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.
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Planning Bike Lanes based on Sharing-Bikes' Trajectories
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 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 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.
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- Award ID(s):
- 1657350
- PAR ID:
- 10098314
- Date Published:
- Journal Name:
- the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Page Range / eLocation ID:
- 1377 to 1386
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3097983.3098056
