Spatio-Temporal Traffic Flow Prediction Based on Coordinated Attention

Li, Min; Li, Mengshan; Liu, Bilong; Liu, Jiang; Liu, Zhen; Luo, Dijia

doi:10.3390/su14127394

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…In recent years, with the rapid development of artificial intelligence, deep learning has been widely used in various types of prediction, including traffic flow [23], passenger demand, electricity load [24], air pollution [25], etc., with its high adaptability and excellent performance. Its excellent performance in fields such as image recognition and natural language processing also proves the effectiveness of neural networks in dealing with multivariate, nonlinear, and nonstationary data, and implies the effectiveness in dealing with time series prediction problems as well.…”

Section: Parking Demand Prediction Based On Machine Learningmentioning

confidence: 99%

A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information

Wang

et al. 2023

Sustainability

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With the development of smart cities and smart transportation, cities can gradually provide people with more information to facilitate their life and travel, and parking is also inseparable from both of them. Accurate on-street parking demand prediction can improve parking resource utilization and parking management efficiency, as well as potentially improve urban traffic conditions. Previous parking demand prediction methods seldom consider the correlation between the parking demand of a road section and its surroundings. Therefore, in order to capture the correlation of parking demand in the temporal and spatial dimensions as carefully as possible and enrich the relevant features in the prediction model so as to achieve more accurate prediction results, we designed a parking demand prediction structure that considers different features from two perspectives: overall and internal. We used gated recurrent units (GRU) to extract demand influences in the temporal dimension. The GRU is used in combination with a graph convolutional neural network (GCN) to extract demand influencing factors in the spatial dimension. Additionally, a more detailed representation is designed to express spatial dimensional features. Then, based on the historical parking demand features extracted using encoder–decoder, we fuse the extracted spatio-temporal features with them to finally obtain an on-street parking demand prediction model combining the overall and the internal information. By combining them, we can integrate more correlation factors to achieve a more accurate parking demand prediction. The performance of the model is evaluated by real parking data in Xiufeng District of Guilin. The results show that the proposed model achieves good prediction performance compared with other baselines. In addition, we also design feature ablation experiments. Through the comparison of the results, we find that each feature considered in the proposed model is important in parking demand prediction.

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Section: Parking Demand Prediction Based On Machine Learningmentioning

confidence: 99%

A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information

Wang

et al. 2023

Sustainability

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“…Foreign scholars have also proposed many methods in the field of traffic flow prediction. Cheng Ju [5] et al applied the autoregressive algorithm to traffic flow data, fitting and predicting the data to achieve traffic flow prediction; Huang [6] Y G proposed the DLSTM-AE model based on RAdam optimization to predict traffic flow, optimize the optimal solution of DLSTM-AE, and maximize the model's performance; Li M [7] et al applied the collaborative attention mechanism to spatiotemporal data and proposed a spatiotemporal graph convolutional network (CVSTGCN) model based on coordinated attention, which is used to dynamically capture the long-term correlation between spatiotemporal information of traffic flow simultaneously; Wang [8] et al proposed a hybrid model for predicting ship traffic flow based on wavelet and Prophet models, which can perform wavelet decomposition on data and combine it with Prophet models for prediction; Jianbin [9] et al used local spatiotemporal autoregressive parameter estimation in the field of statistical learning to predict traffic flow data based on urban road network traffic flow data. Domestic scholars have also made significant progress in this area.…”

Section: Introductionmentioning

confidence: 99%

Research on traffic flow prediction method based on deep learning

Zhao

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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With the widespread development of machine learning and deep learning technology in recent years, artificial intelligence technology has been widely applied in various fields. Traffic flow prediction has been a hot research direction in recent years. Traffic flow is an important influencing factor in the field of transportation and travel, playing an important role in the rational allocation of transportation resources and ensuring the smooth performance of transportation and travel. It is necessary to predict future traffic flow based on historical travel data. Some existing deep learning methods require high time complexity and high hardware costs, and that have some shortcomings in prediction ability. This article proposes a CATformer model combining convolutional neural networks and attention mechanisms to solve the traffic flow prediction problem in time series problems, extracting and fusing features from multiple vector spaces for traffic data, The experimental results of predicting the flow of future traffic nodes show that the CATformer model has improved prediction accuracy compared to the benchmark method, achieving the task of traffic node flow prediction based on time series.

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“…As traffic information collection becomes more accessible and computing power increases, many data-driven methods have also been applied to traffic forecasting. These models perform better and are easier to use, such as ensemble learning, neural network methods, and deep learning methods [8,9]. The data-driven methods greatly improve forecast accuracy by fitting a large amount of measured data and can be easily applied to various complex scenarios [10].…”

Section: Introductionmentioning

confidence: 99%

Optimal Aggregate Size of Traffic Sequence Data Based on Fuzzy Entropy and Mutual Information

Guan

2022

Sustainability

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Before traffic forecasting, it is usually necessary to aggregate the information by a certain length of time. An aggregation size that is too short will make the data unstable and cause the forecast result to be too biased. On the other hand, if the aggregation size is too large, the data information will be lost, resulting in the forecast results tending towards an average or slow response. With the development of intelligent transportation systems, especially the development of urban traffic control systems, high requirements are placed on the real-time accuracy of traffic forecasting. Therefore, it is an essential topic of traffic forecasting research to determine aggregation sizes. In this paper, the mutual information between the forecast input information and the forecast result and the sequence complexity of the forecast result measured by approximate entropy, sample entropy, and fuzzy entropy are considered; then, the optimal data aggregation size is given. To verify the proposed method, the validated data obtained from the simulation is aggregated and calculated with different aggregation sizes, then used for forecasting. By comparing the prediction performance of different aggregate sizes, the optimal aggregate size was found to reduce MSE by 14–30%. The results show that the method proposed in this paper is helpful for selecting the optimal data aggregation size in forecasting and can improve the performance of prediction.

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Spatio-Temporal Traffic Flow Prediction Based on Coordinated Attention

Cited by 6 publications

References 47 publications

A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information

A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information

Research on traffic flow prediction method based on deep learning

Optimal Aggregate Size of Traffic Sequence Data Based on Fuzzy Entropy and Mutual Information

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