Spatial–temporal multi-feature fusion network for long short-term traffic prediction

Wang, Yan; Ren, Qianqian; Li, Jinbao

doi:10.1016/j.eswa.2023.119959

Cited by 17 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, we will capture the inter-dependencies between the subject and object pairs. Let and respectively denote the -th token and -th token in the sentence, and they form potential subject and object pair, cosine similarity between two entities is used as aggregated weights 29 , where is the weight matrix. Next, we determine the relation by comparing it with a threshold .…”

Section: Methodsmentioning

confidence: 99%

A hybrid attention and dilated convolution framework for entity and relation extraction and mining

Shan,

Lu,

Lou

2023

Sci Rep

View full text Add to dashboard Cite

Mining entity and relation from unstructured text is important for knowledge graph construction and expansion. Recent approaches have achieved promising performance while still suffering from inherent limitations, such as the computation efficiency and redundancy of relation prediction. In this paper, we propose a novel hybrid attention and dilated convolution network (HADNet), an end-to-end solution for entity and relation extraction and mining. HADNet designs a novel encoder architecture integrated with an attention mechanism, dilated convolutions, and gated unit to further improve computation efficiency, which achieves an effective global receptive field while considering local context. For the decoder, we decompose the task into three phases, relation prediction, entity recognition and relation determination. We evaluate our proposed model using two public real-world datasets that the experimental results demonstrate the effectiveness of the proposed model.

show abstract

Section: Methodsmentioning

confidence: 99%

A hybrid attention and dilated convolution framework for entity and relation extraction and mining

Shan,

Lu,

Lou

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Zhang et al [48] used one-hot coding and combined temporal information with weather for prediction. Wang et al [49] constructed a graph attention network that effectively extracted weather-driven spatiotemporal features by convolving weather with spatio-temporal feature modules. However, exploring the dynamic spatio-temporal dependency between traffic and weather, the model of utilizing this complex dependency in traffic prediction remains unresolved effectively.…”

Section: Spatio-temporal Traffic Prediction With Embedded Factorsmentioning

confidence: 99%

Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction

Zhong,

Wang,

Chen

et al. 2024

Buildings

View full text Add to dashboard Cite

As the cornerstone of intelligent transportation systems, accurate traffic prediction can reduce the pressure of urban traffic, reduce the cost of residents’ travel time, and provide a reference basis for urban construction planning. Existing traffic prediction methods focus on spatio-temporal dependence modeling, ignoring the influence of weather factors on spatio-temporal characteristics, and the prediction task has complexity and an uneven distribution in different spatio-temporal scenarios and weather changes. In view of this, we propose a weather interaction-aware spatio-temporal attention network (WST-ANet), in which we integrate feature models and dynamic graph modules in the encoder and decoder, and use a spatio-temporal weather interaction perception module for prediction. Firstly, the contextual semantics of the traffic flows are fused using a feature embedding module to improve the adaptability to weather drivers; then, an encoder–decoder is constructed by combining the Dynamic Graph Module and the WSTA Block, to extract spatio-temporal aggregated correlations in the roadway network; finally, the feature information of the encoder was weighted and aggregated using the cross-focusing mechanism, and attention was paid to the hidden state of the encoding. Traffic flow was predicted using the PeMS04 and PeMS08 datasets and compared with multiple typical baseline models. It was learned through extensive experiments that the accuracy evaluation result is the smallest in WST-ANet, which demonstrated the superiority of the proposed model. This can more accurately predict future changes in traffic in different weather conditions, providing decision makers with a basis for optimizing scenarios.

show abstract

Traffic flow prediction: A 3D adaptive multi‐module joint modeling approach integrating spatial‐temporal patterns to capture global features

Ul Abideen,

Sun,

Sun

2024

Journal of Forecasting

View full text Add to dashboard Cite

The challenges in citywide traffic flow are intricate, encompassing various factors like temporal and spatial dependencies, holidays, and weather. Despite the complexity, there are still research gaps in effectively incorporating these spatio‐temporal relations through deep learning. Addressing these gaps is crucial for tackling issues such as traffic congestion, public safety, and efficient traffic management within cities. This paper underscores notable research gaps, including the development of models capable of handling both local and global traffic flow patterns, integrating multi‐modal data sources, and effectively managing spatio‐temporal dependencies. In this paper, we proposed a novel model named 3D spatial–temporal‐based adaptive modeling graph convolutional network (3D(STAMGCN)) that addresses for traffic flow data in better periodicity modeling. In contrast to earlier studies, 3D(STAMGCN) approaches the task of traffic flow prediction as a periodic residual learning problem. This is achieved by capturing the input variation between historical time segments and the anticipated output for future time segments. Forecasting traffic flow, as opposed to a direct approach, is significantly simpler when focusing on learning more stationary deviations. This, in turn, aids in the training of the model. Nevertheless, the networks enable residual generation at each time interval through learned variations between future conditions and their corresponding weekly observations. Consequently, this significantly contributes to achieving more accurate forecasts for multiple steps ahead. We executed extensive experiments on two real‐world datasets and compared the performance of our model to state‐of‐the‐art (SOTA) techniques.

show abstract

Spatial–temporal multi-feature fusion network for long short-term traffic prediction

Cited by 17 publications

References 27 publications

A hybrid attention and dilated convolution framework for entity and relation extraction and mining

A hybrid attention and dilated convolution framework for entity and relation extraction and mining

Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction

Traffic flow prediction: A 3D adaptive multi‐module joint modeling approach integrating spatial‐temporal patterns to capture global features

Contact Info

Product

Resources

About