Abstract
Predicting the travel time of a given path plays an indispensable role in intelligent transportation systems. Although many prior researches have struggled for accurate prediction results, most of them achieve inferior performance due to insufficient extraction of travel speed features from the sparse trajectory data, which confirms the challenges involved in this topic. To overcome those issues, we propose a deep learning framework named Tensor-CNN-LSTM (TCL) in this paper, which can extract travel speed effectively from historical sparse trajectory data and predict travel time with better accuracy. Empirical results over two real-world large-scale datasets show that our proposed TCL can achieve significantly better performance and remarkable robustness.
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- 1.
In our experiments, there are only 1.00% and 1.56% roads in Beijing and Shanghai can satisfy this condition, respectively.
- 2.
Once future information is added, such as the real travel speed, the problem will no longer be travel time prediction [1].
- 3.
The top-k most relevant grids are calculated by time-shifting KL-divergence.
- 4.
The sampling rates on two datasets are different, Beijing has low sampling rates (sampling interval is 60 s), and Shanghai owns higher sampling rates (10 s).
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Acknowledgment
This work is partially supported by the National Natural Science Foundation of China (U1711262, U1811264, 11501204).
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Shen, Y., Hua, J., Jin, C., Huang, D. (2019). TCL: Tensor-CNN-LSTM for Travel Time Prediction with Sparse Trajectory Data. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_39
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DOI: https://doi.org/10.1007/978-3-030-18590-9_39
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