Neural Networks with Improved Extreme Learning Machine for Demand Prediction of Bike-sharing

Accurate demand prediction of bike-sharing is an important prerequisite to reducing the cost of scheduling and improving the user satisfaction. However, it is a challenging issue due to stochasticity and non-linearity in bike-sharing systems. In this paper, a model called pseudo-double hidden layer feedforward neural networks is proposed to approximately predict actual demands of bike-sharing. Specifically, to overcome limitations in traditional back-propagation learning process, an algorithm, an extreme learning machine with improved particle swarm optimization, is designed to construct learning rules in neural networks. The performance is verified by comparing with other learning algorithms on the dataset of Streeter Dr bike-sharing station in Chicago.

Automated summary

This study proposed a pseudo-double hidden layer feedforward neural network model for predicting actual bike-sharing demands. An improved extreme learning machine algorithm was designed to overcome limitations in traditional back-propagation learning process. The performance was verified by comparing with other learning algorithms on a dataset from Streeter Dr bike-sharing station in Chicago.