Fuzzy hybrid framework with dynamic weights for short‐term traffic flow prediction by mining spatio‐temporal correlations

Ma, Dongfang; Sheng, Bowen; Ma, Xiaolong; Jin, Sheng

doi:10.1049/iet-its.2019.0287

Cited by 12 publications

(2 citation statements)

References 49 publications

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“…The spatial features were extracted with CNN and the role of each neural network was analysed. Ma et al [26] selected spatial neighbours by the distance between the detectors, and use the speed series of the selected neighbours to form a picture. Although CNN has realised spatial modelling, it can only be used for data of Euclidean structure.…”

Section: Related Workmentioning

confidence: 99%

Multi‐step traffic speed prediction model with auxiliary features on urban road networks and its understanding

Guo

Song

Zhang

et al. 2020

IET intell. transp. syst

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Multi-step prediction of long-term traffic speed is an important part of the intelligent transportation system. Traffic speed is affected by temporal features, spatial features, and various environmental features. The prediction of traffic speed considering the above features is a big challenge. This study proposed a multi-step prediction model named embedding graph convolutional long short-term memory network (EGC-LSTM) for urban road network traffic speed prediction which can deal with spatial-temporal correlation and auxiliary features at the same time. Firstly, a graph convolutional network (GCN) for capturing directed graph properties is proposed. Based on the GCN, the LSTM and sequence to sequence model are further applied to realise multi-step prediction considering the spatial-temporal correlation of the traffic network. To improve the performance of the model and obtain the importance of each step in the historical data, the attention mechanism is introduced. Then, one-hot encoding is applied to the category-type auxiliary features. Considering that the dimension becomes larger after the features are one-hot encoded, the dimensions are reduced using embedding. The experiment results prove that the proposed model's performance is better than other models, and the model is interpreted in detail.

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Section: Related Workmentioning

confidence: 99%

Multi‐step traffic speed prediction model with auxiliary features on urban road networks and its understanding

Guo

Song

Zhang

et al. 2020

IET intell. transp. syst

View full text Add to dashboard Cite

show abstract

“…Supervisory learning was used to mine the relationship between the factors of historical data and current traffic flow to train the predictor in advance so as to reduce the predicting time [49]. In addition, the match-then-predict method [50] and the fuzzy hybrid framework [51] with dynamic weights by mining spatial-temporal correlations were both proposed. Attention mechanisms were also combined in LSTM to increase the accuracy of prediction [52].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic Prediction considering Spatial-Temporal Characteristics of Freeway Flow

Wang

Yang

et al. 2021

Journal of Advanced Transportation

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This paper presents a short-term traffic prediction method, which takes the historical data of upstream points and prediction point itself and their spatial-temporal characteristics into consideration. First, the Gaussian mixture model (GMM) based on Kullback–Leibler divergence and Grey relation analysis coefficient calculated by the data in the corresponding period is proposed. It can select upstream points that have a great impact on prediction point to reduce computation and increase accuracy in the next prediction work. Second, the hybrid model constructed by long short-term memory and K-nearest neighbor (LSTM-KNN) algorithm using transformed grey wolf optimization is discussed. Parallel computing is used in this part to reduce complexity. Third, some meaningful experiments are carried out using real data with different upstream points, time steps, and prediction model structures. The results show that GMM can improve the accuracy of the multifactor models, such as the support vector machines, the KNN, and the multi-LSTM. Compared with other conventional models, the TGWO-LSTM-KNN prediction model has better accuracy and stability. Since the proposed method is able to export the prediction dataset of upstream and prediction points simultaneously, it can be applied to collaborative management and also has good potential prospects for application in freeway networks.

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Explainability techniques applied to road traffic forecasting using Graph Neural Network models

García-Sigüenza,

Llorens-Largo,

Tortosa

et al. 2023

Information Sciences

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Fuzzy hybrid framework with dynamic weights for short‐term traffic flow prediction by mining spatio‐temporal correlations

Cited by 12 publications

References 49 publications

Multi‐step traffic speed prediction model with auxiliary features on urban road networks and its understanding

Multi‐step traffic speed prediction model with auxiliary features on urban road networks and its understanding

Short-Term Traffic Prediction considering Spatial-Temporal Characteristics of Freeway Flow

Explainability techniques applied to road traffic forecasting using Graph Neural Network models

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