Abstract
AbstractThe impacts of weather and climate on infrastructure are numerous: snow and ice on roads, railway buckling, leaves on the line, wind impacts on power cabling, etc. Advances in modeling mean that these impacts can now be predicted at a high resolution so that mitigation activities can be targeted at vulnerable sections of the infrastructure network.However, while high-resolution models have been in operational use for the last decade, in an environment of increasing litigation, practitioners remain nervous about making mitigation decisions solely based on model output. This means that the verification of forecasts is now needed on a scale previously not required, and it is only with this step that end users will become more open to using risk-based methods (e.g., decision support systems that enable selective salting for winter road maintenance where only the coldest sections of road are treated or localized rail speed restrictions in hot weather as opposed to the blanket restrictions currently used).However, existing monitoring techniques are simply not capable of producing this information. Traditional in situ measurements are too expensive to install in the numbers required and therefore lack the spatial resolution. Conversely, mobile measurements lack the temporal resolution to provide the full picture. This paper outlines how the emerging Internet of Things is starting to provide the enabling technology to saturate our infrastructure with low-cost sensors. In doing so, it will provide unprecedented monitoring of weather impacts as well as facilitating a new generation of products harnessing the benefits of high-resolution observations.
Highlights
Two use cases are presented for winter road maintenance and seasonal resilience on the railways to showcase the potentially transformative impact of the Internet of Things on observations and forecasting
Over the last two decades, developments in modeling [e.g., route-based forecasting for winter road maintenance (Chapman and Thornes 2006)] and decision support systems [e.g., maintenance decision support system (MDSS); Petty and Mahoney 2008] mean that weather impacts can be predicted at high resolution so that mitigation activities can be targeted at vulnerable sections of infrastructure
It is not uncommon to encounter a range in surface temperatures of 30°C on the railway network in high summer (Chapman et al 2006) or 10°C on the road network in winter (Shao et al 1997)
Summary
Two use cases are presented for winter road maintenance and seasonal resilience on the railways to showcase the potentially transformative impact of the Internet of Things on observations and forecasting. Over the last two decades, developments in modeling [e.g., route-based forecasting for winter road maintenance (Chapman and Thornes 2006)] and decision support systems [e.g., maintenance decision support system (MDSS); Petty and Mahoney 2008] mean that weather impacts can be predicted at high resolution so that mitigation activities can be targeted at vulnerable sections of infrastructure. This helps to minimize the cost of interventions while reducing disruption but, more importantly, secures the networks for continued use.
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