Short‐term traffic forecasting: Overview of objectives and methods
By Eleni I. Vlahogianni, John C. Golias, Matthew G. Karlaftis. 2004
582 citations recorded by the source corpus.
Subfields: Building and Construction, Control and Systems Engineering, Transportation
View Paper: https://www.semanticscholar.org/paper/80cd209504af29d8db33cdcd1d9c1b7ad6765411
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In Building and Construction
In Control and Systems Engineering
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Citations in the corpus
- T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2018, background): “With the continuous advancement of research on traffic forecasting, a large number of methods with higher prediction precision have emerged, which can be mainly divided into two categories based on the model: parametric or nonparametric [25], [26].”
- Road Traffic Forecasting: Recent Advances and New Challenges (2018, background): “Prediction Horizon Prediction horizon [1], [7], [10], [11], [13], [25], [29], [30]”
- Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2012, methodology): “For the non-parametric techniques, several 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…”
- SARIMA modelling approach for railway passenger flow forecasting (2016, methodology): “There are two broad categories of forecasting approaches in transportation field: parametric and nonparametric (Smith et al. 2002; Vlahogianni et al. 2004; Wei, Chen 2012).”
- An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic (2017, background): “[2] summarized existing shortterm traffic predictions algorithms up to 2003.”
- Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition (2007, background): “Interested readers can refer to an article written by Vlahogianni et al. (2004) for a more comprehensive review of methods used for short-term traffic flow forecasting.”
- Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles (2015, background): “city power load [18], driving handling behaviors [19], and traffic flows [20] have verified its strong capability in predicting nonlinear dynamic behaviors.”
- A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, unknown): “learning models are data intensive [25].”
- A statistical method for estimating predictable differences between daily traffic flow profiles (2017, background): “The majority of the research aimed at predicting traffic flow profiles focuses on short term forecasting for real-time applications (see Vlahogianni et al. (2004) and Vlahogianni et al. (2014) for a review of methods applied). Vlahogianni et al. (2014) define short term forecasting as a process usin”
- Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models (2009, background): “Vlahogianni and colleagues (2004) compiled a comprehensive literature review of short-term forecasting techniques.”
- Forecasting: theory and practice (2020, background): “(see, for example, Whittaker et al., 1997; Vlahogianni et al., 2004; Kamarianakis and Prastacos, 2005; Vlahogianni et al., 2014).”
- LSTM network: a deep learning approach for short-term traffic forecast (2017, background): “Traffic forecast has been gradually shifting to computational intelligence approaches, and short-term traffic forecast based on deep learning approaches has become a new trend [34].”