재난·재해 상황을 대비한 클러스터링 분석 기반의 도로링크별 취약성 평가 연구
Vulnerability Evaluation by Road Link Based on Clustering Analysis for Disaster Situation
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참고문헌
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초록
재난 및 재해 상황시 이동관리전략을 신속히 마련하기 위해서는 특정 도로구간을 통행하는교통류의 특징과 도로의 위상학적 구조 등을 사전에 파악해야 할 필요성이 있다. 이는 도로관리자가 미시적 도로단위별로 취약성을 평가한 후 재난·재해 상황에 대비한 적절한 모니터링과관리방안을 설정하는데 중요한 근거가 될 수 있기 때문이다. 따라서 본 연구에서는 도시부 도로링크별 취약성 평가를 위하여 공간밀도, 시간점유율, 네트워크 매개중심성 지표를 제시하였으며, 거리 및 밀도기반 클러스터링 분석을 통하여 각 링크그룹별로 가지고 있는 시공간 및위상학적 취약성을 정의하였다. 본 연구를 통해 제시된 결과는 도로 링크를 집단별로 특성화하여 취약성을 관리하는 것에 활용될 수 있으며, 재난·재해 시 우선 통제지점 선정 및 최적경로 제시를 위한 기초자료로도 활용 가능할 것으로 기대된다.
Abstract
It is necessary to grasp the characteristics of traffic flow passing through a specific road section and the topological structure of the road in advance in order to quickly prepare a movement management strategy in the event of a disaster or disaster. It is because it can be an essential basis for road managers to assess vulnerabilities by microscopic road units and then establish appropriate monitoring and management measures for disasters or disaster situations. Therefore, this study presented spatial density, time occupancy, and betweenness centrality index to evaluate vulnerabilities by road link in the city department and defined spatial-temporal and topological vulnerabilities by clustering analysis based on distance and density. From the results of this study, road administrators can manage vulnerabilities by characterizing each road link group. It is expected to be used as primary data for selecting priority control points and presenting optimal routes in the event of a disaster or disaster.
참고문헌 (19)
- Balijepalli, C., "Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas", Journal of Transport Geography, 39, pp.145~155 (2014)
- Berdica, K., "An introduction to road vulnerability : What has been done, is done and should be done", Transport Policy, 9(2), pp.117~127 (2002)
- Bono, F., "A network-based analysis of the impact of structural damage on urban accessibility following a disaster : The case of the seismically damaged Port Au Prince and Carrefour urban road networks", Journal of Transport Geography, 19(6), pp.1443~1455 (2011)
- Cantillo, V., "Assessing vulnerability of transportation networks for disaster response operations", Networks and Spatial Economics, 19, pp.243~273 (2019)
- Chen, B. Y., "Vulnerability analysis for large-scale and congested road networks with demand uncertainty", Transportation Research Part A : Policy and Practice, 46(3), pp.501~516 (2012)
- Chikaraishi, M., "On the possibility of short-term traffic prediction during disaster with machine learning approaches : An exploratory analysis", Transport Policy, 98, pp.91~104 (2020)
- Dalziell, E., "Risk and impact of natural hazards on a road network", Journal of Transportation Engineering, 127(2), pp.159~166 (2001)
- Dehghani, M. S., "Impact of road conditions and disruption uncertainties on network vulnerability", Journal of Infrastructure Systems, 20(3), pp.04014015~ (2014)
- Dong, S., "Measuring the topological robustness of transportation networks to disaster-induced failures : A percolation approach", Journal of Infrastructure Systems, 26(2), pp.04020009~ (2020)
- El-Rashidy, R. A., "An assessment method for highway network vulnerability", Journal of Transport Geography, 34, pp.34~43 (2014)
- Hong, J., "A spatially autoregressive and heteroskedastic space-time pedestrian exposure modeling framework with spatial lags and endogenous network topologies", Analytic Methods in Accident Research, 10, pp.26~46 (2016)
- Jenelius, E., "Network structure and travel patterns : Explaining the geographical disparities of road network vulnerability", Journal of Transport Geography, 17(3), pp.234~244 (2009)
- Jenelius, E., "Importance and exposure in road network vulnerability analysis", Transportation Research Part A : Policy and Practice, 40(7), pp.537~560 (2006)
- Kwon, C., "The Perfect Guide of Python Machine Learning", pp.409~410 (2020), Wikibooks
- Lee, J., "A Study on the Establishment of Disaster Prevention Road Network in Korea through Analysis of Disaster Prevention System in Japan", International Journal of Highway Engineering, 15(4), pp.5~8 (2013)
- Nielsen, F., "Introduction to HPC with MPI for Data Science", pp.221~239 (2016), Springer
- Scott, D. M., "Network robustness index : A new method for identifying critical links and evaluating the performance of transportation networks", Journal of Transport Geography, 14(3), pp.215~227 (2006)
- The Korea Transport Institute, "A Preliminary Study on National Traffic Forecasting System Development to Cope with Transportation Vulnerability", 1~4 (2011)
- Yin, H. Y., "A model for identifying vulnerable links of road networks based on Bayesian networks", Journal of Systems & Management, 19(6), pp.656~661 (2010)
