A multivariate state space approach for urban traffic flow modeling and prediction

By A. Stathopoulos, Matthew G. Karlaftis. 2003

538 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/df439fec446a580b873a4303633d4664e3342417

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In Building and Construction

In Control and Systems Engineering

In Transportation

Citations in the corpus

Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models (2009, unknown): “Stathopoulos and Karlaftis (2003) then extended the application of multivariate approaches by applying state space approaches (which is also the Kalman filtering technique) and extended it to use the crosscorrelations from the upstream detectors.”
Traffic Flow Prediction With Big Data: A Deep Learning Approach (2015, methodology): “The multivariate state-space models and the ARIMA models were compared in [50], and it showed that the performance of the multivariate state-space models is better than that of the ARIMA models.”
Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition (2007, methodology): “…1998), ATHENA (Kirby et al., 1997), pattern recognition (Wild, 1997), spectral analysis (Nicholson and Swann, 1974), Kalman filtering theory (Okutani and Stephanedes, 1984; Stathopoulos and Karlaftis, 2003), adaptive prediction system (Lu, 1990), and Gaussian maximum likelihood models (Lin, 2001).”
Road Traffic Forecasting: Recent Advances and New Challenges (2018, background): “Again, simulation models are the best suited to address this issue, parametrizing preceding inputs; notwithstanding, some researchers have concentrated on this matter by incorporating spatial information to time series [28], neural networks [37]–[39] or other non-parametric approaches [14], [40].”
Short-term traffic flow prediction using seasonal ARIMA model with limited input data (2015, methodology): “In most of the studies on flow prediction [23, 24, 29, 30, 35], a MAPE in the range of 10–20 % was reported.”
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions (2009, methodology): “Several other techniques have been applied to predict 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,…”
Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, background): “Since Okutani and Stephanedes (1984) first applied the spatial characteristics of road traffic to traffic flow prediction, more and more traffic prediction studies have considered spatial correlations of traffic (Ermagun and Levinson 2018; Stathopoulos and Karlaftis 2003).”
Space-time modeling of traffic flow (2002, methodology): “Total daily demand for travel is about 5.5 million trips with about 1 million occurring during the 2-h peak period (Stathopoulos and Karlaftis, 2003).”
Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches (2003, background): “(2), and Stathopoulos and Karlaftis (10) with a relatively small number of measurement locations.”
Short‐term traffic forecasting: Overview of objectives and methods (2004, methodology): “Exceptions are the works of Williams (2001), who developed a multivariate ARIMA approach to model traffic flow, of Okutani and Stephanedes (1984) and Stathopoulos and Karlaftis (2003) with a state-space approach to modelling traffic flow, and of Tebaldi et al. (2002) using dynamic hierarchical…”