Public bike-sharing systems (BSS) are booming all around the world, providing a flexible trip mode for people in cities. However, the imbalance usage among stations is causing problems for both system operators and users. As a first step to address this challenge, a recent paper [1] has proposed a spatio-temporal bicycle mobility model based on historical bike-sharing data, and devised a traffic prediction mechanism on a per-station basis with sub-hour granularity. To better demonstrate the mobility model and prediction, we develop this data analysis and visualization system, not only presenting massive real-time prediction results but also serving as a platform for human mobility analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"
Public bike-sharing systems (BSS) are booming all around the world, providing a flexible trip mode for people in cities. However, the imbalance usage among stations is causing problems for both system operators and users. As a first step to address this challenge, a recent paper [1] has proposed a spatio-temporal bicycle mobility model based on […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"ACM International Conference on Mobile Systems, Applications, and Services (MobiSys)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"ACM International Conference on Mobile Systems, Applications, and Services 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