|PI||Professor Hui Xiong, Rutgers, the State University of New Jersey|
|Duration||August 1, 2016 - July 31, 2018 (Estimated)|
|Title||EAGER: Collaborative Research: Towards the Development of Smart Bike Sharing Systems|
|NSF Program(s)||INFO INTEGRATION & INFORMATICS|
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.