  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.
