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

Suining He, K. Shin
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引用次数: 12

Abstract

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.
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(二)众包信息融合与联合优化配置自行车站网络
自行车共享服务(BSS)网络作为一种绿色城市交通工具取得了巨大成功,在全球范围内蓬勃发展。因此,他们的站点(重新)安置和码头大小调整已成为共享单车服务提供商日益重要的问题。与使用传统的劳动密集型用户调查相比,我们提出了一种新的优化框架,称为CBikes,(重新)配置BSS网络与来自在线网站的众包站点建议。基于全面的真实数据分析,我们识别并利用重要的全局出行模式来(重新)配置BSS网络,同时平衡个体反馈的局部偏差。具体而言,将众包反馈、车站使用历史、成本等约束因素融合为BSS网络配置的联合优化。在此基础上,设计了一种半定规划变换,有效地解决了自行车站(再)安置问题。基于芝加哥、双子城和洛杉矶的3个大型BSS系统(超过900个站点)以及相关的众包反馈,我们的评估证明了自行车在(重新)放置站点和调整码头大小方面的有效性和准确性。
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