Spatiotemporal Travel Patterns and Demand Prediction of Shared Bikes in Beijing

Using the Mobike data on workdays in Beijing, this study explores the patterns of bike-sharing behavior through the non-negative matrix factorization(NMF) algorithm from the spatial and temporal dimensions. A reverse population stability index was proposed to improve the selection of the k-value. Then, based on the revealed travel patterns information, the BP neural network prediction model with a non-negative matrix factorization (NMF) algorithm was built by MATLAB to predict the travel demand of shared bikes. The prediction results were compared with other two prediction models, i.e., the BP neural network model without NMF and the long and short term memory (LSTM) neural network model. Results show that the share of bicycle travel can be divided into five basic travel patterns, and the travel demand in each area can be represented by a linear combination of these five travel patterns. The coefficients represent the intensity and temporal fluctuation of each travel pattern. Based on the spatial and temporal characteristics, the five travel patterns can be regarded as cycling from residences to subway stations in commuting travel, cycling from subway stations to workplaces in commuting travel, non-commuting travel such as shopping or recreational travel, cycling from workplaces to subway stations in commuting travel and cycling from subway stations to residences in commuting travel. The results also show that the NMF-based BP neural network model proposed in this study is superior to the other two prediction models in both prediction accuracy and practical operational convenience. Copyright © 2022 by Science Press.