Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning
المؤلفون المشاركون
Ochieng, Washington Y.
Shang, Wen-Long
Li, Xingang
Chen, Yanyan
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-19، 19ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-21
دولة النشر
مصر
عدد الصفحات
19
التخصصات الرئيسية
الملخص EN
Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management.
However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient.
This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve the network performance by adjusting red/green time split.
In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions.
In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split.
A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one.
The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions.
This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Shang, Wen-Long& Chen, Yanyan& Li, Xingang& Ochieng, Washington Y.. 2020. Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning. Complexity،Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1144805
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Shang, Wen-Long…[et al.]. Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning. Complexity No. 2020 (2020), pp.1-19.
https://search.emarefa.net/detail/BIM-1144805
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Shang, Wen-Long& Chen, Yanyan& Li, Xingang& Ochieng, Washington Y.. Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning. Complexity. 2020. Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1144805
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1144805
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