A Decentralised Approach to Intersection Traffic Management
Authors: Huan Vu, Samir Aknine, Sarvapali D. Ramchurn
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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]. |