Sequential Decision Making for Improving Efficiency in Urban Environments
Authors: Pradeep Varakantham
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on two large real world taxi data sets in comparison to the standard greedy (myopic) approach typically employed in taxi applications (ex: Uber, Ola, Lyft, Grab etc.). |
| Researcher Affiliation | Academia | Pradeep Varakantham School of Information Systems, Singapore Management University pradeepv@smu.edu.sg |
| Pseudocode | No | The paper describes methods textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about open-sourcing the code for the described methodology or links to code repositories. |
| Open Datasets | No | The paper mentions using 'large real world data sets' and refers to several datasets like 'large real world taxi data sets' and 'real world emergency response data sets from asian cities' but does not provide specific access information, DOI, or a formal public dataset citation for these datasets. |
| Dataset Splits | No | The paper mentions using 'large real world data sets' and 'real world emergency response data sets' but does not provide specific details on how the data was split into training, validation, and testing sets, or the percentages/counts for each split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper describes methods and approaches (e.g., 'Stochastic Optimization with Sample Average Approximation', 'Fictitious play approach') but does not list any specific software or library names with version numbers used for implementation. |
| Experiment Setup | No | The paper describes the general solution methods but does not provide specific experimental setup details such as hyperparameters, learning rates, batch sizes, or other training configurations. |