Crash prediction for freeway work zones in real time: A comparison between Convolutional Neural Network and Binary Logistic Regression model
Safety of drivers in freeway work zones has been a problem. Real-time crash prediction helps prevent crashes before they happen. This paper looks at real-time crash prediction in freeway work zones by using machine learning approaches. Both the Convolutional Neural Network and the Binary Logistic Regression model are introduced. For training and testing the models, crash data and traffic data from several freeways in D7 zone, Los Angeles, California, were used. Crash data were collected from California Highway Patrol Incident System, and traffic data were obtained from the Caltrans Performance Measurement System. Data processing and matching were conducted. Both the two models were trained and tested. Results show that the Convolutional Neural Network performed slightly better over the Binary Logistic Regression model in predicting crashes with a global accuracy of 79.50%. Despite this, the main merit of the Binary Logistic Regression model is that it is able estimate the impact of affecting variables on the probability of crashes and can help identify the factors related to risks in work zones. Machine learning approaches applied in this study perform well in crash prediction. In general, machine learning techniques and reliable real-time crash prediction applications can be promising in helping drivers and transportation engineers make timely responses to potential crashes on freeways.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/20460430
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Supplemental Notes:
- © 2021 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. Abstract reprinted with permission of Elsevier.
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Authors:
- Wang, Junhua
- Song, Hao
- Fu, Ting
- Behan, Molly
- Jie, Lei
- He, Yingxian
- Shangguan, Qiangqiang
- Publication Date: 2022-9
Language
- English
Media Info
- Media Type: Web
- Features: Figures; Maps; References; Tables;
- Pagination: pp 484-495
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Serial:
- International Journal of Transportation Science and Technology
- Volume: 11
- Issue Number: 3
- Publisher: Elsevier
- ISSN: 2046-0430
- Serial URL: http://www.sciencedirect.com/science/journal/20460430
Subject/Index Terms
- TRT Terms: Crash data; Crash risk forecasting; Freeways; Logistic regression analysis; Machine learning; Neural networks; Predictive models; Traffic data; Work zone safety
- Geographic Terms: Los Angeles (California)
- Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;
Filing Info
- Accession Number: 01778306
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 29 2021 12:02PM