A simple and effective method for predicting travel times on freeways

By John Rice, Erik van Zwet. IEEE transactions on intelligent transportation systems (Print), 2004

389 citations recorded by the source corpus.

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Accurate freeway travel time prediction with state-space neural networks under missing data (2005, methodology): “These approaches include ARIMA(X) models (Williams, 2001), non-linear time series modeling (Ishak and Al-Deek, 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,…”
Travel-time prediction with support vector regression (2004, background): “SVM is quite satisfying from a theoretical point of view and can lead to great potential and superior performance in practical applications.”
Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches (2003, methodology): “When multiplied by a constant related to the average vehicle length it can provide a proxy for the exact speed (4).”
Travel time prediction with LSTM neural network (2016, methodology): “The linear model [6], [7] predicts the travel time of one trip departing at current time”
Space-time modeling of traffic flow (2002, neutral): “Keywords: ARIMA; Time series; Network”
Travel time estimation of a path using sparse trajectories (2014, methodology): “[9, 14, 16] use various models to estimate the travel speed on an individual road segment based on the sensor readings from loop detectors, and then convert the speed into a travel time.”