Elevator traffic flow prediction using artificial intelligence

Abstract

Elevator traffic flow prediction is essential part of the modern elevator group control system to enable controller apply the best dispatching strategy based on predicted traffic flow data to achieve optimum operation with the aim to reduce average waiting time of passenger for arrival of elevator to serve them. Generally, elevator traffic flow has high complexity and passenger flow possesses nonlinear feature which is difficult to be expressed by a certain functional style. In this thesis, artificial intelligent technique radial basis function neural network (RBF NN) is used to develop elevator traffic flow prediction model. RBF NN is selected because it is suitable to model nonlinear system and can be trained using fast 2 stages training algorithm assures fast convergence. The past interval traffic flow data and traffic flow data at same time on previous days are used to train RBF NN so that it could predict traffic flow ahead. Neural network toolbox that incorporates newrbe and newrb functions in matlab software is employed to develop algorithm and program of RBF NN. Optimum spread constant that will yield minimum mean square error is obtained and become input to the RBF NN. Ten cases with different k and p are studied to evaluate performance of RBF NN. Given training data collected from field, RBF NN is able to predict elevator up peak traffic flow occur at 8:15 a.m. (in 5 minutes interval) which is short term traffic fairly accurate. Mean square errors from simulation results are small and some of them could be neglected. The maximum mean square error is 2.82 for case that use past 3 interval data on 4th day and past 3 days (1st,2nd and 3rd day) data to predict traffic flow on 5th day executed by using newrb function. It is concluded that RBF NN is an effective artificial intelligent technique to build elevator traffic flow prediction model.