Predicting critical conditions in bicycle sharing systems

Bicycle sharing systems are eco-friendly transportation systems that have found wide application in Smart urban environments. Monitoring and analyzing the occupancy levels of the system's stations is crucial for guaranteeing the quality of the offered service. Advanced data mining solutions tailored to bicycle sharing data analysis are needed to support system managers to react to critical situations (e.g., lack of parked bicycles at a station) that can yield service disruption. This paper presents STation Occupancy Predictor (STOP), a data mining framework to predicting the occupancy levels (i.e., critical or non-critical) of the stations in the near future through Bayesian and associative classifiers. The prediction is made based on the current and past station occupancy values and on the temporal information associated with the predicted time instant. A classification model per station is generated on the collected data to support domain experts in understanding the underlying events. The model allows predicting short-term station occupancy levels and characterizing system usage to support planning maintenance activities in the medium term as well. As a case study, STOP has been thoroughly evaluated on real data acquired from the bicycle sharing system of New York City. The results demonstrate the effectiveness of the STOP system.