Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling
Authors: Naveen Raman, Sanket Shah, John Dickerson
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our methods, we utilize the New York City taxi data set [New York City, 2016]... We run experiments comparing the different objective functions on both profitability and fairness metrics. |
| Researcher Affiliation | Academia | Naveen Raman1 , Sanket Shah2 , John P. Dickerson1 1University of Maryland 2Harvard University nraman1@umd.edu, sanketshah@g.harvard.edu, john@cs.umd.edu |
| Pseudocode | No | The paper describes algorithmic steps but does not include structured pseudocode or an algorithm block. |
| Open Source Code | Yes | 1Our code and data is publicly available at https://github.com/naveenr414/ijcai-rideshare |
| Open Datasets | Yes | To evaluate our methods, we utilize the New York City taxi data set [New York City, 2016] |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper indicates the use of deep learning and a neural network but does not provide specific software names with version numbers for reproducibility (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x). |
| Experiment Setup | No | The paper has a section titled 'Experiment Setup' (4.4), but it defers the concrete hyperparameter values and detailed value function training specifics to 'Appendix B', which is not part of the main text provided. |