Graph-Based Optimisation of Network Expansion in a Dockless Bike Sharing System
This work addresses operational efficiency for bike-sharing system operators, but it is incremental as it applies existing graph and community detection methods to new data.
The study tackled optimizing network expansion in a dockless bike-sharing system by constructing an optimized geo-temporal graph from trip data, revealing prime locations for new stations and self-contained sub-networks with similar usage patterns at different temporal granularities. It reinforced that bike-sharing systems are intrinsically spatiotemporal, with findings potentially aiding operators in improving redistribution efficiency.
Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. BSSs alleviate congestion, reduce pollution and promote physical exercise. It is essential to explore the spatiotemporal patterns of bike-sharing demand, as well as the factors that influence these patterns, in order to optimise system operational efficiency. In this study, an optimised geo-temporal graph is constructed using trip data from Moby Bikes, a dockless BSS operator. The process of optimising the graph unveiled prime locations for erecting new stations during future expansions of the BSS. The Louvain algorithm, a community detection technique, is employed to uncover usage patterns at different levels of temporal granularity. The community detection results reveal largely self-contained sub-networks that exhibit similar usage patterns at their respective levels of temporal granularity. Overall, this study reinforces that BSSs are intrinsically spatiotemporal systems, with community presence driven by spatiotemporal dynamics. These findings may aid operators in improving redistribution efficiency.