Short-term travel time prediction

By Xiaoyan Zhang, John Rice. 2003

348 citations recorded by the source corpus.

How researchers see this paper

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LSTM network: a deep learning approach for short-term traffic forecast (2017, background): “Over the past few decades, many data analysis models have been proposed to solve the short-term traffic forecast, including historical average and smoothing [4, 5], statistical and regression methods [6, 7], traffic flow theory-based methods [8, 9] and machine learning techniques [10, 11].”
Accurate freeway travel time prediction with state-space neural networks under missing data (2005, background): “These models are referred to as instantaneous travel time predictors, see e.g. Zhang and Rice (2003) and Van Toorenburg (1998)—in Dutch.”
A simple and effective method for predicting travel times on freeways (2004, methodology): “Earlier work with the method [19] used a short stretch of freeway with very-high-quality data, including probe vehicles.”
A gradient boosting method to improve travel time prediction (2015, background): “, 2013b), various machine learning techniques (Zhang and Rice, 2003; Wang and Shi, 2012; Wei and Chen, 2012) and traffic flow theory based methods (Li et al.”
Travel time prediction with LSTM neural network (2016, background): “The linear model [6], [7] predicts the travel time of one trip departing at current time”