@article{234,
  author = {Tianfu He and Jie Bao and Sijie Ruan and Ruiyuan Li and Yanhua Li and Hui He and Yu Zheng},
  title = {Interactive Bike Lane Planning using Sharing Bikes' Trajectories},
  abstract = {Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task to promote the cycling life style, as well-planned bike lanes can reduce traffic congestions and safety risks. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider one or more key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this paper, we propose a data-driven approach to develop bike lane construction plans based on the large-scale real world bike trajectory data collected from Mobike, a station-less bike sharing system. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. To improve the efficiency of the bike lane planning system for the urban planner, we propose a novel trajectory indexing structure and deploy the system based on a parallel computing framework (Storm) to improve the system’s efficiency. Finally, extensive experiments and case studies are provided to demonstrate the system efficiency and effectiveness.},
  year = {2019},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  chapter = {1},
  pages = {1},
  month = {03},
  issn = {1041-4347},
  url = {https://par.nsf.gov/biblio/10098328},
  doi = {10.1109/TKDE.2019.2907091},
}