Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand
المؤلفون المشاركون
Yang, Yongjian
Wang, Xintao
Xu, Yuanbo
Huang, Qiuyang
المصدر
Journal of Advanced Transportation
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-19
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their operational experience or cruise randomly to find passengers.
Without macroguidance, the role of the taxi system cannot be fully utilized.
Many scholars have studied taxi behaviors to find better operational strategies for drivers, but their researches rely on local optimization methods to improve the profit of drivers, which will lead to imbalance between supply and demand in the city.
To solve this problem, we propose a Multiagent Reinforcement Learning- (MARL-) based taxi predispatching model through analyzing the running data of 13,000 taxis.
Different from other methods of scheduling taxis based on the real-time location of orders, our model first predicts the demand for taxis in different regions in the next period and then dispatches taxis in advance to meet the future requirement; thus, the number of taxis needed and available in different regions can be balanced.
Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning.
Finally, we compare our method with another taxi dispatching method, and the results show that the proposed method has a significant improvement in vehicle utilization rate and passenger demand satisfaction rate.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yang, Yongjian& Wang, Xintao& Xu, Yuanbo& Huang, Qiuyang. 2020. Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1176140
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yang, Yongjian…[et al.]. Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand. Journal of Advanced Transportation No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1176140
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yang, Yongjian& Wang, Xintao& Xu, Yuanbo& Huang, Qiuyang. Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1176140
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1176140
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