Travel-time prediction with support vector regression

By Chun-Hsin Wu, Jan-Ming Ho, Der-Tsai Lee. IEEE transactions on intelligent transportation systems (Print), 2004

1,342 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/1193b52bb6c4baa55edb7bbad3dd0e20da497d1c

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Citations in the corpus

A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting (2020, background): “hese problems well because they can learn the statistical laws of data automatically with adequate historical data. Common non-parametric models include k-nearest [2], support vector regression (SVR) [11, 30], fuzzy logic [34], Bayesian network[28], and neural network models. Recently, deep neural n”
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2018, methodology): “model [5], [6], the Kalman filtering model [7], the support vector regression machine model [8], [9], the k-nearest neighbor model [10], the Bayesian model [11], and partial neural network model [12], [13].”
Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting (2021, methodology): “To relax the linear assumption, traditional machine learning-based models such as SVR [3] and KNN [4] are adopted.”
Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach (2017, background): “, 2014a), travel time prediction (Wu et al., 2004), traveler socio-demographic role prediction (Zhu et al.”
An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic (2017, methodology): “The support vector regression (SVR) model has been widely used in travel time [20] and speed [23] prediction.”
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, methodology): “Thus, those classical statistical models, such as ARIMA Williams and Hoel (2003) and machine learning models, such as support vector regression Wu et al. (2004) and random forest Zarei et al. (2013), are not compared in this study.”
GMAN: A Graph Multi-Attention Network for Traffic Prediction (2019, methodology): “…in trafﬁc conditions, compared with traditional time-series methods (e.g., auto-regressive integrated moving average (ARIMA) (Makridakis and Hibon 1997)) and machine learning models (e.g., support vector regression (SVR) (Wu, Ho, and Lee 2004), k-nearest neighbor (KNN) (Zheng and Su 2014)).”
Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction (2013, methodology): “Several short-term prediction methods for traffic flow, including artificial intelligence and optimization-based methods, have been proposed during the last several decades [1], [8], [9], [16], [17], [31], [32], [37].”
Time Series Prediction Using Support Vector Machines: A Survey (2009, methodology): “[92] used an SVR to analyze and predict travel time for highway traffic.”
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018, methodology): “Many classical baseline models used in traffic forecasting problems, like ARIMA [2, 23] Support Vector Regression (SVR) [37], Kalman filter [35].”
Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction (2021, methodology): “…methods [16], Autoregressive Integrated Moving Average Model (ARIMA) and its extensions [17], [18], Support Vector Regression (SVR) [19]–[21], Random Forest Regression [22], [23], Neural Networks(NN) [24], [25], Bayesian networks (BNs) [26], [27], Recurrent Neural Network (RNN)…”
Accurate freeway travel time prediction with state-space neural networks under missing data (2005, background): “…2002; DÕAngelo et al., 1999) linear (Rice and Van Zwet, 2001; Zhang and Rice, 2003; Sun et al., 2003), and support vector regression models (Chun-Hsin et al., 2003); feed-forward (Innamaa, 2001; Rilett and Park, 2001; Park and Rilett, 1999; Cheu, 1998; Huisken and Van Berkum, 2003) and…”
Travel time estimation of a path using sparse trajectories (2014, neutral): “[19] predicts the travel time of a road segment by applying support vector regression to its historical travel times.”
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction (2017, methodology): “SVM usually takes a long time and consumes considerable computer memory on training and, hence, it might be powerless in large data-related applications.”