Journal ArticleInteractive Bike Lane Planning using Sharing Bikes' TrajectoriesHe, Tianfu; Bao, Jie; Ruan, Sijie; Li, Ruiyuan; Li, Yanhua; He, Hui; Zheng, YuCycling 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.2019-03-2510098328IEEE Transactions on Knowledge and Data Engineering1 to 11041-4347https://doi.org/10.1109/TKDE.2019.29070911657350; 1831140National Science Foundation