Building machine-learning models for reducing the severity of bicyclist road traffic injuries
Predicting the severity of injuries caused by traffic accidents is an important undertaking because it may lead to establishing regulations increasing road-user safety. Bicyclists are a particularly susceptible category of road users, which is especially troubling considering the environmental, fina...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Transportation Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666691X23000192 |
| _version_ | 1797813749555396608 |
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| author | Slava Birfir Amir Elalouf Tova Rosenbloom |
| author_facet | Slava Birfir Amir Elalouf Tova Rosenbloom |
| author_sort | Slava Birfir |
| collection | DOAJ |
| description | Predicting the severity of injuries caused by traffic accidents is an important undertaking because it may lead to establishing regulations increasing road-user safety. Bicyclists are a particularly susceptible category of road users, which is especially troubling considering the environmental, financial, and health benefits of this mode of transportation. As a result, this study aims to apply machine learning to identify risk variables that may result in serious biker injuries in the case of an accident.Machine-learning models make no assumptions about the connections between variables. Hence, it has been argued that machine-learning approaches produce better outcomes than statistical procedures. This study selects the “best” machine-learning classification system from a vast pool of similar algorithms to predict the severity of bicycling injuries. Machine learning allows the system to learn from experience and improve without being too programmed.We first use a variety of feature selection algorithms to identify a list of features related to the accident and the environment that have the greatest impact on the severity of bicyclist injuries. This feature list is then used as input data to various machine-learning algorithms that predict the class of bicyclist injury severity at one of three levels (fatal, serious, and slight). The “best” machine-learning algorithm is identified on the basis of having the highest levels of accuracy, precision, recall, and F1 score. The current models were developed and trained based on Israeli road-traffic accident data from 2009 to 2019, meaning that new models would need to be developed for other geographical locations. In addition, the models would need to be updated to take account of the changing relationships between motorists, bicyclists, and the environment. Nevertheless, the proposed methodology has universal applicability. |
| first_indexed | 2024-03-13T07:57:22Z |
| format | Article |
| id | doaj.art-b225feee33cf4b5786161ca5c0c021ea |
| institution | Directory Open Access Journal |
| issn | 2666-691X |
| language | English |
| last_indexed | 2024-03-13T07:57:22Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Engineering |
| spelling | doaj.art-b225feee33cf4b5786161ca5c0c021ea2023-06-02T04:23:57ZengElsevierTransportation Engineering2666-691X2023-06-0112100179Building machine-learning models for reducing the severity of bicyclist road traffic injuriesSlava Birfir0Amir Elalouf1Tova Rosenbloom2Bar-Ilan University, Department of Management, Ramat-Gan 52900, IsraelCorresponding author.; Bar-Ilan University, Department of Management, Ramat-Gan 52900, IsraelBar-Ilan University, Department of Management, Ramat-Gan 52900, IsraelPredicting the severity of injuries caused by traffic accidents is an important undertaking because it may lead to establishing regulations increasing road-user safety. Bicyclists are a particularly susceptible category of road users, which is especially troubling considering the environmental, financial, and health benefits of this mode of transportation. As a result, this study aims to apply machine learning to identify risk variables that may result in serious biker injuries in the case of an accident.Machine-learning models make no assumptions about the connections between variables. Hence, it has been argued that machine-learning approaches produce better outcomes than statistical procedures. This study selects the “best” machine-learning classification system from a vast pool of similar algorithms to predict the severity of bicycling injuries. Machine learning allows the system to learn from experience and improve without being too programmed.We first use a variety of feature selection algorithms to identify a list of features related to the accident and the environment that have the greatest impact on the severity of bicyclist injuries. This feature list is then used as input data to various machine-learning algorithms that predict the class of bicyclist injury severity at one of three levels (fatal, serious, and slight). The “best” machine-learning algorithm is identified on the basis of having the highest levels of accuracy, precision, recall, and F1 score. The current models were developed and trained based on Israeli road-traffic accident data from 2009 to 2019, meaning that new models would need to be developed for other geographical locations. In addition, the models would need to be updated to take account of the changing relationships between motorists, bicyclists, and the environment. Nevertheless, the proposed methodology has universal applicability.http://www.sciencedirect.com/science/article/pii/S2666691X23000192Transportation engineeringBicyclist injury severityMachine-learning classification algorithms |
| spellingShingle | Slava Birfir Amir Elalouf Tova Rosenbloom Building machine-learning models for reducing the severity of bicyclist road traffic injuries Transportation Engineering Transportation engineering Bicyclist injury severity Machine-learning classification algorithms |
| title | Building machine-learning models for reducing the severity of bicyclist road traffic injuries |
| title_full | Building machine-learning models for reducing the severity of bicyclist road traffic injuries |
| title_fullStr | Building machine-learning models for reducing the severity of bicyclist road traffic injuries |
| title_full_unstemmed | Building machine-learning models for reducing the severity of bicyclist road traffic injuries |
| title_short | Building machine-learning models for reducing the severity of bicyclist road traffic injuries |
| title_sort | building machine learning models for reducing the severity of bicyclist road traffic injuries |
| topic | Transportation engineering Bicyclist injury severity Machine-learning classification algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2666691X23000192 |
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