Zone pAth Construction (ZAC) based Approaches for Effective Real-Time Ridesharing
Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
JAIR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experimental results, we demonstrate that our myopic approach outperforms the current best myopic approach for ridesharing on both real-world and synthetic datasets (with respect to both objective and runtime). |
| Researcher Affiliation | Academia | Meghna Lowalekar EMAIL School of Information Systems, Singapore Management University, Singapore Pradeep Varakantham EMAIL School of Information Systems, Singapore Management University, Singapore Patrick Jaillet EMAIL Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, USA |
| Pseudocode | Yes | Algorithm 1 ZAC-Online(); Algorithm 2 Generate RPVGraph(t, Poff, D, V, T , Sp); Algorithm 3 Process Offline Partial Paths(t,Poff,D,V, T ,Sp); Algorithm 4 Online Completion(t, P off, R , T , Sp); Algorithm 5 Get Paths For Vehicle(i, qi, R[k], Rp[k], Poff); Table 14: Optimization Formulation for Rebalancing unassigned vehicles |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The first real-world dataset is the publicly available New York Yellow Taxi Dataset (NYYellow Taxi, 2016), henceforth referred to as the NYDataset. ... We also perform experiments on a synthetic dataset introduced by Bertsimas et al. (2018). |
| Dataset Splits | No | The paper mentions evaluating over '1 hour' and '24 hours' and using '15 different weekdays' for evaluation, but it does not specify explicit training, validation, and testing dataset splits by percentages or sample counts to reproduce the experiments. |
| Hardware Specification | Yes | All experiments are run on a 24 core 2.4GHz Intel Xeon E5-2650 processor and 256GB RAM. |
| Software Dependencies | Yes | The algorithms are implemented in Java and optimization models are solved using CPLEX 12.6. |
| Experiment Setup | Yes | Table 11 shows the values of different input parameters considered in the experiments. ... Table 12 shows the different values for the parameters used in the experiments. |