Abstract
In Taiwan, there are hundreds of accidents every day recorded by government due to the human factor and environmental factor. The accident usually involved the money dispute; therefore the accident appraisal must indicate the bilateral parties' blame clearly: all blame; major blame; minor blame and none blame. Although the local police can give a preliminary analysis report at first, the report cannot be official evidence. If the people need a credible appraisal report, they have to apply for the Taiwan Provincial Government Traffic Accident Investigation Committee's accident appraisal report. However, applying for Committee's accident appraisal report will take long time. Therefore, this study employed radial basis function neural network to build an expert system for appraisal of bilateral vehicle accident. The database was built from 307 accident cases in Taiwan from the year of 2004 to 2008. According to Committee's analysis, there are 30 appraisal basses including 6 environmental basses and 24 vehicle basses chosen to be the input of the expert system. The training data includes three types: 70 cases training; 140 cases training; 207 cases training. Validation stage was carried out by using 100 fixed cases and the correctness was recorded. In the first stage, correctness rate is 76% for training with 70 cases. In the second stage, correctness rate is increased to 81% for training with 140 cases. In the third stage, correctness rate is increased to 89% for training with 207 cases. The training and validation processes were completed in one second. Therefore, the expert system proposed in this work is demonstrated to be an efficient system for the accident appraisal.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011 |
| Publisher | IEEE Computer Society |
| Pages | 869-872 |
| Number of pages | 4 |
| ISBN (Print) | 9781424499533 |
| DOIs | |
| Publication status | Published - 2011 Jan 1 |
| Event | 2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China Duration: 2011 Jul 26 → 2011 Jul 28 |
Publication series
| Name | Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011 |
|---|---|
| Volume | 2 |
Other
| Other | 2011 7th International Conference on Natural Computation, ICNC 2011 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 11-07-26 → 11-07-28 |
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All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Neuroscience(all)
Cite this
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RETRACTED ARTICLE : The system for appraisal of vehicle accident based on radial basis function neural networks. / Tseng, Wen Kung; Lu, Chung Sheng.
Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011. IEEE Computer Society, 2011. p. 869-872 6022220 (Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011; Vol. 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - RETRACTED ARTICLE
T2 - The system for appraisal of vehicle accident based on radial basis function neural networks
AU - Tseng, Wen Kung
AU - Lu, Chung Sheng
PY - 2011/1/1
Y1 - 2011/1/1
N2 - In Taiwan, there are hundreds of accidents every day recorded by government due to the human factor and environmental factor. The accident usually involved the money dispute; therefore the accident appraisal must indicate the bilateral parties' blame clearly: all blame; major blame; minor blame and none blame. Although the local police can give a preliminary analysis report at first, the report cannot be official evidence. If the people need a credible appraisal report, they have to apply for the Taiwan Provincial Government Traffic Accident Investigation Committee's accident appraisal report. However, applying for Committee's accident appraisal report will take long time. Therefore, this study employed radial basis function neural network to build an expert system for appraisal of bilateral vehicle accident. The database was built from 307 accident cases in Taiwan from the year of 2004 to 2008. According to Committee's analysis, there are 30 appraisal basses including 6 environmental basses and 24 vehicle basses chosen to be the input of the expert system. The training data includes three types: 70 cases training; 140 cases training; 207 cases training. Validation stage was carried out by using 100 fixed cases and the correctness was recorded. In the first stage, correctness rate is 76% for training with 70 cases. In the second stage, correctness rate is increased to 81% for training with 140 cases. In the third stage, correctness rate is increased to 89% for training with 207 cases. The training and validation processes were completed in one second. Therefore, the expert system proposed in this work is demonstrated to be an efficient system for the accident appraisal.
AB - In Taiwan, there are hundreds of accidents every day recorded by government due to the human factor and environmental factor. The accident usually involved the money dispute; therefore the accident appraisal must indicate the bilateral parties' blame clearly: all blame; major blame; minor blame and none blame. Although the local police can give a preliminary analysis report at first, the report cannot be official evidence. If the people need a credible appraisal report, they have to apply for the Taiwan Provincial Government Traffic Accident Investigation Committee's accident appraisal report. However, applying for Committee's accident appraisal report will take long time. Therefore, this study employed radial basis function neural network to build an expert system for appraisal of bilateral vehicle accident. The database was built from 307 accident cases in Taiwan from the year of 2004 to 2008. According to Committee's analysis, there are 30 appraisal basses including 6 environmental basses and 24 vehicle basses chosen to be the input of the expert system. The training data includes three types: 70 cases training; 140 cases training; 207 cases training. Validation stage was carried out by using 100 fixed cases and the correctness was recorded. In the first stage, correctness rate is 76% for training with 70 cases. In the second stage, correctness rate is increased to 81% for training with 140 cases. In the third stage, correctness rate is increased to 89% for training with 207 cases. The training and validation processes were completed in one second. Therefore, the expert system proposed in this work is demonstrated to be an efficient system for the accident appraisal.
UR - http://www.scopus.com/inward/record.url?scp=80053400019&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053400019&partnerID=8YFLogxK
U2 - 10.1109/ICNC.2011.6022220
DO - 10.1109/ICNC.2011.6022220
M3 - Conference contribution
AN - SCOPUS:80053400019
SN - 9781424499533
T3 - Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
SP - 869
EP - 872
BT - Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
PB - IEEE Computer Society
ER -