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도시조직이 생활도로 보행자 교통사고에 미치는 영향 : 서울시 생활도로 내 보행자 교통사고 다발구간을 중심으로The Effect of Urban Tissue on Pedestrian Traffic Accidents in the Living Roads : Focused on the Pedestrian Traffic Accident Hot Spots Section in the Seoul’s Living Road

Other Titles
The Effect of Urban Tissue on Pedestrian Traffic Accidents in the Living Roads : Focused on the Pedestrian Traffic Accident Hot Spots Section in the Seoul’s Living Road
Authors
박수훈김동현박진아
Issue Date
Mar-2020
Publisher
대한국토·도시계획학회
Keywords
Pedestrian Traffic Accidents; Urban Tissue; Living Roads; Deep Learnning; Urban Design; 보행자 교통사고; 도시조직; 생활도로; 딥 러닝; 도시설계
Citation
국토계획, v.55, no.2, pp 5 - 14
Pages
10
Indexed
KCI
Journal Title
국토계획
Volume
55
Number
2
Start Page
5
End Page
14
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145988
DOI
10.17208/jkpa.2020.04.55.2.5
ISSN
1226-7147
2383-9171
Abstract
도시조직 중 가로경관의 시각적 요소는 보행자 교통사고에 직접적 요인으로 도출되었지만 대상의 크기에 따라 데이터 구축의어려움이 있었기에 교통사고와 도시조직 사이의 영향관계를 규명하는 연구들은 도시의 맥락적인 의미를 가지는 영역단위의 밀도와 토지이용 특성 등으로만 연구를 진행하였다. 그러나 최근컴퓨터 비전 분야의 발달로 가로경관의 시각적 요소를 구축할 수있게 되었다. 따라서 본 연구의 목적은 가로단위 또는 영역단위로 도시조직을 세분화하여 가로경관의 시각적 요소를 포함한 생활도로 보행자 교통사고 영향요소를 확인하고, 추후 안전한 생활도로를 위한정책적 시사점을 제시하고자 한다.
The purpose of this study is to subdivide urban tissue into road-based and area-based factors and to identify the effects of pedestrian traffic accidents on living roads. The major findings of this study are as follows. First, the variables affecting the accident ratio and the dependent variables, were found to be statistically significant in terms of openness, enclosure, greenery, signboard, number of buildings, and the block size of area-based factors. Second, the block size was significant in the area unit. This is because the smaller the block size, the higher the accident ratio of the road segment. This result is in agreement with the related studies. Third, the statistically significant variables in the road units were found to be dependent on the openness, enclosure, greenery, signboard, and number of buildings. After checking the spatial location of these sections, it is found that the location is a dense space of neighborhood living facilities, which is a grid road structure. Lastly, this study constructs and analyzes the road unit elements, which have difficulty in data construction, using deep learning. Through this, it suggested applicability in the city, architecture, and transportation.
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