EAGER: Collaborative Research: Towards the Development of Smart Bike Sharing Systems
Contact Information
| PI | Professor Hui Xiong, Rutgers, the State University of New Jersey |
| Tel | 1-973-353-5261 |
| Fax | 1-973-353-5003 |
| hxiong@rutgers.edu |
Award Information
| Award Number | 1648664 |
| 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 |
Project Abstract
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.
Publications
- Qi Liu, Biao Xiang, Nicholas Jing Yuan, Enhong Chen, Hui Xiong, Yi Zheng, Yu Yang, An Influence Propagation View of PageRank, ACM Transactions on Knowledge Discovery from Data (TKDD), to Appear, 2017.
- Chuanren Liu, Hui Xiong, Spiros Papadimitriou, Yong Ge, Keli Xiao, A Proactive Workflow Model for Healthcare Operation and Management, IEEE Transactions on Knowledge and Data Engineering (TKDE), to Appear, 2017.
- Yanchi Liu, Chuanren Liu, Xinjiang Lu, Mingfei Teng, Hengshu Zhu, Hui Xiong. Point-of_Interest Demand Modeling with Human Mobility Patterns. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017).
- Junming Liu, Leilei Sun, Qiao Li, Jingci Ming, Yanchi Liu, Hui Xiong. Functional Zone Based Hierarchical Demand Prediction for Bike System Expansion. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017).
- Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong. REMIX: Automated Exploration for Interactive Outlier Detection. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017).
- Zijun Yao, Yanjie Fu, Bin Liu, Yanchi Liu, Hui Xiong. Mining Personal Context-Aware Preferences for Mobile Users. In Proceedings of the 16th IEEE Conference on Data Mining (ICDM 2016), to appear, 2016.