Estimating Urban Traffic Congestions with Multi-sourced Data

By Senzhang Wang, Lifang He, Leon Stenneth, Philip S. Yu, Zhoujun Li, Zhiqiu Huang. 2016

38 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. Philip S. Yu
2. Senzhang Wang
3. Zhoujun Li
4. Leon Stenneth
5. Lifang He

In Transportation

1. Senzhang Wang
2. Philip S. Yu
3. Zhoujun Li
4. Zhiqiu Huang
5. Leon Stenneth
6. Lifang He

Citations in the corpus

• Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting (2020, methodology): “In addition to parametric learning based predictive models, there are some researchers having attempted to integrate multi-source traffic data including external factors such as traffic accidents, festival, weather, etc [31, 32, 33, 10, 11].”
• Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017, methodology): “Note that different from our previous model [Wang et al. 2016a], we take the weather information into consideration to guide us in selecting more relevant historical data.”