Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning
المؤلفون المشاركون
Xu, Ming
Li, Duowei
Wu, Jianping
Wang, Ziheng
Hu, Kezhen
المصدر
Journal of Advanced Transportation
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-03
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time.
However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem.
To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning.
The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions.
The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model.
To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases.
The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network.
By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases.
The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Duowei& Wu, Jianping& Xu, Ming& Wang, Ziheng& Hu, Kezhen. 2020. Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1175881
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Duowei…[et al.]. Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning. Journal of Advanced Transportation No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1175881
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Duowei& Wu, Jianping& Xu, Ming& Wang, Ziheng& Hu, Kezhen. Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1175881
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1175881
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر