Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach

By Arief Barkah Koesdwiady, Ridha Soua, F. Karray. IEEE Transactions on Vehicular Technology, 2016

316 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/a3f0ebc7a124b84c5de2c73b6f0dae36c919f322

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. Weibin Zhang
2. Yinghao Yu
3. Chunhong Pan
4. Qi Zhang
5. Shiming Xiang
6. Feng Shu
7. Yong Qi
8. Jianlong Chang

In Control and Systems Engineering

1. Weibin Zhang
2. Chunhong Pan
3. Shiming Xiang
4. Gaofeng Meng
5. Yinhai Wang

In Transportation

1. Yinghao Yu
2. Weibin Zhang
3. Chunhong Pan
4. Qi Zhang
5. Shiming Xiang
6. Feng Shu
7. Yong Qi
8. Jianlong Chang

Citations in the corpus

• Spatio-Temporal Graph Structure Learning for Traffic Forecasting (2020, methodology): “Deep belief network was applied on traf-fic speed and flow forecasting tasks (Jia, Wu, and Du 2016; Koesdwiady, Soua, and Karray 2016).”
• Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, background): “The applicationofadeeplearningframeworktothefieldoftransportationhasproventhatitcan achieve good performance in real traffic prediction problems (Lv et al. 2015; Koesdwiady, Soua, and Karray 2016; Polson and Sokolov 2017).”