An Empirical Experiment on Deep Learning Models for Predicting Traffic Data

Lee, Hyunwook; Park, Cheonbok; Jin, Seungmin; Chu, Hyeshin; Choo, Jaegul; Ko, Sungahn

doi:10.1109/icde51399.2021.00160

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…We adopted three widely used evaluation metrics, namely MAE, root mean square error (RMSE), and mean absolute percentage error (MAPE), to evaluate the performance of our model. We followed prior research (6)(7)(8)(9)(10)(11)(12)(13) and excluded missing values when calculating the evaluation metrics. Specifically, these evaluation metrics are as follows:…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The key component of a GNN is the adjacency matrix, which describes the graph structure and preserves the spatial correlations between nodes. For traffic speed forecasting, most works ( 6 – 13 ) usually construct the adjacency matrices based on the Euclidean distance between sensors in the traffic network. But Chen et al argued that the graph structure constructed in this way neglects the complexity and interaction of edges ( 9 ).…”

Section: Related Workmentioning

confidence: 99%

“…GNNs can be utilized to capture the spatial correlations on graph structures and, thus, are widely applied to traffic speed forecasting (4,5). Existing methods for traffic speed forecasting (6)(7)(8)(9)(10)(11)(12)(13) employ spatiotemporal GNNs to capture spatial and temporal correlations, improving the performance of traffic speed forecasting. They mostly predefine a fixed adjacency matrix (representing the graph structure of a traffic network) to capture the spatial correlation between sensors.…”

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confidence: 99%

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Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

Liu

Mao

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Traffic speed forecasting plays an important role in intelligent traffic monitoring systems. Existing methods mostly predefine a fixed adjacency matrix to capture the spatial correlation between sensors in a traffic network. However, there are multiple hidden spatial correlations between sensors. A single fixed adjacency matrix cannot adaptively capture multiple spatial correlations. To overcome this limitation, we proposed a novel multiadaptive spatiotemporal flow graph neural network (MAF-GNN) for traffic speed forecasting. Specifically, MAF-GNN mainly consists of a multiadaptive adjacency matrix mechanism and a spatiotemporal flow mechanism. The multiadaptive adjacency matrix mechanism was proposed to adaptively capture multiple hidden spatial correlations between sensors. The spatiotemporal flow mechanism was proposed to further enhance the capture of temporal and spatial correlations. The experimental results on two real-world traffic datasets, METR-LA and PeMS-Bay, demonstrated the superiority of MAF-GNN. MAF-GNN outperformed baseline models in 1-h ahead forecasting.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

Liu

Mao

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…Intelligent transportation systems (ITSs) [1][2][3][4][5], as an integral part of smart city development, play a crucial role in managing, analysing, optimising, and improving urban traffic conditions. Among the core technologies of ITSs, traffic flow prediction [6][7][8][9] aims to forecast the future traffic conditions for a certain period of time based on the historical traffic data collected from sensors in the road traffic network [10][11][12][13][14][15]. This prediction capability is essential for proactive traffic management, real-time route planning, congestion mitigation, and the overall improvement of urban mobility.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Spatial–Temporal Self-Attention Network for Traffic Flow Prediction

Wang,

Yang,

Zhou

2024

Future Internet

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Traffic flow prediction is considered to be one of the fundamental technologies in intelligent transportation systems (ITSs) with a tremendous application prospect. Unlike traditional time series analysis tasks, the key challenge in traffic flow prediction lies in effectively modelling the highly complex and dynamic spatiotemporal dependencies within the traffic data. In recent years, researchers have proposed various methods to enhance the accuracy of traffic flow prediction, but certain issues still persist. For instance, some methods rely on specific static assumptions, failing to adequately simulate the dynamic changes in the data, thus limiting their modelling capacity. On the other hand, some approaches inadequately capture the spatiotemporal dependencies, resulting in the omission of crucial information and leading to unsatisfactory prediction outcomes. To address these challenges, this paper proposes a model called the Dynamic Spatial–Temporal Self-Attention Network (DSTSAN). Firstly, this research enhances the interaction between different dimension features in the traffic data through a feature augmentation module, thereby improving the model’s representational capacity. Subsequently, the current investigation introduces two masking matrices: one captures local spatial dependencies and the other captures global spatial dependencies, based on the spatial self-attention module. Finally, the methodology employs a temporal self-attention module to capture and integrate the dynamic temporal dependencies of traffic data. We designed experiments using historical data from the previous hour to predict traffic flow conditions in the hour ahead, and the experiments were extensively compared to the DSTSAN model, with 11 baseline methods using four real-world datasets. The results demonstrate the effectiveness and superiority of the proposed approach.

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“…Time series prediction, a cornerstone of time series analysis, entails forecasting future values using historical sequential data patterns and trends. In the specific context of traffic prediction (James 2022), which includes forecasting traffic flow, speed, and demand, its applications span route planning, vehicle scheduling, and congestion management (Lee et al 2021;Fang et al 2021;Li et al 2023a).…”

Section: Introductionmentioning

confidence: 99%

ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting

Sun,

Liu,

et al. 2024

AAAI

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Traffic prediction is the core issue of Intelligent Transportation Systems. Recently, researchers have tended to use complex structures, such as transformer-based structures, for tasks such as traffic prediction. Notably, traffic data is simpler to process compared to text and images, which raises questions about the necessity of these structures. Additionally, when handling traffic data, researchers tend to manually design the model structure based on the data features, which makes the structure of traffic prediction redundant and the model generalizability limited. To address the above, we introduce the ‘ModWaveMLP’—A multilayer perceptron (MLP) based model designed according to mode decomposition and wavelet noise reduction information learning concepts. The model is based on simple MLP structure, which achieves the separation and prediction of different traffic modes and does not depend on additional features introduced such as the topology of the traffic network. By performing experiments on real-world datasets METR-LA and PEMS-BAY, our model achieves SOTA, outperforms GNN and transformer-based models, and outperforms those that introduce additional feature data with better generalizability, and we further demonstrate the effectiveness of the various parts of the model through ablation experiments. This offers new insights to subsequent researchers involved in traffic model design. The code is available at: https://github.com/Kqingzheng/ModWaveMLP.

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An Empirical Experiment on Deep Learning Models for Predicting Traffic Data

Cited by 9 publications

References 20 publications

Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

Dynamic Spatial–Temporal Self-Attention Network for Traffic Flow Prediction

ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting

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