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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Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories
Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. Traditional approaches to detect illegal parking events rely highly on active human efforts. However, these approaches are extremely ineffective to cover a large city. The massive and high quality sharing bike trajectories from Mo- bike offer us with a unique opportunity to design a ubiquitous illegal parking detection system, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. Two main components are employed in the proposed illegal park- ing detection system: 1) trajectory pre-processing, which filters outlier GPS points, performs map-matching and builds trajectory indexes; and 2) illegal parking detection, which models the normal trajectories, extracts features from the evaluation trajectories and utilizes a distribution test-based method to discover the illegal parking events. The system is deployed on the cloud, and used by Mo- bike internally. Finally, extensive experiments and many insightful case studies are presented.
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- Award ID(s):
- 1657350
- PAR ID:
- 10098317
- Date Published:
- Journal Name:
- the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Page Range / eLocation ID:
- 340 to 349
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3219819.3219887
