Mercury: Metro density prediction with recurrent neural network on streaming CDR data

By Victor C. Liang, Richard T. B. Ma, Wee Siong Ng, Li Wang, Marianne S. Winslett, Huayu Wu, Shanshan Ying, Zhenjie Zhang. 2016

36 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Ala Al-Fuqaha
2. M. Mohammadi
3. Mohsen Guizani
4. Sameh Sorour
5. Chaoyun Zhang
6. Ekram Hossain
7. Syed Ali Hassan
8. Rasheed Hussain

In Transportation

1. Ala Al-Fuqaha
2. Mohsen Guizani
3. Shree Krishna Sharma
4. Xianbin Wang

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[117] presented a real-time crowd density prediction system in transportation stations that leverages the mobile phone users’ telecommunication data known as caller detail record (CDR).”
• Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, background): “[237] combine recurrent neural networks with geographical spatial data processing and parallel streaming analytical programming for crowd density prediction in metro areas.”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “propose Mercury to estimate metro density from streaming CDR data, using RNNs [201].”