Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling

By Billy M. Williams. 2001

480 citations recorded by the source corpus.

Subfields: Automotive Engineering, Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/f9b1e11eef88cff5d5fb8183f2de4d52c5066761

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

In Building and Construction

In Transportation

Citations in the corpus

SARIMA modelling approach for railway passenger flow forecasting (2016, methodology): “1998; Lee, Fambro 1999; Williams 2001; Kamarianakis, Prastacos 2005) have been applied to forecast transportation demand.”
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…”
An Aggregation Approach to Short-Term Traffic Flow Prediction (2009, methodology): “As the MA and ES models cannot handle trend or seasonality well [31] and the ARIMA model is often used to forecast hourly time series [6], [7], we use the ARIMA model to forecast time series s3(t).”
LSTM network: a deep learning approach for short-term traffic forecast (2017, methodology): “Some other improved approaches such as Kohonen-ARIMA, subset ARIMA and vector autoregressive ARIMA are also used for short-term traffic forecast [14–16].”
Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models (2009, methodology): “Williams (2001) evaluated the use of ARIMA with exogenous variables (ARIMAX) model using the upstream flow data to a location as the input series to predict the flow at a location 30 minutes in to the future.”
A multivariate state space approach for urban traffic flow modeling and prediction (2003, background): “…ranging from ARIMA to dynamic generalized linear models (Chang and Miaou, 1999; Hamed et al., 1995; Lee and Fambro, 1999; Lingras et al., 2000; Williams, 2001), and neural networks (Chang et al., 2000; Fu and Rilett, 2000; Park et al., 1999; Park and Rilett, 1998; Rilett and Park, 1999; Smith…”
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].”
Spatio-Temporal Graph Structure Learning for Traffic Forecasting (2020, methodology): “Subsequently, many variant of ARIMA and nonparametric methods were applied on this task, including KARIMA (Van Der Voort, Dougherty, and Watson 1996), ARIMAX (Williams 2001), k -NN (Davis and Nihan 1991), Bayesian network (Sun, Zhang, and Yu 2006), and so on.”
A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, unknown): “Williams [18] discovered the impact of upstream traffic sensors to downstream ones and applied ARIMAXmodel for traffic flow prediction.”
Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, methodology): “ARIMA is a widely used parametric linear model and its extensions have been widely applied to predict traffic flow (Williams 2001; Zhang, Zhang, and Haghani 2014; Kumar and Vanajakshi 2015).”