Space-time modeling of traffic flow

By Yiannis Kamarianakis, Poulicos Prastacos. Computational Geosciences, 2002

322 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b0f4d5003cffa6b66c652f1798a1efe5738270d7

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In Building and Construction

In Control and Systems Engineering

In Transportation

Citations in the corpus

A Survey on Deep Learning for Human Mobility (2020, methodology): “Crowd flow prediction may be tackled using time-series prediction models based on autoregression (AR), such as the AutoRegressive Moving Average (ARMA) [22], the Autoregressive Integrated Moving Average (ARIMA) [111, 126], and their variants such as the Stationary and Seasonal ARIMA (SARIMA) [206], ”
Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data (2015, background): “Kamarianakis and Prastacos [22] and Min and Wynter [23] proposed the space–time autoregressive integrated moving-average model to satisfy interrelations between links.”
Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models (2009, background): “Kamarianakis and Prastacos (2002) used space-time modeling of traffic flow on urban arterial networks in Athens, Greece, by defining neighborhood matrices that reflect the position of the loop detectors and incorporating it in their model specification.”
Consistency and asymptotic normality of least squares estimators in generalized STAR models (2008, methodology): “STAR models are also well known in economics (Giacomini and Granger, 2004) and have been applied to forecasting of regional employment data (Hernandez-Murillo and Owyang, 2004) and traffic flow (Garrido 2000; Kamarianakis and Prastacos, 2005).”
EOF Calculations and Data Filling from Incomplete Oceanographic Datasets (2003, methodology): “Spatio-temporal autocorrelations can be considered by using multivariate ARIMA and space-time autoregressive models (Kamarianakis and Prastacos, 2003;20 Wikle et al., 1998; Kamarianakis and Prastacos, 2005); however, ARIMA cannot capture nonlinear trends because it assumes a linear dependence…”
Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches (2003, methodology): “Both the augmented Dickey Fuller and the Bos Fetherston tests were performed (see (12) for details) and as expected, they indicated nonstationarity for the raw data.”