Lane-Level Heterogeneous Traffic Flow Prediction: A Spatiotemporal Attention-Based Encoder–Decoder Model

Zheng, Yufeng; Li, Wenquan; Wen, Zheng; Dong, Chunjiao; Wang, Shengyou; Chen, Qian

doi:10.1109/mits.2022.3224218

Cited by 7 publications

(6 citation statements)

References 34 publications

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“…Zheng et al [38] proposed an STA-ED framework based on the scenario of predicting the flow of different vehicle models on the traffic network. This method sequentially inputs traffic data into the Spatial Attention Layer, LSTM Encoder, Temporary Attention Layer, LSTM Decoder, and finally obtains traffic prediction values.…”

Section: Preliminaries a Related Workmentioning

confidence: 99%

“…Main highlights Zhao et al [33] Exploring the performance of traffic prediction from both temporal and spatial dimensions Wang et al [34] Using spatial layer to extract spatial relationships between traffic networks Wang et al [35] Incorporating the Adjacent Similar algorithm to predict traffic flow at intersections without historical data Lai et al [36] Introducing the NodeRank algorithm to calculate the road importance based on spatiotemporal features Qi et al [37] Designing a cloud model to aggregate the global parameters of each submodel Zheng et al [38] Sequentially inputting traffic data into several neural computing structures to obtain prediction results…”

Section: Referencesmentioning

confidence: 99%

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A Graph Deep Learning-Based Fast Traffic Flow Prediction Method in Urban Road Networks

Yang,

2023

IEEE Access

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In modern smart cities, road networks are becoming more and more complicated, resulting in more complex format of graphs. This brings many challenges to the forecasting of traffic flow in road graphs. Most of traditional traffic flow forecasting methods ignored many implicit relationships inside road graphs. And this cannot be well suitable for modern road networks in smart cities. Besides, the operation of smart cities is accompanied with real-time big data stream. The running efficiency of forecasting methods is another important concern. To handle this issue, this paper proposes a graph deep learning-based fast traffic flow forecasting method in urban road networks. Firstly, the theory about graph convolution operations is deduced and can be used as the basis of a graph convolution network (GCN). Then, the whole road network is viewed as a complex road graph, and the GCN is introduced to establish a novel forecasting method for graph-level traffic flow. With roads being regarded as nodes and their relations being regarded as edges, graph-level forecasting can be realized with the use of the proposed method. Experiments are carried out on a standard real dataset to evaluate the proposal. The experimental results show a proper performance of the proposal.

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Section: Preliminaries a Related Workmentioning

confidence: 99%

Section: Referencesmentioning

confidence: 99%

A Graph Deep Learning-Based Fast Traffic Flow Prediction Method in Urban Road Networks

Yang,

2023

IEEE Access

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“…Road traffic flow exhibits time-space characteristics [7][8][9]. To enhance road traffic efficiency, scholars have conducted extensive research on traffic flow characteristics and developed numerous models, including the cellular automata model [10,11] and the viscoelastic model [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…During the traffic planning stage, the topological structure of the road network is determined by planners or designers based on design specifications, and adopting different saturations as the design basis for different classes of roads is the requirement of these specifications. In Equation (7), once the saturation of a segment is given, reasonable integration can be decided using Equation (8).…”

Section: Decision Of Threshold Of Integrationmentioning

confidence: 99%

“…x = (y − 0.67113)/0.14713 (8) For example, if the saturation of one segment is designated as 0.85, then the reasonable integration threshold is 1.22. After comparing the actual integration of the planned road with the integration threshold, if the actual integration is less than the threshold, there is room for increased road network density.…”

Section: Decision Of Threshold Of Integrationmentioning

confidence: 99%

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Modelling the Coupling Relationship between Urban Road Spatial Structure and Traffic Flow

et al. 2023

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In order to promote the sustainable development of urban traffic systems, improve the accuracy of traffic system analysis in the urban planning stage and reduce the possibility of traffic congestion in the operation stage of road networks, the coupling relationship and evolution mechanism between urban road spatial structure and traffic flow were studied, and a model of the relationship between the metrics was established in this study based on real road network and traffic flow data. First, the road spatial structure model of the study area was established from the perspective of road space, and the spatial syntax method was applied to verify the rationality of the spatial structure of the road network. Secondly, the initial OD matrix was determined by OD backpropagation based on the measured traffic flow data. Thirdly, the coupling rule between the spatial structure and the traffic flow of the road network was explored by loading the increment in the OD matrix to the initial OD matrix step by step based on a simulation experiment. Finally, the relationship between the degree of integration of the spatial syntactic feature parameter and the saturation of the traffic flow feature parameter was modelled on the basis of experimental results and verified by an example. This research shows that the spatial structure of urban roads has a significant impact on the characterisation of the traffic flow distribution of road networks, and a strong correlation can be found between the integration degree and saturation degree. An optimal fit, which can be used as a reference for the design of road spatial structure, was explored in this research.

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A dynamic multigraph and multidimensional attention neural network model for metro passenger flow prediction

Xi,

Yan,

Jia

et al. 2024

Concurrency and Computation

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SummaryPassenger flow prediction is an important part of daily metro operation, and its accuracy affects the deployment of train resources management. Due to the complex spatiotemporal correlation characteristics of metro passenger flow, it is necessary to describe it to improve the accuracy of passenger flow prediction. However, the existing models mainly construct the weight matrix based on the static graph and the similarity between stations when describing the spatial correlation of station passenger flow but ignore the time‐varying characteristics of the spatial correlation of station passenger flow. To address this problem, this study introduces a dynamic multi‐graph and multidimensional attention spatiotemporal model. Specifically, the Graph Convolutional Neural Network combined with dynamic multigraph extracts spatial features and the Gated Recurrent Unit extracts temporal features of passenger flow. The multidimensional attention can obtain the spatiotemporal correlation of passenger flow data by assigning weights to them. Finally, this model has been used to conduct experiments on Beijing metro passenger flow datasets with time granularity of 10 and 15 min. The result indicates that the DGMANN model outperforms state‐of‐the‐art other deep learning methods in passenger flow prediction. In addition, the effectiveness of its key submodules has been verified through ablation experiments.

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Lane-Level Heterogeneous Traffic Flow Prediction: A Spatiotemporal Attention-Based Encoder–Decoder Model

Cited by 7 publications

References 34 publications

A Graph Deep Learning-Based Fast Traffic Flow Prediction Method in Urban Road Networks

A Graph Deep Learning-Based Fast Traffic Flow Prediction Method in Urban Road Networks

Modelling the Coupling Relationship between Urban Road Spatial Structure and Traffic Flow

A dynamic multigraph and multidimensional attention neural network model for metro passenger flow prediction

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