Correction to: An adaptive model of optimal traffic flow prediction using adaptive wildfire optimization and spatial pattern super learning

Real-time traffic prediction uses past data to anticipate traffic volume. The volume of traffic in the region may be estimated using interpolation and extrapolation from library data by the trend structure. It is based on a prediction model with a linear function. From this, the distance-relevant procedures are used to conduct the vehicle traffic flow system. To improve forecast accuracy for managing traffic flow and representing the traffic pattern in the trip route, an adaptive model of optimum learning was presented for missing traffic flow predictions. To categorize and forecast the traffic flow from the database for this model, Adaptive Wildfire Optimization (AWO) with the AI method is suggested. It chooses the best features from the database's overall properties to outperform the conventional classification model in making predictions. Spatial Pattern Super Learning (SPSL), a paradigm for enhancing pattern learning, is presented to increase learning accuracy. By comparing the suggested model's overall outcomes with those of other cutting-edge techniques using statistical factors, the findings may be confirmed.