Balancing bike sharing systems (BBSS): instance generation from the CitiBike NYC data
Tommaso Urli

TL;DR
This paper presents a method for generating benchmark instances of balancing bike sharing systems using CitiBike NYC data, aiding optimization research in system rebalancing.
Contribution
It introduces a procedure to create realistic BBSS problem instances from real-world CitiBike NYC data, facilitating benchmarking of rebalancing algorithms.
Findings
Generated realistic BBSS instances from CitiBike NYC data
Provides a benchmark for optimization approaches in bike sharing
Enhances research in system rebalancing strategies
Abstract
Bike sharing systems are a very popular means to provide bikes to citizens in a simple and cheap way. The idea is to install bike stations at various points in the city, from which a registered user can easily loan a bike by removing it from a specialized rack. After the ride, the user may return the bike at any station (if there is a free rack). Services of this kind are mainly public or semi-public, often aimed at increasing the attractiveness of non-motorized means of transportation, and are usually free, or almost free, of charge for the users. Depending on their location, bike stations have specific patterns regarding when they are empty or full. For instance, in cities where most jobs are located near the city centre, the commuters cause certain peaks in the morning: the central bike stations are filled, while the stations in the outskirts are emptied. Furthermore, stations…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsUrban Transport and Accessibility · Transportation and Mobility Innovations · Smart Parking Systems Research
