An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm
المؤلفون المشاركون
Gan, Jing
Li, Linheng
Zhang, Dapeng
Yi, Ziwei
Xiang, Qiaojun
المصدر
Journal of Advanced Transportation
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-21
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety.
A huge variety of forecasting models have been proposed to meet this challenge.
These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models.
Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University.
This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms.
Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm.
To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume.
It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Gan, Jing& Li, Linheng& Zhang, Dapeng& Yi, Ziwei& Xiang, Qiaojun. 2020. An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175296
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Gan, Jing…[et al.]. An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm. Journal of Advanced Transportation No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1175296
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Gan, Jing& Li, Linheng& Zhang, Dapeng& Yi, Ziwei& Xiang, Qiaojun. An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175296
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1175296
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