Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment
Joint Authors
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-09
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The approaches monitoring fatigue driving are studied because of the fact that traffic accidents caused by fatigue driving often have fatal consequences.
This paper proposes a new approach to predict driving fatigue using location data of commercial dangerous goods truck (CDT) and driver’s yawn data.
The proposed location data are from an existing dataset of a transportation company that was collected from 166 vehicles and drivers in an actual driving environment.
Six different categories of the predictor set are considered as fatigue-related indexes including travel time, day of week, road type, continuous driving time, average velocity, and overall mileage.
The driver’s yawn data are used as a proxy for ground truth for the classification algorithm.
From the six different categories of the predictor set, we obtain a set of 17 predictor variables to train logistic regression, neural network, and random forest classifiers.
Then, we evaluate the predictive performance of the classifiers based on three indexes: accuracy, F1-measure, and area under the ROC curve (AUROC).
The results show that the random forest is more suitable for predicting fatigue driving using location data according to its best accuracy (74.18%), F1-measure (62.02%), and AUROC (0.8059).
Finally, we analyze the relationship between fatigue driving and driving environment according to variable importance described by random forest.
In summary, our results obviously exhibit the potential of location data for reducing the accident rate caused by fatigue driving in practice.
American Psychological Association (APA)
Niu, Shifeng& Li, Guiqiang. 2020. Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1175609
Modern Language Association (MLA)
Niu, Shifeng& Li, Guiqiang. Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment. Journal of Advanced Transportation No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1175609
American Medical Association (AMA)
Niu, Shifeng& Li, Guiqiang. Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1175609
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175609