RETRACTED ARTICLE

The system for appraisal of vehicle accident based on radial basis function neural networks

Wen-Kung Tseng, Chung Sheng Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
PublisherIEEE Computer Society
Pages869-872
Number of pages4
Volume2
ISBN (Print)9781424499533
DOIs
Publication statusPublished - 2011 Jan 1
Event2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
Duration: 2011 Jul 262011 Jul 28

Other

Other2011 7th International Conference on Natural Computation, ICNC 2011
CountryChina
CityShanghai
Period11-07-2611-07-28

Fingerprint

Accidents
Neural networks
Bass
Expert Systems
Taiwan
Expert systems
State Government
Dissent and Disputes
Highway accidents
Traffic Accidents
Police
Law enforcement
Human engineering
Databases

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Neuroscience(all)

Cite this

Tseng, W-K., & Lu, C. S. (2011). RETRACTED ARTICLE: The system for appraisal of vehicle accident based on radial basis function neural networks. In Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011 (Vol. 2, pp. 869-872). [6022220] IEEE Computer Society. https://doi.org/10.1109/ICNC.2011.6022220
Tseng, Wen-Kung ; Lu, Chung Sheng. / RETRACTED ARTICLE : The system for appraisal of vehicle accident based on radial basis function neural networks. Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011. Vol. 2 IEEE Computer Society, 2011. pp. 869-872
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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.",
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Tseng, W-K & Lu, CS 2011, RETRACTED ARTICLE: The system for appraisal of vehicle accident based on radial basis function neural networks. in Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011. vol. 2, 6022220, IEEE Computer Society, pp. 869-872, 2011 7th International Conference on Natural Computation, ICNC 2011, Shanghai, China, 11-07-26. https://doi.org/10.1109/ICNC.2011.6022220

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. Vol. 2 IEEE Computer Society, 2011. p. 869-872 6022220.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Tseng W-K, Lu CS. RETRACTED ARTICLE: The system for appraisal of vehicle accident based on radial basis function neural networks. In Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011. Vol. 2. IEEE Computer Society. 2011. p. 869-872. 6022220 https://doi.org/10.1109/ICNC.2011.6022220