Traffic Density Based Travel-Time Prediction With GCN-LSTM

Katayama, Hiroki; Yasuda, Shohei; Fuse, Takashi

doi:10.1109/itsc55140.2022.9922259

Cited by 3 publications

(2 citation statements)

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“…The graph convolution network algorithm learns network's topology and the LSTM correlates the data features in time. The authors in [84] applied spatial densities to the LSTM and evaluated the missing values of the densities using interpolation. They concluded that the spatial densities are better than the speed…”

Section: E Traffic Prediction Using Graph Convolutional Neural Networ...mentioning

confidence: 99%

“…Computationally complex steps involved in the forecast output [83] Designs an algorithm that uses principal component analysis for reducing data dimension, graph convolutional network for learning network's topology and the LSTM obtaining features in time Improved accuracy due to combination of various sub-algorithms for varied tasks in prediction Inefficient for real-time traffic analysis due to involvement of multiple algorithms [84] Applies interpolation with spatial densities as input to the GCN-LSTM to predict traffic congestion and travel time Effective for congested traffic networks Requires use of high storage for data processing of congested traffic networks [85] Obtains spatial traffic data by GCN and temporal dependencies by Bi-LSTM with attention mechanism…”

Section: Accurate In Dealing With Data Prediction Involving Periodic ...mentioning

confidence: 99%

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Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Khan,

Fouda,

et al. 2023

IEEE Access

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This paper surveys the short-term road traffic forecast algorithms based on the long-short term memory (LSTM) model of deep learning. The algorithms developed in the last three years are studied and analyzed. This provides an in-depth and thorough description of the algorithms rather than their marginal description as performed in the existing surveys that focus on general deep learning algorithms. The chosen algorithms are classified depending upon the use of LSTM in combination with other techniques for processing input data features towards a final traffic forecast. The operational strategies of the algorithms are described with merits and limitations. Moreover, a comparative analysis of the compared classes of algorithms is also provided. These strategies are helpful in selection of the right algorithms and their classes for the diverse traffic conditions and their future investigation for improvement. Besides, the applications of these classes of algorithms to traffic forecast in various networks for the latest decade is graphically depicted. Moreover, the applications of the LSTM in other fields involving a forecast are provided. Finally, the challenges associated with the short-term traffic forecast using the LSTM are described and strategies are highlighted for their future investigation.INDEX TERMS short-term traffic prediction, long short-term memory, LSTM, deep learning, intelligent transportation.

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Section: E Traffic Prediction Using Graph Convolutional Neural Networ...mentioning

confidence: 99%

Section: Accurate In Dealing With Data Prediction Involving Periodic ...mentioning

confidence: 99%

Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Khan,

Fouda,

et al. 2023

IEEE Access

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Graph convolutional LSTM algorithm for real-time crash prediction on mountainous freeways

Azati,

Wang,

Quddus

et al. 2024

International Journal of Transportation Science and Technology

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An Algorithm for Predicting Vehicle Behavior in High-Speed Scenes Using Visual and Dynamic Graphical Neural Network Inference

Li,

Liu,

Zhang

et al. 2024

Applied Sciences

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Accidents caused by vehicles changing lanes occur frequently on highways. Moreover, frequent lane changes can severely impact traffic flow during peak commuting hours and on busy roads. A novel framework based on a multi-relational graph convolutional network (MR-GCN) is herein proposed to address these challenges. First, a dynamic multilevel relational graph was designed to describe interactions between vehicles and road objects at different spatio-temporal granularities, with real-time updates to edge weights to enhance understanding of complex traffic scenarios. Second, an improved spatio-temporal interaction graph generation method was introduced, focusing on spatio-temporal variations and capturing complex interaction patterns to enhance prediction accuracy and adaptability. Finally, by integrating a dynamic multi-relational graph convolutional network (DMR-GCN) with dynamic scene sensing and interaction learning mechanisms, the framework enables real-time updates of complex vehicle relationships, thereby improving behavior prediction’s accuracy and real-time performance. Experimental validation on multiple benchmark datasets, including KITTI, Apollo, and Indian, showed that our algorithmic framework achieves significant performance improvements in vehicle behavior prediction tasks, with Map, Recall, and F1 scores reaching 90%, 88%, and 89%, respectively, outperforming existing algorithms. Additionally, the model achieved a Map of 91%, a Recall of 89%, and an F1 score of 90% under congested road conditions in a self-collected high-speed traffic scenario dataset, further demonstrating its robustness and adaptability in high-speed traffic conditions. These results show that the proposed model is highly practical and stable in real-world applications such as traffic control systems and self-driving vehicles, providing strong support for efficient vehicle behavior prediction.

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Traffic Density Based Travel-Time Prediction With GCN-LSTM

Cited by 3 publications

References 10 publications

Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Graph convolutional LSTM algorithm for real-time crash prediction on mountainous freeways

An Algorithm for Predicting Vehicle Behavior in High-Speed Scenes Using Visual and Dynamic Graphical Neural Network Inference

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