Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models

By Srinivasa Ravi Chandra, Haitham Al-Deek. Journal of Intelligent Transportation Systems / Taylor & Francis, 2009

347 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/3858c84faff4fe25479b9721b789cc2a44b3e0ef

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. Yinhai Wang
4. Junbo Zhang
5. Yu Zheng
6. Ruimin Ke
7. Zhiyong Cui
8. Xiuwen Yi

In Control and Systems Engineering

1. Yinhai Wang
2. Zhiyong Cui
3. Ruimin Ke
4. Philip S. Yu
5. Gabriel-Miro Muntean
6. John Murphy
7. Soufiene Djahel
8. Yu Zheng

In Transportation

1. Philip S. Yu
2. Senzhang Wang
3. Junbo Zhang
4. Yu Zheng
5. Xiuwen Yi
6. Yunpeng Wang
7. Xiaolei Ma
8. Dekang Qi

Citations in the corpus

• Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018, background): “Previous studies [26, 27] which only making use of the forward dependencies of time series data have found that the past speed values of upstream as well as downstream locations influence the future speed values of a location along a corridor.”
• Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting (2020, methodology): “Generally, traditional traffic passenger flows prediction methods can be categorized into classic statistical models, including Auto-Regressive Integrated Moving Average (ARIMA) based methods [25, 26] and parametric learning methods, including K-nearest neighbor (KNN) nonparametric regression methods,”
• Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017, background): “The Vector Autoregressive (VAR) models capture the linear inter dependencies among interrelated time series [31].”
• Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks (2017, background): “Al-Deek proposed vector autoregressive models for short-term traffic prediction on freeways that consider upstream and downstream location information and yield a high accuracy [16].”
• Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning (2020, methodology): “Existing timeseries models, like the auto-regressive integrated moving average model (ARIMA, [3]), seasonal ARIMA [26], and the vector autoregressive model [4] can capture the temporal dependencies very well, yet it fails to handle spatial correlations.”
• A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches (2015, methodology): “Finally, the third factor that affects the forecasting accuracy is the technique used to model the traffic data, such as statistical and time series analysis models [107], [108], the well-known autoregressive-integratedmoving-average model [103], [106], neural networks [105], pattern recognition tec”