A Broad Learning Ensemble System Using Bagging for Typhoon Trajectory Forecasting

Hao, Fengyun; Jin, Jian

doi:10.1109/iccece54139.2022.9712832

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…With the innovative development of communication technology and location acquisition equipment, satellites, surveillance systems, and mobile devices are constantly collecting trajectory data from various moving objects. The massive trajectory data, containing rich information and knowledge, can be applied to such fields as location recommendation [1,2], intelligent transportation systems [3][4][5], and public safety [6,7]. Today, trajectory data mining has become one of the research hotspots in Computer Science and Geographic Information Science.…”

Section: Introductionmentioning

confidence: 99%

Learning Universal Trajectory Representation via a Siamese Geography-Aware Transformer

Wu,

Xiang,

Chen

et al. 2024

IJGI

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With the development of location-based services and data collection equipment, the volume of trajectory data has been growing at a phenomenal rate. Raw trajectory data come in the form of sequences of “coordinate-time-attribute” triplets, which require complicated manual processing before they can be used in data mining algorithms. Current works have started to explore the emerging deep representation learning method, which maps trajectory sequences to vector space and applies them to various downstream applications for boosting accuracy and efficiency. In this work, we propose a universal trajectory representation learning method based on a Siamese geography-aware transformer (TRT for short). Specifically, we first propose a geography-aware encoder to model geographical information of trajectory points. Then, we apply a transformer encoder to embed trajectory sequences and use a Siamese network to facilitate representation learning. Furthermore, a joint training strategy is designed for TRT. One of the training objectives is to predict the masked trajectory point, which makes the trajectory representation robust to low sampling rates and noises. The other is to distinguish the difference between trajectories by means of contrastive learning, which makes the trajectory representation more uniformly distributed over the hypersphere. Last, we design a benchmark containing four typical traffic-related tasks to evaluate the performance of TRT. Comprehensive experiments demonstrate that TRT consistently outperforms the state-of-the-art baselines across all tasks.

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Section: Introductionmentioning

confidence: 99%

Learning Universal Trajectory Representation via a Siamese Geography-Aware Transformer

Wu,

Xiang,

Chen

et al. 2024

IJGI

View full text Add to dashboard Cite

show abstract

Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic

Chen

Yang

Huang

et al. 2023

Entropy

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This study proposes a decomposed broad learning model to improve the forecasting accuracy for tourism arrivals on Hainan Island in China. With decomposed broad learning, we predicted monthly tourist arrivals from 12 countries to Hainan Island. We compared the actual tourist arrivals to Hainan from the US with the predicted tourist arrivals using three models (FEWT-BL: fuzzy entropy empirical wavelet transform-based broad learning; BL: broad Learning; BPNN: back propagation neural network). The results indicated that US foreigners had the most arrivals in 12 countries, and FEWT-BL had the best performance in forecasting tourism arrivals. In conclusion, we establish a unique model for accurate tourism forecasting that can facilitate decision-making in tourism management, especially at turning points in time.

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A Broad Learning Ensemble System Using Bagging for Typhoon Trajectory Forecasting

Cited by 2 publications

References 8 publications

Learning Universal Trajectory Representation via a Siamese Geography-Aware Transformer

Learning Universal Trajectory Representation via a Siamese Geography-Aware Transformer

Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic

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