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..
A Decentralised Approach to Intersection Traffic Management
Authors: Huan Vu, Samir Aknine, Sarvapali D. Ramchurn
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | More specifically, this paper advances the state of the art in the following ways. First, we propose a novel DCOP formulation of the right-of-way allocation problem. Second, we show how to solve the DCOP approximately using the max-sum algorithm [Farinelli et al., 2008; Macarthur et al., 2011]. Third, we empirically show that our algorithm outperforms the state of the art in terms of reductions in waiting time and robustness to dynamic events. |
| Researcher Affiliation | Academia | Huan Vu1,2, Samir Aknine1 and Sarvapali Ramchurn3 1 Universit e de Lyon, CNRS, Universit e Lyon 1, LIRIS, UMR5205, Lyon 69622, France 2 University of Transport and Communications, Hanoi, Vietnam 3 University of Southampton, Southampton, United Kingdom |
| Pseudocode | No | The paper describes algorithms and uses factor graphs (Figure 3 and 4) but does not provide structured pseudocode blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology. |
| Open Datasets | No | The paper describes a simulation environment for traffic flow and insertion rates rather than using a pre-existing public dataset with explicit training splits. |
| Dataset Splits | No | The paper describes evaluation in terms of insertion rates and dynamic events in a simulated environment, but does not specify traditional train/validation/test dataset splits. |
| Hardware Specification | Yes | All experiments were performed using an Intel Core i5-4690 3.5 GHz, 8 GB RAM, under Ubuntu 16.04. |
| Software Dependencies | No | The paper states: 'Max-sum algorithm is implemented using Frodo [L eaut e et al., 2009].' However, it does not provide a specific version number for Frodo, which is required for reproducibility. |
| Experiment Setup | Yes | All algorithms are evaluated according to the insertion rate of vehicles. The insertion rate varies from 0.1 (off-peak) to 0.5 (rush hour) [Junges and Bazzan, 2008]. |