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Current Result Document :
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
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¿ø¹®¼ö·Ïó(Citation) |
VOL 10 NO. 08 PP. 0301 ~ 0310 (2021. 08) |
Çѱ۳»¿ë (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. |
¿µ¹®³»¿ë (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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