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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö > Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö ¼ÒÇÁÆ®¿þ¾î ¹× µ¥ÀÌÅÍ °øÇÐ

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ÇѱÛÁ¦¸ñ(Korean Title) ±³Åë»ç°í ½É°¢ Á¤µµ ¿¹ÃøÀ» À§ÇÑ TATI ¸ðµ¨ Á¦¾È
¿µ¹®Á¦¸ñ(English Title) Proposed TATI Model for Predicting the Traffic Accident Severity
ÀúÀÚ(Author) Manzoor Hussain   Nazakat Ali   È«ÀåÀÇ   Manzoor Hussain   Nazakat Ali   Jang-Eui Hong   Ãß¹ÎÁö   ¹Ú¼ÒÇö   ¹Ú¿µÈ£   Min-Ji Choo   So-Hyun Park   Young-Ho Park  
¿ø¹®¼ö·Ïó(Citation) VOL 10 NO. 08 PP. 0301 ~ 0310 (2021. 08)
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(Korean Abstract)
TATI ¸ðµ¨À̶õ Traffic Accident Text to RGB Image ¸ðµ¨·Î, ±³Åë»ç°í ½É°¢ Á¤µµ ¿¹ÃøÀ» À§ÇÑ º» ³í¹®¿¡¼­ Á¦¾ÈÇÏ´Â ¹æ¹ý·ÐÀÌ´Ù. ±³Åë»ç°í Ä¡»çÀ²Àº ¸Å³â °¨¼ÒÇÏ´Â Ãß¼¼À̳ª OECD ȸ¿ø±¹ Áß ÇÏÀ§±Ç¿¡ ¼ÓÇØÀÖ´Ù. ±³Åë»ç°í Ä¡»çÀ² °¨¼Ò¸¦ À§ÇØ ¸¹Àº ¿¬±¸µéÀÌ ÁøÇàµÇ¾ú°í, ±× Áß¿¡¼­ ±³Åë»ç°í ½É°¢ Á¤µµ¸¦ ¿¹ÃøÇÏ¿© ¹ß»ý ¹× Ä¡»çÀ²À» ÁÙÀ̱â À§ÇÑ ¿¬±¸°¡ ²ÙÁØÇÏ°Ô ÁøÇàµÇ°í ÀÖ´Ù. ÀÌ¿Í °ü·ÃÇÏ¿© ÃÖ±Ù¿¡´Â Åë°è ¸ðµ¨°ú µö·¯´× ¸ðµ¨À» È°¿ëÇÏ¿© ±³Åë»ç°í ½É°¢ Á¤µµ ¿¹ÃøÀ» ÇÏ´Â ¿¬±¸°¡ È°¹ßÇÏ´Ù. º» ³í¹®¿¡¼­´Â ±³Åë»ç°í ½É°¢ Á¤µµ¸¦ ¿¹ÃøÇϱâ À§Çؼ­ ±³Åë»ç°í µ¥ÀÌÅ͸¦ Ä÷¯ À̹ÌÁö·Î º¯È¯ÇÏ°í, CNN ¸ðµ¨À» ÅëÇØ À̸¦ ¼öÇàÇÑ´Ù. ¼º´É ºñ±³¸¦ À§ÇØ Á¦¾ÈÇÏ´Â ¸ðµ¨°ú ´Ù¸¥ ¸ðµ¨µéÀ» °°Àº µ¥ÀÌÅÍ·Î ÇнÀ½ÃÅ°°í, ¿¹Ãø°á°ú¸¦ ºñ±³ÇÏ´Â ½ÇÇèÀ» ÁøÇàÇß´Ù. 10¹øÀÇ ½ÇÇèÀ» ÅëÇØ 4°³ÀÇ µö·¯´× ¸ðµ¨ÀÇ Á¤È®µµ¿Í ¿ÀÂ÷ ¹üÀ§¸¦ ºñ±³ÇÏ¿´´Ù. ½ÇÇè °á°ú¿¡ µû¸£¸é Á¦¾ÈÇÏ´Â TATI ¸ðµ¨ÀÇ Á¤È®µµ°¡ 0.85·Î °¡Àå ³ôÀº Á¤È®µµ¸¦ º¸¿´°í, 0.03À¸·Î µÎ ¹ø°·Î ³·Àº ¿ÀÂ÷ ¹üÀ§¸¦ º¸¿© ¼º´ÉÀÇ ¿ì¼ö¼ºÀ» È®ÀÎÇÏ¿´´Ù.
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(English Abstract)
The TATI model is a Traffic Accident Text to RGB Image model, which is a methodology proposed in this paper for predicting the severity of traffic accidents. Traffic fatalities are decreasing every year, but they are among the low in the OECD members. Many studies have been conducted to reduce the death rate of traffic accidents, and among them, studies have been steadily conducted to reduce the incidence and mortality rate by predicting the severity of traffic accidents. In this regard, research has recently been active to predict the severity of traffic accidents by utilizing statistical models and deep learning models. In this paper, traffic accident dataset is converted to color images to predict the severity of traffic accidents, and this is done via CNN models. For performance comparison, we experiment that train the same data and compare the prediction results with the proposed model and other models. Through 10 experiments, we compare the accuracy and error range of four deep learning models. Experimental results show that the accuracy of the proposed model was the highest at 0.85, and the second lowest error range at 0.03 was shown to confirm the superiority of the performance.
Å°¿öµå(Keyword) ¾ÈÀü¼º   ÅëÇÕ À§ÇùºÐ¼®   °áÇÔ Ä¡¸íµµ   »çÀ̹ö¹°¸®½Ã½ºÅÛ   ±ºÁýÁÖÇà ½Ã½ºÅÛ   Safety   Composite Hazards Analysis   Fault Criticality   Cyber-Physical Systems   Platooning System   TATI   Ä÷¯ Ç¥Çö   ½É°¢ Á¤µµ ¿¹Ãø   ±³Åë»ç°í   TATI   Color Representation   Severity Prediction   Traffic Accident  
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