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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchically and Cooperatively Learning Traffic Signal Control
Authors: Bingyu Xu, Yaowei Wang, Zhaozhi Wang, Huizhu Jia, Zongqing Lu669-677
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate that Hi Light outperforms state-of-the-art RL methods for traffic signal control in real road networks with real traffic. |
| Researcher Affiliation | Collaboration | Bingyu Xu1, Yaowei Wang1, Zhaozhi Wang2, Huizhu Jia2, Zongqing Lu2 1Peng Cheng Laboratory 2Peking University |
| Pseudocode | Yes | Algorithm 1 Hi Light training |
| Open Source Code | No | The paper mentions using 'City Flow (Zhang et al. 2019), an open-source simulator' and using 'open-source implementations' for baselines, but does not provide a link or state that the authors are releasing the source code for their own method, Hi Light. |
| Open Datasets | Yes | The road networks and traffic flows of Jinan, Hangzhou, and New York City are the public datasets1. 1https://traffic-signal-control.github.io/ |
| Dataset Splits | No | The paper mentions training and evaluating performance, but does not explicitly provide specific train/validation/test dataset splits, percentages, or sample counts needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or cloud computing instance specifications. |
| Software Dependencies | No | The paper mentions using City Flow simulator, and algorithms like DQN and PPO, but does not provide specific version numbers for any software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | Hyperparameters Table 1 summarizes the hyperparameters of Hi Light, Co Light, and Press Light. For Co Light and Press Light, we use their open-source implementations. For fair comparison, we also use their default parameter settings which perform better than other settings as verified by experiments. |