2023 Volume 4 Issue 2 Pages 142-153
Short-term speed prediction was investigated as a way to utilize the digital twin of traffic conditions. Traffic data for England is publicly available in real time, and its historical data can be downloaded as open data. In this study, we selected a relatively congested point of an arterial road from the downloaded data, compared the traffic condition data with neighboring points of the target, and predicted the speed using machine learning (AI). In addition, we compared the importance of features using SHapley Additive ex-Planations (SHAP), and found that not only the current situation, but also the past speed history had an effect on improving the prediction performance. In the authors’ previous study, only the data of the forecast point were used as input variables for the short-term forecasting, but in this study, the traffic conditions of the neighboring points were input to see if there was any improvement in forecasting performance. The results showed a slight improvement in prediction performance at the points of interest in this study due to the traffic conditions at the neighboring points.
