Towards development of a roadway flood severity index

Curtis L. Walker, Amanda Siems-Anderson, Erin Towler, Aubrey Dugger, Andrew Gaydos, Gerry Wiener
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引用次数: 0

Abstract

Flooding is among the costliest and deadliest weather disasters. Moreover, different types of flooding have significant impacts on the transportation network and infrastructure including flash, riverine, urban, coastal, and storm surge. The variety of flooding scenarios makes it challenging to quantify the impacts of flooding on transportation across spatial scales; however, such metrics would be beneficial both prior to and after the event. Pre-flood metrics can promote enhanced impact-based decision-support guidance and hazard communication, while post-flood metrics may include larger regional disruptions located away from the most inundated areas and their associated secondary societal impacts. This study developed a retrospective Roadway Flood-Severity Index (RFSI) from 1982 to 2020 capable of integrating geo-located hydrometeorological data and transportation mobility information across localized and multi-state, sub-national regions to (1) categorize larger-scale, flood-related transportation disruptions, (2) understand the origins of those disruptions, and (3) identify severity risk levels of individual road segments and broader regions of transportation disruption during flood events. The fundamental question is, as flooding events unfold, can past hydrometeorological inundation information be coupled with transportation system network and mobility data to identify the most vulnerable roadway segments and regions? The overall mobility impacts of flooding on transportation were highly variable and relatively uncommon throughout the study period. Given this variability in other mobility data (e.g., vehicle speeds), hydrometeorological parameters were used exclusively as model inputs and crowdsourced Waze flood reports were used as the target response variable. A logistic regression based RFSI was found to best align with the dataset providing a “no flood” or “flood” classification. Eventually, this retrospective analysis will be extended to provide predictive capability as well. The RFSI is intended to provide transportation agencies with a quantitative metric to classify, categorize, and communicate the potential impacts of flood events throughout the transportation network.

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开发公路洪水严重程度指数
洪水是损失最大、死亡人数最多的天气灾害之一。此外,不同类型的洪水会对交通网络和基础设施产生重大影响,包括山洪、河水、城市洪水、沿海洪水和风暴潮。洪水情况多种多样,因此要量化洪水在不同空间尺度上对交通的影响具有挑战性;不过,在洪水事件发生之前和之后,这种衡量标准都是有益的。洪水前的指标可以促进基于影响的决策支持指导和危险沟通,而洪水后的指标则可能包括远离淹没最严重地区的更大区域性破坏及其相关的次生社会影响。本研究开发了从 1982 年到 2020 年的回顾性公路洪水严重性指数(RFSI),该指数能够整合地理位置水文气象数据和跨地方及多州、次国家区域的交通流动性信息,以(1)对更大规模的、与洪水相关的交通中断进行分类,(2)了解这些中断的起源,以及(3)确定洪水事件期间单个路段和更广泛区域交通中断的严重性风险等级。最根本的问题是,随着洪水事件的发生,过去的水文气象淹没信息能否与交通系统网络和流动性数据相结合,以确定最易受影响的路段和区域?在整个研究期间,洪水对交通的整体流动性影响变化很大,而且相对不常见。鉴于其他流动性数据(如车速)的这种可变性,水文气象参数被完全用作模型输入,而众包 Waze 的洪水报告被用作目标响应变量。结果发现,基于逻辑回归的 RFSI 与提供 "无洪水 "或 "洪水 "分类的数据集最为匹配。最终,这种回顾性分析还将扩展到提供预测能力。RFSI 的目的是为交通机构提供一个量化指标,用于对整个交通网络中的洪水事件的潜在影响进行分类、归类和交流。
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来源期刊
Transportation Research Interdisciplinary Perspectives
Transportation Research Interdisciplinary Perspectives Engineering-Automotive Engineering
CiteScore
12.90
自引率
0.00%
发文量
185
审稿时长
22 weeks
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