Reconstruction of Missing Trajectory Data: A Deep Learning Approach

Wang, Ziwei; Zhang, Shiyao; Yu, James J.Q.

doi:10.1109/itsc45102.2020.9294402

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Wang et al [22] have introduced a novel deep learning approach to address the prevalent problem of missing trajectory data in GPS applications. Their proposed method has leveraged an RNN with an encoder-decoder architecture and an attention mechanism [23].…”

Section: Related Workmentioning

confidence: 99%

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

Barua,

Halder,

Shopnil

et al. 2024

Preprint

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Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges for adverse weather conditions, interference from other vehicles sensors and electronic devices, and signal reception failure, leading to incompleteness in trajectory data. But for real-time decision making for autonomous driving, the trajectory imputation is no less crucial. Previous attempts to address this issue such as statistical inference and machine learning approaches have shown promise. Yet, the landscape of deep learning is rapidly evolving with new and more robust models emerging. In this research, we have proposed an encoder-decoder architecture, Human Trajectory Imputation Model, coined as HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model has been evaluated using the Intersection drone inD dataset, containing trajectory data at suitable altitudes preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we have utilized L1, MSE, Quantile and ADE loss. Our experiments have demonstrated that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories.

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Section: Related Workmentioning

confidence: 99%

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

Barua,

Halder,

Shopnil

et al. 2024

Preprint

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“…This study proposes a new methodology, called the DSE-NN interpolation method, for reconstructing ocean temperature and salinity using deep neural networks. The input latitude and longitude data are discretized into a specialized vector, which has been proven to be effective in accurately capturing temporal patterns and improving the accuracy of the reconstruction process [27]. We demonstrate the effectiveness of our approach by utilizing the latitude and longitude range of 35 • W to 65 • W and the latitude range of 20 • N to 50 • N in the North Atlantic as a case study.…”

Section: Introductionmentioning

confidence: 99%

DSE-NN: Discretized Spatial Encoding Neural Network for Ocean Temperature and Salinity Interpolation in the North Atlantic

Liu,

Jia,

Zhang

2024

JMSE

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The precise interpolation of oceanic temperature and salinity is crucial for comprehending the dynamics of marine systems and the implications of global climate change. Prior neural network-based interpolation methods face constraints related to their capacity to delineate the intricate spatio-temporal patterns that are intrinsic to ocean data. This research presents an innovative approach, known as the Discretized Spatial Encoding Neural Network (DSE-NN), comprising an encoder–decoder model designed on the basis of deep supervision, network visualization, and hyperparameter optimization. Through the discretization of input latitude and longitude data into specialized vectors, the DSE-NN adeptly captures temporal trends and augments the precision of reconstruction, concurrently addressing the complexity and fragmentation characteristic of oceanic data sets. Employing the North Atlantic as a case study, this investigation shows that the DSE-NN presents enhanced interpolation accuracy in comparison with a traditional neural network. The outcomes demonstrate its quicker convergence and lower loss function values, as well as the ability of the model to reflect the spatial and temporal distribution characteristics and physical laws of temperature and salinity. This research emphasizes the potential of the DSE-NN in providing a robust tool for three-dimensional ocean temperature and salinity reconstruction.

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Challenges of spatio-temporal trajectory datasets

Arslan,

Cruz

2024

Journal of Location Based Services

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Reconstruction of Missing Trajectory Data: A Deep Learning Approach

Cited by 6 publications

References 25 publications

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

DSE-NN: Discretized Spatial Encoding Neural Network for Ocean Temperature and Salinity Interpolation in the North Atlantic

Challenges of spatio-temporal trajectory datasets

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