A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting
By Pinlong Cai, Yunpeng Wang, G. Lu, Peng Chen, C. Ding, Jianping Sun. 2016
456 citations recorded by the source corpus.
Subfields: Building and Construction, Transportation
View Paper: https://www.semanticscholar.org/paper/b245760a4cccda4f2e352078b1de467c3263bd4a
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
- Ruimin Ke
- Zhiyong Cui
- Yinhai Wang
- Zhifeng Bao
- A. K. Qin
- Farhana Murtaza Choudhury
- Baihua Zheng
- David Alexander Tedjopurnomo
In Transportation
- Yinhai Wang
- Zhiyong Cui
- Ruimin Ke
- Zhifeng Bao
- A. K. Qin
- Farhana Murtaza Choudhury
- Baihua Zheng
- David Alexander Tedjopurnomo
Citations in the corpus
- A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, methodology): “Other types of machine learning models aside from neural network were also used, such as the kNearest Neighbor [31], [32], [33] and the Support Vector Regression [34], [35], [36].”
- Road Traffic Forecasting: Recent Advances and New Challenges (2018, methodology): “Exogenous factors continue to be barely addressed in recent research, and calendar information is the most considered one [9], [32], [44], [46], [55], [61], [69], [76], [87], [88], [93], [104], [107].”
- Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2017, background): “Besides, ST-KNN is a non-parametric approach and each road is modeled and calculated separately (Cai et al., 2016), which makes it hard to generalize to unseen situations and to scale to large datasets.”
- Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, methodology): “The representative machine learning methods include support vector regression Asif et al. (2013), K-nearest neighbor Cai et al. (2016), etc.”
- Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction (2021, methodology): “Thereafter, many data-driven techniques were designed to address the short-time traffic prediction problem, such as K-Nearest Neighbor (KNN) [15] and other clustering-based methods [16], Autoregressive Integrated Moving Average Model (ARIMA) and its extensions [17], [18], Support Vector Regression…”