论文标题
在未来的不确定性下最小化车队的规模并改善自行车共享的自行车分配
Minimizing Fleet Size and Improving Bike Allocation of Bike Sharing under Future Uncertainty
论文作者
论文摘要
作为一项迅速扩大的服务，自行车共享正面临着过度供应的严重问题，并且在许多中国城市中需求波动。这项研究开发了一种大规模的方法，可以根据自行车共享南京旅行的数据，以确定不确定性下的最小车队规模。发现在不完整的信息方案下，最小化车队规模的算法有效地处理未来的不确定性。对于无码头自行车共享系统，供应原始车队的14.5％可以满足旅行需求的96.8％。同时，结果表明，提供一个集成多家公司的集成服务平台可以将总舰队的规模显着降低44.6％。此外，鉴于Covid-19的大流行，这项研究提出了一项社会疏远政策，以保持合适的用法间隔。这些发现为提高自行车共享和共享移动性的资源效率和运营服务提供了有用的见解。
As a rapidly expanding service, bike sharing is facing severe problems of bike over-supply and demand fluctuation in many Chinese cities. This study develops a large-scale method to determine the minimum fleet size under uncertainty, based on the bike sharing data of millions of trips in Nanjing. It is found that the algorithm of minimizing fleet size under the incomplete-information scenario is effective in handling future uncertainty. For a dockless bike sharing system, supplying 14.5% of the original fleet could meet 96.8% of trip demands. Meanwhile, the results suggest that providing a integrated service platform that integrates multiple companies can significantly reduce the total fleet size by 44.6%. Moreover, in view of the COVID-19 pandemic, this study proposes a social distancing policy that maintains a suitable usage interval. These findings provide useful insights for improving the resource efficiency and operational service of bike sharing and shared mobility.
