@inproceedings{9c8e2096c55e4cbb9913eb4762656572,
title = "TCL: Tensor-CNN-LSTM for Travel Time Prediction with Sparse Trajectory Data",
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.",
author = "Yibin Shen and Jiaxun Hua and Cheqing Jin and Dingjiang Huang",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 24th International Workshops on Database Systems for Advanced Applications, DASFAA 2019: BDMS, BDQM, and GDMA ; Conference date: 22-04-2019 Through 25-04-2019",
year = "2019",
doi = "10.1007/978-3-030-18590-9\_39",
language = "英语",
isbn = "9783030185893",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "329--333",
editor = "Guoliang Li and Jun Yang and Joao Gama and Juggapong Natwichai and Yongxin Tong",
booktitle = "Database Systems for Advanced Applications - DASFAA 2019 International Workshops",
address = "德国",
}