Urban Mobility Modeling: Application to Seoul Bike-Sharing Data

This study applies a model from the normal variance–mean mixture family to capture daily demand in urban bike sharing. We fit both a mixture-based model and a standard Gaussian model to the logarithmic returns of total daily rental counts from the Seoul Bike-Sharing Demand dataset. Parameter estimation is performed, and model performance is assessed using mean squared error (MSE). Using one year of hourly rental data aggregated to daily counts from the Seoul Bike dataset, we find that the mixture-based model substantially outperforms the Gaussian counterpart, achieving a lower MSE. These results suggest that models from the normal variance–mean mixture family are more effective at capturing the large fluctuations and outliers inherent in bike-sharing demand data compared to models assuming normally distributed returns.