Constructing an Expert Support System for Traffic Accident Liability of Pedestrian

碩士 === 逢甲大學 === 運輸科技與管理學系 === 99 === Recent years, traffic accidents have been increasing constantly in Taiwan, and the accident will cause the casualties, legal disputations, vehicle damages and other issues. However, domestic Local Traffic Accident Appraisal Committees (LTAAC) is lack of manpower,...

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Main Authors: Pei-Jung Chung, 鍾佩蓉
Other Authors: Ming-Shan Ye
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/52982252374381235468
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spelling ndltd-TW-099FCU054230082015-10-21T04:10:28Z http://ndltd.ncl.edu.tw/handle/52982252374381235468 Constructing an Expert Support System for Traffic Accident Liability of Pedestrian 建構行人交通事故責任判定之專家決策支援系統 Pei-Jung Chung 鍾佩蓉 碩士 逢甲大學 運輸科技與管理學系 99 Recent years, traffic accidents have been increasing constantly in Taiwan, and the accident will cause the casualties, legal disputations, vehicle damages and other issues. However, domestic Local Traffic Accident Appraisal Committees (LTAAC) is lack of manpower, and each committee member has different background. Therefore, the different LTAAC have different judging criteria for traffic accident liability. It highlights that we need a rational criteria for those different LTAAC for reference. The aim of this study used the Classification and Regression Tree (CART), Genetic Programming (GP) and Artificial Neural Network (ANN) to construct an expert support system of traffic accident liability for pedestrian. Methodology includes literature review to select variables and then to apply the chi-square test to select the significant variables such as LTAAC database has 30 significant variables, and Taiwan Provincial Traffic Accident Appraisal Committee (TPTAAC) database has 25 significant variables, and both LTAAC and TPTAAC having the same traffic accident liability has 24 significant variables. We apply those significant variables to construct three models and then to validate. There are 685 pedestrian accidents in the database, and the collision types include car/pedestrian, and motorcycle /pedestrian. Also, the database will be distinguished into three types: (1) for LTAAC data only; (2) for TPTAAC only; (3) the same traffic accident liability data between LTAAC with TPTAAC. This study shows that ANN has the maximum accuracy rate 87.25%. The next, CART has 82.35%, and the GP has 69.61%. This study offers an expert support system, so the relevant person from LTAAC or TPTAAC can input the significant variables from a specific traffic accident. Then the system can automatically output the traffic accident liability. It is hopeful that this expert system can be used to improve the traffic accident liability among LTAAC or TPTAAC. Ming-Shan Ye 葉名山 2011 學位論文 ; thesis 136 zh-TW
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description 碩士 === 逢甲大學 === 運輸科技與管理學系 === 99 === Recent years, traffic accidents have been increasing constantly in Taiwan, and the accident will cause the casualties, legal disputations, vehicle damages and other issues. However, domestic Local Traffic Accident Appraisal Committees (LTAAC) is lack of manpower, and each committee member has different background. Therefore, the different LTAAC have different judging criteria for traffic accident liability. It highlights that we need a rational criteria for those different LTAAC for reference. The aim of this study used the Classification and Regression Tree (CART), Genetic Programming (GP) and Artificial Neural Network (ANN) to construct an expert support system of traffic accident liability for pedestrian. Methodology includes literature review to select variables and then to apply the chi-square test to select the significant variables such as LTAAC database has 30 significant variables, and Taiwan Provincial Traffic Accident Appraisal Committee (TPTAAC) database has 25 significant variables, and both LTAAC and TPTAAC having the same traffic accident liability has 24 significant variables. We apply those significant variables to construct three models and then to validate. There are 685 pedestrian accidents in the database, and the collision types include car/pedestrian, and motorcycle /pedestrian. Also, the database will be distinguished into three types: (1) for LTAAC data only; (2) for TPTAAC only; (3) the same traffic accident liability data between LTAAC with TPTAAC. This study shows that ANN has the maximum accuracy rate 87.25%. The next, CART has 82.35%, and the GP has 69.61%. This study offers an expert support system, so the relevant person from LTAAC or TPTAAC can input the significant variables from a specific traffic accident. Then the system can automatically output the traffic accident liability. It is hopeful that this expert system can be used to improve the traffic accident liability among LTAAC or TPTAAC.
author2 Ming-Shan Ye
author_facet Ming-Shan Ye
Pei-Jung Chung
鍾佩蓉
author Pei-Jung Chung
鍾佩蓉
spellingShingle Pei-Jung Chung
鍾佩蓉
Constructing an Expert Support System for Traffic Accident Liability of Pedestrian
author_sort Pei-Jung Chung
title Constructing an Expert Support System for Traffic Accident Liability of Pedestrian
title_short Constructing an Expert Support System for Traffic Accident Liability of Pedestrian
title_full Constructing an Expert Support System for Traffic Accident Liability of Pedestrian
title_fullStr Constructing an Expert Support System for Traffic Accident Liability of Pedestrian
title_full_unstemmed Constructing an Expert Support System for Traffic Accident Liability of Pedestrian
title_sort constructing an expert support system for traffic accident liability of pedestrian
publishDate 2011
url http://ndltd.ncl.edu.tw/handle/52982252374381235468
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