Version 1
: Received: 24 January 2020 / Approved: 15 April 2020 / Online: 15 April 2020 (14:13:35 CEST)
How to cite:
Yang, Q.; Fang, Y.; Zheng, L.; Zhou, X.; Peng, B. An Expressway Traffic Incident Detection Method Based on Convolutional Neural Network and Extreme Gradient Boosting. Preprints2020, 2020040248
Yang, Q.; Fang, Y.; Zheng, L.; Zhou, X.; Peng, B. An Expressway Traffic Incident Detection Method Based on Convolutional Neural Network and Extreme Gradient Boosting. Preprints 2020, 2020040248
Yang, Q.; Fang, Y.; Zheng, L.; Zhou, X.; Peng, B. An Expressway Traffic Incident Detection Method Based on Convolutional Neural Network and Extreme Gradient Boosting. Preprints2020, 2020040248
APA Style
Yang, Q., Fang, Y., Zheng, L., Zhou, X., & Peng, B. (2020). An Expressway Traffic Incident Detection Method Based on Convolutional Neural Network and Extreme Gradient Boosting. Preprints. https://doi.org/
Chicago/Turabian Style
Yang, Q., Xiangyu Zhou and Bo Peng. 2020 "An Expressway Traffic Incident Detection Method Based on Convolutional Neural Network and Extreme Gradient Boosting" Preprints. https://doi.org/
Abstract
Accurate and efficient traffic incident detection methods can effectively alleviate traffic congestion caused by traffic incidents, prevent secondary accidents and improve the safety of urban road traffic.Aiming at the problems that the traditional machine learning event detection method cannot fully extract the parameter characteristics of traffic flow and is not suitable formulti-dimensional and non-linear massive data, we propose a new traffic event detection method(CNN-XGBoost).This method combines the respective advantages of Convolution Neural Network(CNN) and Extreme Gradation Boosting (XGBoost). Firstly, we preprocessed the original freeway traffic incident detection data set by constructing initial variable set, data normalization, data balance processing and dimension reorganization. Secondly,we use CNN network to automatically extract the deep features of event detection data, and use XGBoost as a classifier to classify the extracted features for expressway traffic event detection.Finally, we use the data set of Hangzhou expressway microwave detector in China to carry out simulation experiments on CNN-XGBoost. The experimental results show that compared with XGBoost, CNN, Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT) and other methods, CNN-XGBoost method can effectively improve the accuracy of expressway traffic event detection and has better generalization ability.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
