Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

“…One way to reduce, for example, the cost of air quality monitoring is estimating emissions based on various traffic properties such as flow, speed, and vehicle fleet composition (see Kean et al (2003) or Barth and Boriboonsomsin (2009b)). Furthermore, the cost of traffic monitoring can be significantly reduced by deployment strategies that would maximize the utility of traffic-related monitoring, given a predefined number of monitoring units (such as by employing correlations with upstream or downstream traffic; Chandra & Al-Deek, 2009). …”

Comparative Evaluation of The Traffic Flow Volatility Forecasting Models

“…To further improve the short-term forecasting accuracy, some multivariate models was introduced to calibrate the relationships between different traffic flow variables at a traffic station or the same variable at different traffic stations. The most popular multivariate forecasting models include state space model [13], multivariable nonparametric regression model [14] and vector auto-regression model [15][16]. Whatever univariate or multivariate, the above models focus on improving the forecasting accuracy through point estimation techniques.…”

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

“…Incomplete or inaccurate input data produce an inaccurate time series model, resulting in an incorrect prediction [8]. Time series models used to predict traffic flow include the auto-regressive integrated moving average (ARIMA) [9], seasonal ARIMA [10], vector ARIMA [11], and ARIMA with EXtra (ARIMAX) [12].…”