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An Improved Deep Learning Model for Traffic Crash Prediction
المؤلفون المشاركون
Dong, Chunjiao
Shao, Chunfu
Li, Juan
Xiong, Zhihua
المصدر
Journal of Advanced Transportation
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-12-10
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Machine-learning technology powers many aspects of modern society.
Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction.
In this study, an improved deep learning model is proposed to explore the complex interactions among roadways, traffic, environmental elements, and traffic crashes.
The proposed model includes two modules, an unsupervised feature learning module to identify functional network between the explanatory variables and the feature representations and a supervised fine tuning module to perform traffic crash prediction.
To address the unobserved heterogeneity issues in the traffic crash prediction, a multivariate negative binomial (MVNB) model is embedding into the supervised fine tuning module as a regression layer.
The proposed model was applied to the dataset that was collected from Knox County in Tennessee to validate the performances.
The results indicate that the feature learning module identifies relational information between the explanatory variables and the feature representations, which reduces the dimensionality of the input and preserves the original information.
The proposed model that includes the MVNB regression layer in the supervised fine tuning module can better account for differential distribution patterns in traffic crashes across injury severities and provides superior traffic crash predictions.
The findings suggest that the proposed model is a superior alternative for traffic crash predictions and the average accuracy of the prediction that was measured by RMSD can be improved by 84.58% and 158.27% compared to the deep learning model without the regression layer and the SVM model, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Dong, Chunjiao& Shao, Chunfu& Li, Juan& Xiong, Zhihua. 2018. An Improved Deep Learning Model for Traffic Crash Prediction. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1181199
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Dong, Chunjiao…[et al.]. An Improved Deep Learning Model for Traffic Crash Prediction. Journal of Advanced Transportation No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1181199
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Dong, Chunjiao& Shao, Chunfu& Li, Juan& Xiong, Zhihua. An Improved Deep Learning Model for Traffic Crash Prediction. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1181199
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1181199
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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