Self-Organizing Feature Maps for Traffic Accident Decision Support System

• Main Authors: Pin-Hung Chen, 陳品宏
• Other Authors: Pei Liu
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/17358090747268285672

碩士 === 逢甲大學 === 交通工程與管理所 === 93 === Traffic accidents can be resulted from various factors. Consequently, authentication on accident liabilities can be very tedious and difficult. Due to the fact that information collected in traffic accident reports are normally incomplete and are varied from case to case. Similar cases can sometimes be authenticated with different liabilities. On the other hand, knowledge and experiences of committee members can also bias his/her judgment on similar cases at different time. Such variance of authentication on similar cases can easily be criticized by general public, and may hurt the image of government-established investigation committees. Consequently, it is apparent that there is a need to unify liability authentication of all traffic accident investigation committees. In this study, a liability authentication support system was constructed by using self-organizing feature maps. This system is intended to provide accident records and liability authentication results similar to inquiries as supplementary information to committee members. Hopefully, righteousness and fairness can be better reached with the help of this system. Due to the fact that accidents involving more than three cars can be very complicated, this study was thus limited to two-car accidents. The first step to construct the proposed system is to establish a self-organizing feature map (SOM) model for two-car crashes. Effectiveness of SOM models were checked by using the Silhouette coefficients (SC). After SC value for every cluster being determined, the best clusters were chosen to be the proposed SOM models. Grey relation analysis was then employed to decide order of referable cases. Traffic accident information adopted in this study is abstracted from the database constructed by the center for traffic accident authentication in Feng Chia university. The grey relational values between new cases and reference cases calculated from the selected SOM models were found range between 0.6458 and 1. Average grey relational value of same crash was approximately 0.8208. Average grey relational value of crosswise crash was approximately 0.8668. Average grey relational value of opposite crash was approximately 0.8641. These values indicated that the proposed models do have ability to provide similar accident cases as the inquiry. With the selected SOM models, a decision support system for traffic accident liability authentication is constructed using Active Sever Pages (ASP). The system is designed to provide characteristics and liability authentication results of cases similar to user input inquiries. Meanwhile, traffic safety rules related to the input inquiry can also be provided to the users for reference. Although initial results appeared to be acceptable, the system is still under development. In this paper, basic example is provided for better understanding of the system. Any comment or suggestion will certainly be sincerely appreciated.