Publication: Using Machine Learning to Predict Injury Severity of Road Traffic Accidents during New Year Festivals from Thailand's Open Government Data
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Issued Date
2021
Resource Type
Language
eng
File Type
application/pdf
Other identifier(s)
2-s2.0-85107776287
Rights Holder(s)
Scopus
Bibliographic Citation
Proceeding of the 2021 9th International Electrical Engineering Congress, iEECON 2021. Vol , No. (2021), p.464-467
Suggested Citation
Boonserm E., Wiwatwattana N. Using Machine Learning to Predict Injury Severity of Road Traffic Accidents during New Year Festivals from Thailand's Open Government Data. Proceeding of the 2021 9th International Electrical Engineering Congress, iEECON 2021. Vol , No. (2021), p.464-467. doi:10.1109/iEECON51072.2021.9440287 Retrieved from: https://hdl.handle.net/20.500.14740/7857
Author(s)
Abstract
The fast-rising number of vehicles on the road has resulted in a matching number of traffic accidents, especially during long holidays when people travel back home. A better understanding of the risk factors leading to traffic accidents may help decrease the number and prevent severe injuries and deaths. This paper addresses the imbalanced binary classification problem of predicting injury severity of road traffic accidents during Thailand's new year festivals using Thailand's open government data. Random undersampling, oversampling with SMOTE, and the combination of both undersampling and oversampling were used to rebalance training instances before fitting any Random Forests models. The results show that random undersampling gives the best performance at 83 percent with regards to both recall and accuracy. Important factors driving the prediction of severe injuries are delivering method, age, and alcohol drinking. Our work could benefit public authorities in analyzing accidents' hot spots and crucial factors that discourage road safety. © 2021 IEEE.
Subject(s)
Accidents
Decision trees
Forecasting
Government data processing
Health risks
Machine learning
Motor transportation
Roads and streets
Alcohol drinking
Binary classification problems
Injury severity
Matching numbers
Number of vehicles
Public authorities
Random under samplings
Road traffic accidents
Open Data
Decision trees
Forecasting
Government data processing
Health risks
Machine learning
Motor transportation
Roads and streets
Alcohol drinking
Binary classification problems
Injury severity
Matching numbers
Number of vehicles
Public authorities
Random under samplings
Road traffic accidents
Open Data