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The impact of flooding on road transport: A depth-disruption function

洪水(心理学) 环境科学 功能(生物学) 运输工程 岩土工程 法律工程学 工程类 细胞生物学 心理学 心理治疗师 生物
作者
Maria Pregnolato,Alistair Ford,Sean M. Wilkinson,Richard Dawson
出处
期刊:Transportation Research Part D-transport and Environment [Elsevier BV]
卷期号:55: 67-81 被引量:580
标识
DOI:10.1016/j.trd.2017.06.020
摘要

• Transport networks underpin economic cities competiveness and society functioning. • During flooding transport infrastructure can be directly or indirectly damaged. • This paper reviewed modelling studies of the impacts of weather on transport. • The paper derived a new empirical function to relate flood depth and vehicle speed. • The function move forwards from the binary consideration of flood roads. • The function can be incorporated into flood risk analysis and transport appraisal. Transport networks underpin economic activity by enabling the movement of goods and people. During extreme weather events transport infrastructure can be directly or indirectly damaged, posing a threat to human safety, and causing significant disruption and associated economic and social impacts. Flooding, especially as a result of intense precipitation, is the predominant cause of weather-related disruption to the transport sector. Existing approaches to assess the disruptive impact of flooding on road transport fail to capture the interactions between floodwater and the transport system, typically assuming a road is fully operational or fully blocked, which is not supported by observations. In this paper we develop a relationship between depth of standing water and vehicle speed. The function that describes this relationship has been constructed by fitting a curve to video analysis supplemented by a range of quantitative data that has be extracted from existing studies and other safety literature. The proposed relationship is a good fit to the observed data, with an R-squared of 0.95. The significance of this work is that it is simple to incorporate our function into existing transport models to produce better estimates of flood induced delays and we demonstrate this with an example from the 28 th June 2012 flood in Newcastle upon Tyne, UK.
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