An Ensemble Learning Model for Short-Term Passenger Flow Prediction
المؤلفون المشاركون
Huang, Lei
Huang, Haifeng
Wang, Xiangping
Li, Baoyu
Xia, Ziyang
Li, Jing
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-19
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather.
If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow.
This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning.
The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression (MLR), K-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGBoost), and Gated Recurrent Unit (GRU) as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020.
Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model.
The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Xiangping& Huang, Lei& Huang, Haifeng& Li, Baoyu& Xia, Ziyang& Li, Jing. 2020. An Ensemble Learning Model for Short-Term Passenger Flow Prediction. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1143278
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Xiangping…[et al.]. An Ensemble Learning Model for Short-Term Passenger Flow Prediction. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1143278
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Xiangping& Huang, Lei& Huang, Haifeng& Li, Baoyu& Xia, Ziyang& Li, Jing. An Ensemble Learning Model for Short-Term Passenger Flow Prediction. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1143278
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1143278
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