Scholarlink

<p>War conflicts constantly generate spatiotemporal disruption waves, particularly affecting transport systems. Limited transport observability during conflicts challenges assessing real-time disruptions and elucidating underlying causes and consequences. However, predicting event-specific system response and capacity loss is paramount for rehabilitation and resiliency. This study proposes a comprehensive framework that leverages pervasive data from conflict regions to automate time-series traffic models development, derive multiple time-series mobility metrics, and discern the spatiotemporal patterns through statistical learning to isolate war-induced disruptions. The disruptions are categorized based on plausible war events to quantify cause-specific impacts for three Ukrainian cities. Kyiv is most severely disrupted by migration, and Mariupol by occupation and battles with impact area, accessibility loss, and capacity reduction up to 39 km<sup>2</sup>, 300%, and 85%, respectively. This approach provides a rapid, exploratory alternative for understanding the immediate and dynamic consequences of conflicts on transport systems in large-scale, data-scarce conflict environments, informing resilient transport planning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Travel impacts of spatiotemporal disruptions in war conflicts: the case of Russian invasion of Ukraine

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

查看全文
  • Moeid Qurashi,
  • Anna Sotnikova,
  • Dung-Ying Lin,
  • Tomasz Bednarz,
  • S. Travis Waller

摘要

War conflicts constantly generate spatiotemporal disruption waves, particularly affecting transport systems. Limited transport observability during conflicts challenges assessing real-time disruptions and elucidating underlying causes and consequences. However, predicting event-specific system response and capacity loss is paramount for rehabilitation and resiliency. This study proposes a comprehensive framework that leverages pervasive data from conflict regions to automate time-series traffic models development, derive multiple time-series mobility metrics, and discern the spatiotemporal patterns through statistical learning to isolate war-induced disruptions. The disruptions are categorized based on plausible war events to quantify cause-specific impacts for three Ukrainian cities. Kyiv is most severely disrupted by migration, and Mariupol by occupation and battles with impact area, accessibility loss, and capacity reduction up to 39 km2, 300%, and 85%, respectively. This approach provides a rapid, exploratory alternative for understanding the immediate and dynamic consequences of conflicts on transport systems in large-scale, data-scarce conflict environments, informing resilient transport planning.