Journey Data Based Arrival Forecasting for Bicycle Hire Schemes

摘要

The global emergence of city bicycle hire schemes has recently received a lot of attention in the performance and modelling research community. A particularly important challenge is the accurate forecast of future bicycle migration trends, as these assist service providers to ensure availability of bicycles and parking spaces at docking stations, which is vital to match customer expectations. This study looks at how historic information about individual journeys could be used to improve interval arrival forecasts for small groups of docking stations. Specifically, we compare the performance of small area arrival predictions for two types of models, a mean-field analysable time-inhomogeneous population CTMC model (IPCTMC) and a multiple linear regression model with ARIMA error (LRA). The models are validated using historical rush hour journey data from the London Barclays Cycle Hire scheme, which is used to train the models and to test their prediction accuracy.