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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Mechanism Design with Predicted Task Revenue for Bike Sharing Systems
Authors: Hongtao Lv, Chaoli Zhang, Zhenzhe Zheng, Tie Luo, Fan Wu, Guihai Chen2144-2151
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using an industrial dataset obtained from a large bike-sharing company, our experiments show that Tru Pre Tar is effective in rebalancing bike supply and demand and, as a result, generates high revenue that outperforms several benchmark mechanisms. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, China 2Department of Computer Science, Missouri University of Science and Technology, USA |
| Pseudocode | Yes | Algorithm 1: Tru Pre Tar: a truthful and budget feasible incentive mechanism with predicted task revenue |
| Open Source Code | No | The paper provides a link to its full version on arXiv but does not explicitly state that source code for the methodology is available or provide a direct link to a code repository. |
| Open Datasets | No | We conduct simulation using a real-world dataset obtained from a large bike-sharing company in China called Mobike. The bike riding data cover 8 8 regions of Beijing with each region being 0.6km 0.6km, and are dated from May 10th to 14th, 2017. |
| Dataset Splits | No | The paper refers to using a real-world dataset for simulations but does not specify explicit training/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | In the experiments, we set the number of users n = 200, and test different location numbers m. The cost of each user ci is drawn from uniform distribution over [0, c] where c = 5. The value of a task is calculated as the difference between the Kullback-Leibler (KL) divergences (Kullback and Leibler 1951) before and after fulfilling the task... The acceptable range h is set as 300m and 600m, respectively. We also test the budget of 50 and 500 where 500 is sufficient while 50 is not. |