Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data

By Afshin Abadi, Tooraj Rajabioun, Petros A. Ioannou. IEEE transactions on intelligent transportation systems (Print), 2015

334 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/dfa3b8924ec7af29251545208dd97d988554ab07

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. Junbo Zhang
2. Yu Zheng
3. Xiuwen Yi
4. Tianrui Li
5. Dekang Qi
6. Yuxuan Liang
7. Junkai Sun
8. Philip S. Yu

In Control and Systems Engineering

1. Yu Zheng
2. Junbo Zhang
3. Dekang Qi
4. Junkai Sun

In Transportation

1. Junbo Zhang
2. Yu Zheng
3. Xiuwen Yi
4. Dekang Qi
5. Tianrui Li
6. Yuxuan Liang
7. Junkai Sun
8. Philip S. Yu

Citations in the corpus

• Process (2020, background): “Most of the previous works [10, 11, 12, 13, 14, 15, 16, 17, 18, 19] on traffic modeling focus on predicting speed, volume, density and travel time, which have achieved remarkable success in this field.”
• Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017, background): “Some other researchers aim to predict travel speed and traffic volume on the road [25, 26, 27].”
• Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning (2020, background): “Some other researchers aim to predict travel speed and traffic volume on the road [1], [25].”
• Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (2019, background): “Some other researchers aim to predict travel speed and traffic volumes on roads [1].”