GSTA: gated spatial–temporal attention approach for travel time prediction

Khaled, Alkilane; Elsir, Alfateh M. Tag; Shen, Yanming

doi:10.1007/s00521-021-06560-z

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…To demonstrate the application potential of the CQ-LSTM method proposed in this paper, the CQ-LSTM model is compared with the gated spatiotemporal attention model (GSTA) proposed by Khaled and Alfateh [27]. Te GSTA method predicts based on the temporal correlation and spatial correlation of travel time and is able to predict the average travel time of the target road segment over a future period.…”

Section: Methodsmentioning

confidence: 99%

Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression

Li,

Wang,

et al. 2023

Journal of Advanced Transportation

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Travel time reliability assessment has been widely used in recent years to evaluate the performance of transportation networks and measure the operation level of transportation systems. Weather, as one of the important factors influencing travel time reliability, affects the relationship between the supply and requirement of urban road networks. Considering the traffic characteristics under different traffic conditions, a study on the influence of weather on travel time reliability under different conditions is proposed to predict the probability of travelers completing their trips within the expected time under different weather conditions. Based on the urban road network data and cab trajectory data of Harbin city, this paper correlates the floating vehicle location with the road network information through a hidden Markov model to reduce the influence of vehicle trajectory errors on the calculation results of path travel time. To analyze the entire distribution of extreme travel time and its impact on the reliability of travel time under various traffic situations, it captures the tail features of the travel time distribution based on extreme value theory. Then, to increase the predictability of each quantile, it combines a deep-learning LSTM model and a quantile regression model to create a probabilistic travel time prediction model utilizing combined layers. The proposed model is compared with the linear quantile regression and neural network quantile regression models, and the model is evaluated in terms of point prediction results and probabilistic prediction results, respectively, to ensure the accuracy of predictions from the model. As a result, the prediction accuracy of the model in this paper is greatly improved, and the degree of violating quantile constraints is greatly reduced.

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Section: Methodsmentioning

confidence: 99%

Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression

Li,

Wang,

et al. 2023

Journal of Advanced Transportation

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“…To achieve accurate TTP, a multi-layer GCN module paralleled with a transformer layer [34] was devised to capture both spatial and temporal features [16] and then integrated with another transformer layer to obtain TTP. Khaled et al [17] exploited the gated attention mechanism to fetch the spatial-temporal features and a feature selection module to obtain precise TTP. Though the aforementioned research work may not directly relate to TTP, the relevant results still highly correspond with the spatial-temporal feature extraction in traic prediction, which can be helpful in solving the TTP problem.…”

Section: Deep Learning For Travel Time Predictionmentioning

confidence: 99%

Travel Time Prediction Method Based on Spatial-Feature-based Hierarchical Clustering and Deep Multi-input Gated Recurrent Unit

Fang

Liu

Chen

et al. 2022

ACM Trans. Sen. Netw.

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Accurate travel time prediction (TTP) is a significant aspect in the intelligent transportation system (ITS). Travel times of certain road segments explicitly reflect the traffic conditions of those sections. Effective TTP of road segments is instrumental in route planning, traffic control, and traffic management. However, the accuracy of TTP is greatly affected by the intricate topological structure of traffic network and the dynamics of traffic flow over time. This paper develops a TTP method based on the spatial-feature-based hierarchical clustering (SFHC) and deep multi-input gated recurrent unit (DMGRU). The proposed two-stage method is capable of capturing the spatial-temporal features of traffic network. Specifically, the SFHC divides the road segments into several clusters having similar traffic features, and then the clustered data is fed into the DMGRU for TTP. Our experiments conducted on the practical dataset demonstrate that the designed prediction method can achieve the mean absolute percentage error (MAPE) of 3.3109% and mean absolute error (MAE) of 2.5658, which outperform various combinations of baseline clustering algorithms and prediction models.

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HLGST: Hybrid local–global spatio-temporal model for travel time estimation using Siamese graph convolutional with triplet networks

Elsir

Khaled

Shen

2023

Expert Systems with Applications

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GSTA: gated spatial–temporal attention approach for travel time prediction

Cited by 9 publications

References 22 publications

Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression

Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression

Travel Time Prediction Method Based on Spatial-Feature-based Hierarchical Clustering and Deep Multi-input Gated Recurrent Unit

HLGST: Hybrid local–global spatio-temporal model for travel time estimation using Siamese graph convolutional with triplet networks

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