Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems
Authors: Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Experiments: Learning to rebalance a Shared Mobility System. In this section, we evaluate our approach in a realistic case study of rebalancing the SMS of Louisville, KY, based on historical trip data. |
| Researcher Affiliation | Academia | 1ETH Zurich, Switzerland. |
| Pseudocode | Yes | Algorithm 1 Example of NO-REGRET algorithm class, with update rule of MWU (Freund & Schapire, 1997) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for the described methodology. |
| Open Datasets | Yes | Data from Louisville Advanced Planning Office (2020) include trips timestamps, starting and end coordinates of the dockless SMS of the city of Louisville, KY, for the year of 2019. |
| Dataset Splits | No | The paper uses data from 2019 to simulate user demand but does not specify any explicit training, validation, or test splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers with their versions) that would be needed for replication. |
| Experiment Setup | Yes | We consider N = 5 trucks, each dropping off 8 vehicles to one of the candidate regions... We let context zt = [zt[1], zt[2], zt[3]] 2 R3 represent average daily temperature, precipitation, and the users demand in day t... We use a composite kernel k(xt, zt) = k1( xt, zt[3]) k2(zt[1], zt[2]), where xt = PN i=1 xi + represents the total number of vehicles positioned in each region, k1 is a polynomial kernel of degree 3... and k2 is a squared-exponential kernel... We use two distinct models, depending on day t being a weekday or a weekend. Kernel hyperparameters are optimized offline over 100 random datapoints using a maximum likelihood method and kept fixed for the whole experiment duration. |