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基于共享单车轨迹规划自行车道

Planning Bike Lanes based on SharingBikes' Trajectories
课程网址: http://videolectures.net/kdd2017_bao_bike_lanes/  
主讲教师: Jie Bao
开课单位: 微软研究院
开课时间: 2017-10-09
课程语种: 英语
中文简介:
自行车作为一种绿色交通方式已经被世界上许多国家的政府所推广。因此,建设有效的自行车道已成为政府推广自行车生活方式的一项重要任务,因为精心规划的自行车道可以减少交通拥堵,降低骑自行车者和机动车驾驶员的安全风险。不幸的是,现有的自行车道规划的轨迹挖掘方法没有考虑到关键的现实政府约束:1)预算限制,2)建设便利性,以及3)自行车道利用率。 在本文中,我们提出了一种基于大规模真实世界自行车轨迹数据的数据驱动方法来制定自行车道建设计划。我们执行这些约束来制定我们的问题,并引入一个灵活的目标函数来调整用户数量的覆盖范围和他们的轨迹长度之间的利益。我们证明了这个问题的np -硬度,并提出了基于贪婪的启发式方法来解决它。最后,我们在Microsoft Azure上部署了我们的系统,提供了大量的实验和案例研究来证明我们方法的有效性。
课程简介: 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 for governments promoting the cycling life style, as well-planned bike paths can reduce traffic congestion and decrease safety risks for both cyclists and motor vehicle drivers. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider key realistic government constraints: 1) budget imitations, 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 large-scale real world bike trajectory data. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of the number of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. Finally, we deploy our system on Microsoft Azure, providing extensive experiments and case studies to demonstrate the effectiveness of our approach.
关 键 词: 共享单车; 轨迹规划; 数据驱动
课程来源: 视频讲座网
数据采集: 2023-04-24:chenxin01
最后编审: 2023-05-18:chenxin01
阅读次数: 18