Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference

Author:

Tang Xianfeng1,Gong Boqing2,Yu Yanwei3,Yao Huaxiu1,Li Yandong4,Xie Haiyong5,Wang Xiaoyu6

Affiliation:

1. The Pennsylvania State University, USA

2. Tencent, USA

3. Yantai University, China

4. University of Central Florida, USA

5. University of Science and Technology of China, China

6. Intellifusion Inc., China

Publisher

ACM Press

Reference47 articles.

1. Andreas Argyriou, Mark Herbster, and Massimiliano Pontil. 2006. Combining graph Laplacians for semi-supervised learning. In Advances in Neural Information Processing Systems. 67-74.

2. Muhammad Tayyab Asif, Nikola Mitrovic, Justin Dauwels, and Patrick Jaillet. 2016. Matrix and tensor based methods for missing data estimation in large traffic networks. IEEE Transactions on Intelligent Transportation Systems 17, 7(2016), 1816-1825.

3. Javed Aslam, Sejoon Lim, Xinghao Pan, and Daniela Rus. 2012. City-scale traffic estimation from a roving sensor network. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems. ACM, 141-154.

4. Prithu Banerjee, Sayan Ranu, and Sriram Raghavan. 2014. Inferring uncertain trajectories from partial observations. In Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, 30-39.

5. Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 785-794.

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