A Comparison of Modelling Approaches for the Long-term Estimation of Origin Destination Matrices in Bike Sharing Systems

Micro-mobility services have gained popularity in the last years, becoming a relevant part of the transportation network in a plethora of cities. This has given rise to a fruitful research area, covering from the impact and relationships of these transportation modes with preexisting ones to the different ways for estimating the demand of such services in order to guarantee the quality of service. Within this domain, docked bike sharing systems constitute an interesting surrogate for understanding the mobility of the whole city, as origin-destination matrices can be obtained straightforward from the information available at the docking stations. This work elaborates on the characterization of such origin-destination matrices, providing an essential set of insights on how to estimate their behavior in the long-term. To do so, the main non-mobility features that affect mobility are studied and used to train different machine learning algorithms to produce viable mobility patterns. The case study performed over real data captured by the bike sharing system of Bilbao (Spain) reveals that, by virtue of a properly selected set of features and the adoption of specialized modeling algorithms, reliable long-term estimations of such origin-destination matrices can be effectively achieved.