Integrated Velocity Prediction Method and Application in Vehicle-Environment Cooperative Control Based on Internet of Vehicles

Zhang, Hongjie; Chen, Zheng; Li, Guang; Liu, Yonggang; Huang, Yanjun; Cunningham, Geoffrey; Early, Juliana

doi:10.1109/tvt.2021.3139367

Cited by 9 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal is to use as small w and h as possible to obtain a better prediction effect. This paper sets h ∈ [2,3,4,5,6] and w ∈ [1,2,3]. The result for p ∈ [1,2,3,4,5] is shown in Fig.…”

Section: B Parameter 1) Model Parametermentioning

confidence: 99%

“…CNN: It refers to a CNN-based flow prediction model. This paper uses a single-layer CNN, the size of the convolution kernel is [3,1], the stride of the convolution kernel is [1,1], with no padding, and the number of channels is 16. Finally, a multi-task linear regression model is trained on the top layer.…”

Section: Comparison With Baselinesmentioning

confidence: 99%

“…For example, drivers can avoid slow road and choose better driving routes based on predicted road traffic speeds. Onboard computers can adjust energy management strategies based on predicted road traffic speeds to save energy and reduce emissions.Traffic managers can manage road networks and allocate resources systematically based on predicted road traffic speeds [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

“…Since the road traffic speed is closely related to the driving state of the vehicles on the road, road traffic speed is mainly affected by two aspects. (1) The impact of historical road traffic speeds. Although the road traffic speed changes all the time, it is impossible to completely separate from the historical road speed.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

Fu¹,

Liang²,

Hou³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Traffic speed prediction is the key to many valuable applications, and it is also a challenging task because of its various influencing factors. Recent work attempts to obtain more information through various hybrid models, thereby improving the prediction accuracy. However, the spatial information acquisition schemes of these methods have two-level differentiation problems. Either the modeling is simple but contains little spatial information, or the modeling is complete but lacks flexibility. In order to introduce more spatial information on the basis of ensuring flexibility, this paper proposes IRNet (Transferable Intersection Reconstruction Network). First, this paper reconstructs the intersection into a virtual intersection with the same structure, which simplifies the topology of the road network. Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models. Third, a self-attention mechanism is used to fuse spatiotemporal features for prediction. In the comparison experiment with the baseline, not only the prediction effect, but also the transfer performance has obvious advantages.

show abstract

Section: B Parameter 1) Model Parametermentioning

confidence: 99%

Section: Comparison With Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

Fu¹,

Liang²,

Hou³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Intelligent Transportation System (ITS) [1] was developed to bolster the efficiency, sustainability, and safety of transport networks and has become a focal point of research. Traffic prediction, essential for intelligent transportation development [2], finds applications in vehicle-environment cooperation [3], passenger demand forecasting [4,5], travel time estimation [6], order scheduling [7], and congestion management [8]. However, with the influx of traffic data from sensors such as loop detectors, cameras, and weather sensors, the efficient extraction of spatial and temporal features becomes challenging.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Chu,

Hou,

Jiang

et al. 2024

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

Traffic condition prediction is crucial for executing traffic control and scheduling tasks within intelligent transportation systems. With diversified data sources, effectively modeling the complex spatial-temporal dependencies in the whole traffic network and predicting nonlinear general traffic condition changes become primary challenges for intelligent transportation systems. In this paper, a double-layer spatial-temporal feature extraction and evaluation (DL-STFEE) model is proposed, aimed at accurately predicting the traffic condition transferring of the whole traffic network. Firstly, a public traffic dataset is processed to extract the spatial and temporal features of vehicles, as well as the clusters of traffic conditions of the entire traffic network. Secondly, a double-layer deep learning traffic condition predictor is proposed. The model incorporates a spatial-temporal feature extraction layer, which leverages the graph convolution network (GCN) and attention mechanism to obtain spatiotemporal features across the whole traffic network. Additionally, a spatial-temporal combination layer employs a high-dimensional self-attention mechanism to integrate features across spatial-temporal combinations, bolstering prediction accuracy. Finally, through rigorous experiments, the contributions of neural network structures on the spatial-temporal feature extraction are comprehensively analyzed and the experiments also validate the effectiveness of traffic condition prediction by the proposed DL-STFEE model.

show abstract

Multi-objective hierarchical energy management for connected plug-in hybrid electric vehicle with cyber–physical interaction

Cui,

et al. 2024

Applied Energy

View full text Add to dashboard Cite

Integrated Velocity Prediction Method and Application in Vehicle-Environment Cooperative Control Based on Internet of Vehicles

Cited by 9 publications

References 40 publications

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Multi-objective hierarchical energy management for connected plug-in hybrid electric vehicle with cyber–physical interaction

Contact Info

Product

Resources

About