(Re)Configuring Bike Station Network via Crowdsourced Information Fusion and Joint Optimization

Thanks to their great success as a green urban transportation means of first/last-mile connectivity, bike sharing service (BSS) networks has been proliferating all over the globe. Their station (re)placement and dock resizing has thus become an increasingly important problem for bike sharing service providers. In contrast to the use of conventional labor-intensive user surveys, we propose a novel optimization framework called CBikes, (re)configuring the BSS network with crowdsourced station suggestions from online websites. Based on comprehensive real data analyses, we identify and utilize important global trip patterns to (re)configure the BSS network while balancing the local biases of individual feedbacks. Specifically, crowdsourced feedbacks, station usage history, cost and other constraints are fused into a joint optimization of BSS network configuration. We further design a semidefinite programming transformation to solve the bike station (re)placement problem efficiently and effectively. Our evaluation has demonstrated the effectiveness and accuracy of CBikes in (re)placing stations and resizing docks based on 3 large BSS systems (with more than 900 stations) in Chicago, Twin Cities, and Los Angeles, as well as related crowdsourced feedbacks.