A multi-task deep learning approach for lane-level pavement performance prediction with segment-level data
<|reference_start|>A multi-task deep learning approach for lane-level pavement performance prediction with segment-level data: The elaborate pavement performance prediction is an important premise of implementing preventive maintenance. Our survey reveals that in practice, the pavement performance is usually measured at segment-level, where an unique performance value is obtained for all lanes within one segment of 1km length. It still lacks more elaborate performance analysis at lane-level due to costly data collection and difficulty in prediction modeling. Therefore, this study developed a multi-task deep learning approach to predict the lane-level pavement performance with a large amount of historical segment-level performance measurement data. The unified prediction framework can effectively address inherent correlation and differences across lanes. In specific, the prediction framework firstly employed an Long Short-Term Memory (LSTM) layer to capture the segment-level pavement deterioration pattern. Then multiple task-specific LSTM layers were designed based on number of lanes to capture lane-level differences in pavement performance. Finally, we concatenated multiple task-specific LSTM outputs with auxiliary features for further training and obtained the lane-level predictions after fully connected layer. The aforementioned prediction framework was validated with a real case in China. It revealed a better model performance regardless of one-way 2-lane, 3-lane, and 4-lane scenarios, all lower than 10% in terms of mean absolute percentage error. The proposed prediction framework also outperforms other ensemble learning and shallow machine learning methods in almost every lane.<|reference_end|>
@article{wang2024a,
    title={A multi-task deep learning approach for lane-level pavement performance
  prediction with segment-level data},
    author={Bo Wang and Wenbo Zhang and Yunpeng LI},
    journal={arXiv preprint arXiv:2408.01967},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01967},
    primaryClass={cs.LG}
}