Information Fusion for (Re)Configuring Bike Station Networks With Crowdsourcing

Bike sharing service (BSS) networks have been proliferating all over the globe thanks to their success as the first/last-mile connectivity inside a smart city. Their (re)configuration — i.e., station (re)placement and dock resizing — has thus become increasingly important for BSS providers and smart city planners. Instead of using conventional labor-intensive manual surveys, we propose a novel information fusion framework called CBikes that (re)configures the BSS network by jointly fusing crowdsourced station suggestions from online websites and the usage history of bike stations. Using comprehensive real data analyses, we identify and exploit important global trip patterns to (re)configure the BSS network while mitigating the local biases of individual feedbacks. Specifically, crowdsourced feedbacks, station usage, cost and other constraints are fused into a joint optimization of BSS network configuration. We also model the spatial distributions of station usage to account for and estimate the unexplored regions without historical usage information. We further design a semidefinite programming transformation to solve the bike station (re)placement problem efficiently and effectively. Our extensive data analytics and evaluation have shown CBikes’ effectiveness and accuracy in (re)placing stations and resizing docks based on three large BSS systems (with $>$ 900 stations) in Chicago, Twin Cities (Minneapolis–Saint Paul), and Los Angeles.