Demand cycles and market segmentation in bicycle sharing

Abstract

Consumers often display unique habitual behaviors, and knowledge of these behaviors is of great value in prediction of future demand. We investigated consumer behavior in bicycle sharing in Beijing, where demand prediction is critical for cost-effective rebalancing of bicycle locations (putting bikes where and when they will be rented) and supply (number of bicycles). We created baseline statistical demand models, borrowing methods from economics, signal processing and animal tracking to find consumption cycles of 7, 12, 24 h and 7-days. Lorenz curves of bicycle demand revealed significant stratification of consumer behavior and a long-tail of infrequent demand. To overcome the limits of traditional statistical models, we developed a deep-learning model to incorporate (1) weather and air quality, (2) time-series of demand, and (3) geographical location of demand. Customer segmentation was added at a later stage, to explore potential for improvement with customer demographics. Our final machine learning model with tuned hyperparameters yielded around 50% improvement in predictions over a discrete wavelet transform model, and 80–90% improvement in predictions over a naïve model the reflects some current industry practice. We assessed causality in the deep-learning model, finding that location and air quality had the strongest causal impact on demand. The extreme market segmentation of customer demand, and our relatively short time span of data combined to make it difficult to find sufficient data on all customers for a model fit based on segmentation. We reduced our model data to only the 10 most frequent to see whether such segmentation improves our model's predictive success. These results, though limited, suggest that customer behavior within market segments is more stable than across all customers, as was expected.