PURPOSE: To obtain a new approach for traffic modelling by modelling behaviors of people who use a building by means of a neural network technique. CONSTITUTION: Traffic data is transferred from car objects 9 to a traffic data storing object 10. Modules M1 , M2 , M3 to be executed as neural networks NN1 , NN2 , NN3 respectively correspond to three traffic models. Copies TM1c, TM3c of the traffic models for predicting a future phenomenon with respect to previous observation are trained by means of a pair of input and output patterns for one-day traffic behavior. The input pattern is a binary version encoded with respect to time, while the output pattern is an arrival rate or a car calling establishment distribution for each floor. The arrival rates from the modules M1 , M2 are combined to each other by a combination circuit 11, and a real arrival rate model adjusts prediction based on the recent phenomena over the latest 10 minutes.
SYSTEM FOR MODELING AND PREDICTION OF ARTIFICIAL INTELLIGENCE TRAFFIC