Monte Carlo simulation-based traffic speed forecasting using historical big data

Because the traffic patterns on roads vary according to the roads' specific spatio-temporal behavior, if we would like to forecast the traffic speed by day of the week, it is necessary to determine an optimal set of the highly related historical patterns to achieve high prediction accuracy. The goal of our paper is to suggest a new statistical modeling method that finds the best historical dataset according to various analyses for each link and provides a more accurate prediction of traffic flow by day of the week. First, we suggest a three-step filtering algorithm based on changepoint analysis, correlation analysis, and Monte Carlo simulation to simultaneously find and remove historical data outliers. Second, we determine the optimal historical data range by using decision factors such as the Mean Squared Error (MSE) and Akaike Information Criterion. Moreover, to verify our statistical model, we use various prediction accuracy measures such as Mean Absolute Percentage Error (MAPE), R-squared value, and Root MSE (RMSE). Finally, we construct a big data processing framework to handle the overall prediction process and calculate large amounts of traffic data. The forecasting results show that the proposed model can achieve a high prediction accuracy for each road by using three measures: less than 20% for MAPE, more than 80% for R-squared value, and less than 1 on average for RMSE.