation systems.
This paper introduces a novel and cost-effective TFE framework that leverages sparse,temporal generative artificial intelligence (GAI) framework.
Within this framework, the conditional encoder mines spatial-temporal correlations in the initial TFE results.
arXiv Detail & Related papers (2024-07-10T20:26:04Z) - RACH Traffic Prediction in Massive Machine Type Communications [5.416701003120508]
This paper presents a machine learning-based framework tailored for forecasting bursty traffic in ALOHA networks.
We develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network.
We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics.
arXiv Detail & Related papers (2024-05-08T17:28:07Z) - Sparse Federated Training of Object Detection in the Internet of
Vehicles [13.864554148921826]
Object detection is one of the key technologies in the Internet of Vehicles (IoV)
Current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server.
We propose a federated learning-based framework, where well-trained local models are shared in the central server.
arXiv Detail & Related papers (2023-09-07T08:58:41Z) - Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual
Antenna Arrays [55.736718475856726]
Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT)
Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points.
We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
arXiv Detail & Related papers (2023-08-03T02:49:50Z) - Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting.
AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations.
Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
arXiv Detail & Related papers (2023-06-15T14:50:27Z) - Efficient Federated Learning with Spike Neural Networks for Traffic Sign
Recognition [70.306089187104]
We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training.
Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
arXiv Detail & Related papers (2022-05-28T03:11:48Z) - Learning spatiotemporal features from incomplete data for traffic flow
prediction using hybrid deep neural networks [0.28675177318965034]
This study focuses on hybrid deep neural networks to predict traffic flow in the California Freeway Performance Measurement System (PeMS) with missing values.
Various architecture configurations with series and parallel connections are considered based on RNNs and CNNs.
A comprehensive analysis performed on two different datasets from PeMS indicates that the proposed series-parallel hybrid network with the mean imputation technique achieves the lowest error in predicting the traffic flow.
arXiv Detail & Related papers (2022-04-21T15:57:08Z) - Space Meets Time: Local Spacetime Neural Network For Traffic Flow
Forecasting [11.495992519252585]
We argue that such correlations are universal and play a pivotal role in traffic flow.
We propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor.
The proposed STNN model can be applied on any unseen traffic networks.
arXiv Detail & Related papers (2021-09-11T09:04:35Z) - Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge
Computing: A Contextual-Bandit Approach [81.5261621619557]
We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC)
We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer.
Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
arXiv Detail & Related papers (2021-08-09T08:45:47Z) - Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph
Convolutional Networks [110.80088437391379]
A graph-based framework called SMART is proposed to model and keep track of the statistics of vehicle-to-temporal (V2I) communication latency across a large geographical area.
We develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm.
Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and the latency performance of large vehicular networks.
arXiv Detail & Related papers (2021-03-13T06:56:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.