Traffic Prediction Using Multivariate Nonparametric Regression

By Stephen Clark. 2003

349 citations recorded by the source corpus.

Subfields: Automotive Engineering, Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/0d17fa01698381f461d54473040aed3427baab11

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Citations in the corpus

Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2012, methodology): “…methods have been used to forecast the transportation demand such as neural networks (Dougherty, 1995; Vlahogianni et al., 2004), non-parametric regression (Smith et al., 2002; Clark, 2003), Kalman filtering models (Wang and Papageorgiou, 2007), and Gaussian maximum likelihood (Tang et al., 2003).”
Short‐term traffic forecasting: Overview of objectives and methods (2004, background): “Non-parametric regression possesses a number of advantages such as its intuitive formulation, the lack of a need for assumption on the transition of traffic states from one period to another, and, finally, the simplicity in modelling multivariate settings (Clark, 2003).”
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction (2017, methodology): “Multivariate nonparametric regression was also used in traffic prediction [12,13].”
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions (2009, methodology): “…real-time traffic flow, including multivariate state space time series (Stathopoulos & Karlaftis, 2003), multivariate non-parametric regression (Clark, 2003; Smith & Demetsky, 1996), nearest neighbor non-parametric regression (Davis & Nihan, 1991; Smith et al., 2002), dynamic generalized…”
SARIMA modelling approach for railway passenger flow forecasting (2016, neutral): “1998; Clark 2003), Kalman filters (Whittaker et al.”
A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system (2015, methodology): “[13] have proven that the Gaussian maximum likelihood model appears to be better than the time series model and nonparametric regression [14] when predicting daily traffic flows in Hong Kong.”
Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning (2014, methodology): “LWL model [23], the multivariate nonparametric regression model [4], the NN model [24], and the NN-S model [3].”
Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models (2009, methodology): “The use of nonparametric regression and approaches to traffic prediction has been explored in multiple studies, notably by Smith and Demetsky (1996), Smith and other colleagues (2002), and Clark (2003).”