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A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis
المؤلفون المشاركون
Huang, Ying
Lu, Pan
Zheng, Zijian
Ren, Yihao
Zhou, Xiaoyi
Keramati, Amin
Tolliver, Denver
المصدر
Journal of Advanced Transportation
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-06-19
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Highway-rail grade crossing (HRGC) crashes continue to be the major contributors to rail causalities in the United States and have been intensively researched in the past.
Data-mining models focus on prediction while dominant general linear models focus on model and data fitness.
Decision makers and traffic engineers rely on prediction models to examine at-grade crash frequency and make safety improvement.
The gradient boosting (GB) model has gained popularity in many research areas.
In this study, to fully understand the model performance on HRGC accident prediction performance, the GB model with functional gradient descent algorithm is selected to analyze crashes at highway-rail grade crossings (HRGCs) and to identify contributor factors.
Moreover, contributors’ importance and partial-dependent relations are generated to further understand the relationship of identified contributors and HRGC crash likelihood to concur “black box” issues that most machine learning methods face.
Furthermore, to fully demonstrate the model’s prediction performance, a comprehensive model prediction power assessment based on six measurements is conducted, and the prediction performance of the GB model is verified and compared with a decision tree model as a reference due to their popularity and comparable data availability.
It is demonstrated that the GB model produces better prediction accuracy and reveals nonlinear relationships among contributors and crash likelihood.
In general, HRGC crash likelihood is significantly impacted by several traffic exposure factors: highway traffic volume, railway traffic volume, and train travel speed and others.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lu, Pan& Zheng, Zijian& Ren, Yihao& Zhou, Xiaoyi& Keramati, Amin& Tolliver, Denver…[et al.]. 2020. A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1175930
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lu, Pan…[et al.]. A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis. Journal of Advanced Transportation No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1175930
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lu, Pan& Zheng, Zijian& Ren, Yihao& Zhou, Xiaoyi& Keramati, Amin& Tolliver, Denver…[et al.]. A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1175930
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1175930
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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