EAGER: Collaborative Research: Towards the Development of Smart Bike Sharing Systems

移动的和基于传感器的技术的最新进展使得收集和处理各种人类移动数据成为可能。当与环境信息和交通信息相结合时，这种人类移动数据可用于开发具有更广泛社会影响的重要应用。 该项目通过集中解决两个数据分析挑战，考虑在城市内构建高效自行车共享系统的背景下出现的技术问题。首先是对不同站点自行车需求量的预测。二是不同站点之间的最优自行车再平衡策略。对自行车需求的成功预测可以帮助系统运营商更好地部署自行车并在站点之间重新分配自行车。有效和最佳的自行车再平衡可以帮助满足自行车租赁的动态需求，并节省系统运营成本。虽然主要集中在自行车共享网络，但通过这个问题改进的数据分析能力应该适用于其他类型的分布式租赁服务的问题。 该探索性研究项目旨在开发有效的、可扩展的数据挖掘和优化技术，这些技术具有分析能力，可以预测不同站点的自行车需求，并优化站点之间的自行车再平衡策略。首先，该项目旨在开发基于回归的预测模型，该模型考虑到相关特征和上下文信息，例如自行车共享站之间的连接。其次，本计画探讨混合整数非线性规划（MINLP）技术，以最小化再平衡车的总行程为目标，求解自行车再平衡问题。虽然传统的MINLP技术不能保证可行的解决方案，研究小组的目标是开发先进的集群技术，首先将站分组为集群，然后使用集群来促进MINLP。该项目还制定了适当的措施，以评估所制定的解决方案的有效性。该项目为研究生和本科生提供基于研究的高级培训机会。该项目产生的所有数据、软件和出版物将向更广泛的研究界公开提供。

Recent advances in mobile and sensor-based techniques have made it possible to collect and process a variety of human mobility data. When combined with environment information and transportation information, such human mobility data can be used to develop important applications with broader societal impacts. This project considers technical problems that arise in the context of building an efficient system for bike sharing within a city by concentrating on two data analytics challenges. The first is the prediction of the demand of bikes at different stations. The second is the optimal bike rebalancing strategy among different stations. The successful prediction of bike demand could help system operators better deploy bikes and redistribute bikes among stations. Effective and optimal bike rebalancing could help meet the dynamic need of bike rental and save system operational costs. Although primarily focusing on bike sharing networks, data analytics capability advanced through this problem should be applicable to problems from other types of distributed rental services. This exploratory research project aims to develop effective and scalable data mining and optimization techniques that have the analytical capability to predict bike demand of different stations and to optimize the bike rebalancing strategy among stations. First, this project aims to develop regression-based prediction models that take into account both relevant features and contextual information such as connections among bike sharing stations. Second, this project explores mixed integer nonlinear programming (MINLP) techniques for solving bike rebalancing problem with the objective of minimizing the total travel distance of rebalancing vehicle. While traditional MINLP techniques could not guarantee feasible solutions, the research team aims to develop advanced clustering techniques to first group stations into clusters and then use the clusters to facilitate MINLP. This project also develops appropriate measures for assessing the effectiveness of the developed solutions. The project offers research based advanced training opportunities for graduate and undergraduate students. All the data, software, and publications resulting from the project will be made publicly available to the broader research community.