Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Incentivizing Users for Balancing Bike Sharing Systems
Authors: Adish Singla, Marco Santoni, Gábor Bartók, Pratik Mukerji, Moritz Meenen, Andreas Krause
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the incentive compatibility of our mechanism and extensively evaluate it through simulations based on data collected via a survey study. Finally, we deployed the proposed system through a smartphone app among users of a large scale bike sharing system operated by a public transport company, and we provide results from this experimental deployment. |
| Researcher Affiliation | Collaboration | Adish Singla ETH Zurich EMAIL Marco Santoni Electric Feel Mobility Systems EMAIL G abor Bart ok ETH Zurich EMAIL Pratik Mukerji Electric Feel Mobility Systems EMAIL Moritz Meenen Electric Feel Mobility Systems EMAIL Andreas Krause ETH Zurich EMAIL |
| Pseudocode | Yes | Procedure 1: Incentives Deployment Schema (IDS) and Procedure 2: Pricing Mechanism DBP-UCB |
| Open Source Code | No | The paper describes the design and deployment of a system and a smartphone app, but it does not contain any explicit statement about releasing the source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | For our simulations, we used a historical dataset from Boston s Hubway, made publicly available by the Boston Metropolitan Area Planning Council2. The Hubway dataset contains data collected between 28th July, 2011 and 1st October, 2012 with rich information about 95 stations, 694 bicycles, 552, 030 rentals, and snapshots of the status of the BSS at regular intervals. |
| Dataset Splits | No | The paper describes using historical data for simulations and a survey for validation of assumptions, but it does not specify explicit training, validation, or test dataset splits for model training or evaluation. |
| Hardware Specification | No | The paper mentions deploying the system via a smartphone app and integrating with BSS infrastructure, but it does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments or simulations. |
| Software Dependencies | No | The paper mentions the use of a smartphone app and APIs for integration, along with proprietary systems from the bike sharing company, but it does not list any specific software dependencies or their version numbers necessary for replication. |
| Experiment Setup | Yes | At the onset of each new time batch h, the mechanism is provided with an additional budget B(h) by the BSS operator. Furthermore, the number of participants N for a batch is approximated by the expected number of trips ˆz(h) taking place in the corresponding batch h estimated by the BSS forecaster. ... Each simulation starts by taking a snapshot of the status of the BSS... and runs the simulation for a total of 30 days. ... TRUCKS, we used the myopic greedy policy as defined by Chemla, Meunier, and Pradeau (2013). Using the idea of dynamic (during rush hours) and static (during off hours) repositioning from Raviv and Kolka (2013), our policies operate in time from 12:00 p.m. and 3:00 p.m, and then from mid-night to morning. We assume that the trucks entail a fixed cost of 50 e per hour to the system and the policy allocates the number of hours based on the total budget. |