Data Challenge 2016

Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/14088

The first edition of the MSU Data Challenge! This event was organized by the Data Infrastructure & Scholarly Communication group, a joint effort from the University Information Technology and the Library. In 2016 the topic of the data challenge was bike parking on campus, in collaboration with the Office of Sustainability. Participants were invited to crunch data on bike racks location and capacity; building location and occupancy; and sidewalk locations to suggest how to best redeploy (or expand on) the existing racks to best serve the campus biking community. All these datasets were provided to participants. The proposals were evaluated by stakeholders from around campus. The best proposals used a combination of data visualization and/or algorithmic approaches to suggest how to optimize bike parking with respect to walking distance to classrooms, aesthetic of the parking spaces, access to snowplows, and feasibility.

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Author: search.filters.author.Smith, Killiansearch.filters.applied.f.department: search.filters.department.Computer Science.

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    Optimizing Cyclist Parking in a Closed System
    (Montana State University, 2016-10) Qingge, Letu; Smith, Killian
    In this paper, we consider the two different aspects of the bike parking problem; namely the assignment of bike racks to locations, and the selection of the minimal number of bike rack locations satisfying some maximum walking distanced. The first sub-problem considered was the assignment of bike racks to individual buildings in the attempt to satisfy the needs of the total number of cyclists expected to reside within a building during the course of an average day. We show that the case of assigning a finite number of bike racks to all buildings on a campus is NP-Hard, and propose a greedy algorithm to obtain a solution. The case of allowing for additional bike racks to be purchased is shown to bePolynomial-Time solvable. The second sub-problem, finding the minimal number of bike rack locations, is shown to be NP-Hard, and a method to use approximation algorithms for the Maximum Independent Set to find solutions is demonstrated.
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