A method for filling traffic data based on feature-based combination prediction model
Abstract Data imputation is a critical step in data processing, directly influencing the accuracy of subsequent research. However, due to the temporal nature of ride-hailing trajectory data, traditional imputation methods often struggle to adequately consider spatiotemporal characteristics, leading...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-92547-y |
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| Summary: | Abstract Data imputation is a critical step in data processing, directly influencing the accuracy of subsequent research. However, due to the temporal nature of ride-hailing trajectory data, traditional imputation methods often struggle to adequately consider spatiotemporal characteristics, leading to limitations in both convergence speed and accuracy. To address this issue, this study employs a prediction-based approach to enhance imputation accuracy. Given the limited feature parameters in trajectory data, traditional prediction models often fail to comprehensively capture data characteristics. Therefore, this study proposes a feature generation model based on LightGBM-GRU, combined with a SARIMA-GRU prediction model, to more thoroughly capture and enrich the data characteristics. This approach effectively imputes missing data, thereby laying a solid foundation for subsequent research. |
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| ISSN: | 2045-2322 |