LEARNING METHODOLOGY FOR IMPROVING TRAFFIC PREDICTION ACCURACY OF ELEVATOR SYSTEMS USING "ARTIFICIAL INTELLIGENCE

A computer controlled elevator system (FIG. 1 ) using prediction methodology to enhance the system's elevator service, having "learning" capabilities to adapt the system to changing building operational characteristics, including signal processing means for computing the "best" prediction model to be used for prediction, the best factoring coefficients for combining real time and historic predictors associated with the best prediction model, the best data and prediction time interval lengths to be used, and the optimal number of look-ahead intervals or steps (for real time predictions) or look-back days (for historic predictions) to the extent applicable to the prediction model, etc. Using the algorithm(s) of the invention the best prediction methodology and associated parameters are selected by running on site simulations based on exemplary values and comparing the prediction results to recorded data indicative of the actual events that have occurred in the system over a past appropriate period of time. That which provides the most accurate predictions, i.e., those with a minimum error as determined by appropriate mathematical models (e.g., sum of the square of the prediction error or sum of absolute error), are thereafter used in the prediction methodology of the system until further evaluations indicate that further changes should be made.