Earticle

초록

영어

Landslides represent a critical threat to transportation systems in developing countries characterized by complex topography and high exposure to meteorological hazards. In such contexts, slope failures are among the primary causes of road network disruptions, significantly affecting connectivity, accessibility, and emergency response capacity. Despite extensive research on landslide susceptibility, limited studies integrate hazard identification with transportation network performance and infrastructure planning. This study proposes a resilience-based framework to evaluate road network connectivity under landslide-induced disruptions. The first component develops a landslide susceptibility model using terrain-based indicators derived from digital elevation models, particularly slope, to classify areas into different risk levels. High-slope zones are identified as potential failure areas and validated against historical landslide records. The second component integrates susceptibility results with road network data to identify vulnerable infrastructure segments. A disruption scenario is simulated by removing road segments exposed to high-risk zones, and a graph-based network analysis is conducted to evaluate the impact on connectivity. Key urban nodes are selected to assess changes in accessibility between regions. Preliminary results indicate that a significant portion of the road network is exposed to high-risk areas, particularly in mountainous regions. The simulated disruption scenario reveals substantial increases in travel distance and the potential isolation of critical regions, especially in northern and southern corridors. The results also highlight limited redundancy in the network, emphasizing the vulnerability of key transportation links. The proposed framework provides a practical and transferable tool for identifying critical infrastructure and supporting strategic planning of alternative routes. The methodology is particularly relevant for developing regions such as the Philippines and Guatemala, where terrain and infrastructure constraints increase susceptibility to disruption. This approach contributes to enhancing transportation resilience and disaster risk reduction.

저자

• Ana Valeria Sosa [ Behavioral Analytics & Research for Transportation Systems (BARTS) Lab ]
• Sunghoo Kim [ Behavioral Analytics & Research for Transportation Systems (BARTS) Lab ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국재난정보학회 학술발표대회

• 간기

  부정기

• 수록기간

  2005~2026

• 십진분류

  KDC 338 DDC 361