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 [1].
Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning
Authors: Jiawei Wang, Lijun Sun
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed framework on real-world bus services and actual passenger demand derived from smart card data. Our results show that the proposed model outperforms both traditional headway-based control methods and existing MARL methods. |
| Researcher Affiliation | Academia | Jiawei Wang and Lijun Sun McGill University, Montreal, Canada EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions using 'real-world data' and 'smart card data' from 'Four bus routes (R1-R4) in an anonymous city', but does not provide any specific link, DOI, or citation for public access to this dataset. |
| Dataset Splits | No | The paper mentions training on R1 and testing on R1, R2, R3, R4 for transferability, but does not provide specific percentages or counts for train/validation/test splits within any of the bus routes' data. It states 'training the model on R1 for 250 episodes' but no data splits. |
| Hardware Specification | No | The paper states: 'All models are implemented with python and Py Torch 1.7.0 on Ubuntu 18.04 LTS, and experiments are conducted on a server with 256GB RAM.' This mentions RAM but not specific CPU or GPU models. |
| Software Dependencies | Yes | All models are implemented with python and Py Torch 1.7.0 on Ubuntu 18.04 LTS |
| Experiment Setup | Yes | We set hyper-parameter w = 0.2 in reward function Eq. (2) to place priority on system stability. We start our experiment by training the model on R1 for 250 episodes. In this simulation, the alighting and boarding times per passenger are set to ta = 1.8 s/pax and tb = 3.0 s/pax, respectively. To simulate the uncertainty of road conditions, buses are given a random speed v U(0.6, 1.2) km/h when travelling between every two consecutive stops, where v is set to 30 km/h and U denotes a continuous uniform distribution. The capacity of the bus is set to 120 pax. |