دورية أكاديمية
Deep Q-network-based traffic signal control models.
| العنوان: | Deep Q-network-based traffic signal control models. |
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| المؤلفون: | Sangmin Park, Eum Han, Sungho Park, Harim Jeong, Ilsoo Yun |
| المصدر: | PLoS ONE, Vol 16, Iss 9, p e0256405 (2021) |
| بيانات النشر: | Public Library of Science (PLoS), 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1932-6203 |
| Relation: | https://doaj.org/toc/1932-6203 |
| DOI: | 10.1371/journal.pone.0256405 |
| URL الوصول: | https://doaj.org/article/7f9b604ae37e4356b07a8755728d2c30 |
| رقم الأكسشن: | edsdoj.7f9b604ae37e4356b07a8755728d2c30 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19326203 |
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| DOI: | 10.1371/journal.pone.0256405 |