A New Approach in Improving Traffic Accident Injury Prediction Accuracy
This paper focused on the effect of intrusion magnitude and maximum deformation location in improving the accuracy of Injury Severity Prediction (ISP) for Advanced Automatic Crash Notification (AACN) system. This study used 545-passenger vehicles involved in Car-to-Car side impact data from NASS CDS...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Society of Automotive Engineers of Japan, Inc.
2017-10-01
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| Series: | International Journal of Automotive Engineering |
| Online Access: | https://www.jstage.jst.go.jp/article/jsaeijae/8/4/8_20174110/_article/-char/ja |
| _version_ | 1797357077183594496 |
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| author | Chinmoy Pal Shigeru Hirayama Narahari Sangolla Jeyabharath Manoharan Vimalathithan Kulothungan |
| author_facet | Chinmoy Pal Shigeru Hirayama Narahari Sangolla Jeyabharath Manoharan Vimalathithan Kulothungan |
| author_sort | Chinmoy Pal |
| collection | DOAJ |
| description | This paper focused on the effect of intrusion magnitude and maximum deformation location in improving the accuracy of Injury Severity Prediction (ISP) for Advanced Automatic Crash Notification (AACN) system. This study used 545-passenger vehicles involved in Car-to-Car side impact data from NASS CDS (CY: 2004-2014). Variables mentioned in Kononen’s 2011 ISP algorithm are considered as base model. In addition to Kononen’s variables, magnitude of intrusion and maximum deformation location are added in the proposed model. As the location of maximum deformation moves away from the B pillar to end regions (front or back), the percentage of serious injury reduces drastically. Similar trend is verified in both accident analysis and FE numerical simulation results. Addition of intrusion magnitude and location of maximum deformation as additional injury predictors helped to improve the proposed model sensitivity, overall accuracy by 16%, 3.12% respectively without any change in specificity value. |
| first_indexed | 2024-03-08T14:38:05Z |
| format | Article |
| id | doaj.art-4485829a659d4efdbffd2b8a66a5dae6 |
| institution | Directory Open Access Journal |
| issn | 2185-0992 |
| language | English |
| last_indexed | 2024-03-08T14:38:05Z |
| publishDate | 2017-10-01 |
| publisher | Society of Automotive Engineers of Japan, Inc. |
| record_format | Article |
| series | International Journal of Automotive Engineering |
| spelling | doaj.art-4485829a659d4efdbffd2b8a66a5dae62024-01-12T01:09:05ZengSociety of Automotive Engineers of Japan, Inc.International Journal of Automotive Engineering2185-09922017-10-018417918510.20485/jsaeijae.8.4_179A New Approach in Improving Traffic Accident Injury Prediction AccuracyChinmoy Pal0Shigeru Hirayama1Narahari Sangolla2Jeyabharath Manoharan3Vimalathithan Kulothungan4NISSAN MotorNISSAN MotorRenault Nissan Technology Business CenterRenault Nissan Technology Business CenterRenault Nissan Technology Business CenterThis paper focused on the effect of intrusion magnitude and maximum deformation location in improving the accuracy of Injury Severity Prediction (ISP) for Advanced Automatic Crash Notification (AACN) system. This study used 545-passenger vehicles involved in Car-to-Car side impact data from NASS CDS (CY: 2004-2014). Variables mentioned in Kononen’s 2011 ISP algorithm are considered as base model. In addition to Kononen’s variables, magnitude of intrusion and maximum deformation location are added in the proposed model. As the location of maximum deformation moves away from the B pillar to end regions (front or back), the percentage of serious injury reduces drastically. Similar trend is verified in both accident analysis and FE numerical simulation results. Addition of intrusion magnitude and location of maximum deformation as additional injury predictors helped to improve the proposed model sensitivity, overall accuracy by 16%, 3.12% respectively without any change in specificity value.https://www.jstage.jst.go.jp/article/jsaeijae/8/4/8_20174110/_article/-char/ja |
| spellingShingle | Chinmoy Pal Shigeru Hirayama Narahari Sangolla Jeyabharath Manoharan Vimalathithan Kulothungan A New Approach in Improving Traffic Accident Injury Prediction Accuracy International Journal of Automotive Engineering |
| title | A New Approach in Improving Traffic Accident Injury Prediction Accuracy |
| title_full | A New Approach in Improving Traffic Accident Injury Prediction Accuracy |
| title_fullStr | A New Approach in Improving Traffic Accident Injury Prediction Accuracy |
| title_full_unstemmed | A New Approach in Improving Traffic Accident Injury Prediction Accuracy |
| title_short | A New Approach in Improving Traffic Accident Injury Prediction Accuracy |
| title_sort | new approach in improving traffic accident injury prediction accuracy |
| url | https://www.jstage.jst.go.jp/article/jsaeijae/8/4/8_20174110/_article/-char/ja |
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