Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
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ÇѱÛÁ¦¸ñ(Korean Title) |
°ú°Å ±³ÅëÁ¤Ã¼ ÆÐÅÏÀ» ÀÌ¿ëÇÑ ÇöÀçÀÇ ±³ÅëÁ¤Ã¼ º¯È ÆǺ° ¾Ë°í¸®Áò |
¿µ¹®Á¦¸ñ(English Title) |
An Algorithm for Identifying the Change of the Current Traffic Congestion Using Historical Traffic Congestion Patterns |
ÀúÀÚ(Author) |
ÀÌ°æ¹Î
È«ºÀÈñ
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Kyungmin Lee
Bonghee Hong
Doseong Jeong
Jiwan Lee
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¿ø¹®¼ö·Ïó(Citation) |
VOL 21 NO. 01 PP. 0019 ~ 0028 (2015. 01) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
In this paper, we proposed an algorithm for the identification of relieving or worsening current traffic congestion using historic traffic congestion patterns. Historical congestion patterns were placed in an adjacency list. The patterns were constructed to represent spatial and temporal length for status of a congested road. Then, we found information about historical traffic congestions that were similar to today¡¯s traffic congestion and will use that information to show how to change traffic congestion in the future. The most similar pattern to current traffic status among the historical patterns corresponded to starting section of current traffic congestion. One of our experiment results had average error when we compared identified changes of the congestion for one of the sections in the congestion road by using our proposal and real traffic status. The average error was 15 minutes. Another result was for the long congestion road consisting of several sections. The average error for this result was within 10 minutes.
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Å°¿öµå(Keyword) |
±³Åë ºò µ¥ÀÌÅÍ
±³ÅëÁ¤Ã¼ ÆÐÅÏ
ÆÐÅÏ ºñ±³
ÀÎÁ¢ ¸®½ºÆ®
traffic big data
congestion patterns
pattern matching
adjacency list
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