Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches

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Road Traffic Forecasting: Recent Advances and New Challenges (2018, background): “Network-wide predictions have been explored mainly via simulation [10], [11], although other approaches are found in literature [35], [36].”
A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, background): “Kamarianakis and Prastacos [20] discussed and compared the Vector Autoregressive Moving Average and Single Space-TimeARIMAmodel.”
Traffic Flow Prediction With Big Data: A Deep Learning Approach (2015, methodology): “Many variants of ARIMA were proposed to improve prediction accuracy, such as KohonenARIMA (KARIMA) [13], subset ARIMA [14], ARIMA with explanatory variables (ARIMAX) [15], vector autoregressive moving average (ARMA) and space–time ARIMA [16], and seasonal ARIMA (SARIMA) [17].”
A Survey on Deep Learning for Human Mobility (2020, methodology): “SARIMA, vector ARMA and space-time ARMA are designed to overtake this assumption, but they present other issues.”