Martinez, Eddy and Buckley, N and Secco, Emanuele Lindo (2022) A PSO-Optimized Fixed and a PSO-Optimized Neural Network-Adaptive Traffic Signal Controllers for Traffic Improvement in Santo Domingo, Dominican Republic. In: 4th International Conference on Communication and Computational Technologies (ICCCT 2022), 26-27 February, 2022, Jaipur, India. (Accepted for Publication)
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Abstract
Satisfying the mobility demand is one of the biggest concerns arising with the increase of urban population. With many people in the road network, traffic congestions are present in most of the cities in the world. The Distrito Nacional in Santo Domingo, capital city of Dominican Republic, is a notorious example of this phenomenon. Unfortunately, all the efforts to improve traffic experience there have had little success.
With this work, two models have been developed using Particle Swarm Optimi-zation (PSO): a PSO-optimized Fixed Traffic Signal Control (PSO-FTSC) and a PSO-optimized Neural Network-Adaptive Traffic Signal Control (PSO-NN-ATSC) that uses 4 Neural Networks to predict phase times.
The intersection of 27 de Febrero Avenue corner with Winston Churchill Ave-nue was simulated using Simulation of Urban Mobility (SUMO), minimizing the Time Loss per vehicle during optimisation.
The models, PSO-FTSC and PSO-NN-ATSC, present reductions of 17% and 24% of Mean Time Loss, respectively.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information and Comments: | This paper has been accepted for publication at the 4th International Conference on Communication and Computational Technologies (ICCCT 2022). |
| Faculty / Department: | Faculty of Human and Digital Sciences > Mathematics and Computer Science |
| Depositing User: | Emanuele Secco |
| Date Deposited: | 21 Jan 2022 09:41 |
| Last Modified: | 01 Mar 2022 01:15 |
| URI: | https://hira.hope.ac.uk/id/eprint/3475 |
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