Correction to “Porous Zr-Based Metal Organic Frameworks (Zr-MOFs) Incorporated Thin Film Nanocomposite Membrane toward Enhanced Desalination Performance”

Travel time prediction has been an interesting research for decades within which various prediction models have been developed. This paper discusses and compares the results and accuracy generated by different prediction models with real-time and historical data collected by TRANSCOM through Road Side Terminals (RST), on the New York State Thruway (NYST). All the tagged vehicles equipped with EZ pass are scanned by RST, while vehicle entry times and the associated reader numbers are recorded. The emphasis of this study is focused on modeling the real-time and historical data for travel time prediction. Factors that would affect the prediction results are discussed. The Kalman filtering algorithm is developed for prediction due to its significant property of updating the state variable continuously as new observation is available. The results reveal that the travel times predicted with link-based data are better than that predicted with path-based data during off peak period and vice versa. In addition to the real-time information the study considers the aggregate data of previous time intervals and days as historical seeds for evaluating prediction accuracy