![](/images/graphics-bg.png)
A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis
المؤلفون المشاركون
Shang, Qiang
Tan, Derong
Gao, Song
Feng, Linlin
المصدر
Journal of Advanced Transportation
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-01-20
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Predicting traffic incident duration is important for effective and real-time traffic incident management (TIM), which helps to minimize traffic congestion, environmental pollution, and secondary incident related to this incident.
Traffic incident duration prediction methods often use more input variables to obtain better prediction results.
However, the problems that available variables are limited at the beginning of an incident and how to select significant variables are ignored to some extent.
In this paper, a novel prediction method named NCA-BOA-RF is proposed using the Neighborhood Components Analysis (NCA) and the Bayesian Optimization Algorithm (BOA)-optimized Random Forest (RF) model.
Firstly, the NCA is applied to select feature variables for traffic incident duration.
Then, RF model is trained based on the training set constructed using feature variables, and the BOA is employed to optimize the RF parameters.
Finally, confusion matrix is introduced to measure the optimized RF model performance and compare with other methods.
In addition, the performance is also tested in the absence of some feature variables.
The results demonstrate that the proposed method not only has high accuracy, but also exhibits excellent reliability and robustness.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Shang, Qiang& Tan, Derong& Gao, Song& Feng, Linlin. 2019. A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1169866
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Shang, Qiang…[et al.]. A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis. Journal of Advanced Transportation No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1169866
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Shang, Qiang& Tan, Derong& Gao, Song& Feng, Linlin. A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1169866
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1169866
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
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
![](/images/kashef-image.png)