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A Study on the Implementation of Walking Environment Projects by Analyzing Characteristics of Pedestrian Accidents by Local Government Types

지방자치단체의 유형별 보행자사고 특성분석 및 보행환경조성사업 개선방안 연구

  • Park, Jinkyung (Department of Regional Development Research, Korea Research Institute for Local Administration) ;
  • Han, Myungjoo (Graduate School of Environmental Studies, Seoul National University)
  • 박진경 (한국지방행정연구원 지역발전연구실) ;
  • 한명주 (서울대학교 환경대학원)
  • Received : 2014.05.12
  • Accepted : 2014.09.04
  • Published : 2014.12.31

Abstract

In this study, nonhierarchical K-mean cluster analysis is used to classify the types of 230 local governments and the Mann-Whitney U test and Kruskal-Wallis analysis are used to analyze the characteristics of pedestrian accidents by region types. With empirical analysis of pedestrian accidents, this study suggests improvements of walking environments reflecting local characteristics. Type 1-A (relatively dominant urban commercial areas), Type 1-B (predominantly urban residence) and Type 2 (rural areas) have been classified using nonhierarchical K-mean cluster analysis. According to the results, pedestrian accident rate on community roads was more than 60% for all types and incidence rate in rural areas was higher than that in urban areas. In addition, pedestrian accidents of Type 1-B have been found to occur more frequently than Type 2 in intersections and crossings, while the number of roadside casualties for Type 2 was highest.

본 연구는 비계층적 K-평균 군집분석을 이용하여 보행자사고와 토지이용특성에 따른 지방자치단체의 유형을 분류하고 유형별 보행자사고의 세부특성을 Mann-Whitney U검증과 Kruskal-Wallis 검증을 이용하여 분석한 다음 그 결과로부터 보행환경 조성사업에 대한 개선방안을 제안하였다. 지역은 행정시와 행정구를 제외한 기초지방자치단체를 기준으로 230개 시 군 구로 구분된다. 비계층적 K-평균 군집분석 결과 230개 지방자치단체는 유형 1-A(상업지가 상대적으로 우세한 도시형), 유형 1-B(주거지가 우세한 도시형), 유형 2(농촌형)로 분류되었으며, 지자체 유형별 보행자사고 차이 검증결과 9m 미만 생활형 도로의 보행자사고 발생율은 공통적으로 60% 이상으로 도시형에 비해 농촌형에서 발생할 비율이 더 높았다. 또한 유형별로 유형 1-B는 교차로 내 보행자사고 사상자수가 많고 횡단 중 사고 비율도 높은 반면 길가장자리구역 통행 중 보행자 사상자수 비율은 유형 2에 해당하는 농촌형이 가장 높았다.

Keywords

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  1. A Selection of High Pedestrian Accident Zones Using Traffic Accident Data and GIS: A Case Study of Seoul vol.34, pp.3, 2016, https://doi.org/10.7848/ksgpc.2016.34.3.221