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An ecologic analysis was conducted to explore the correlation between air pollution, and COVID-19 cases and fatality rates in London. The analysis demonstrated a strong correlation (R 2 >0.7) between increment in air pollution and an increase in the risk of COVID-19 transmission within London boroughs. Particularly, strong correlations (R 2 >0.72) between the risk of COVID-19 15 fatality and NO2 and PM2.5 pollution concentrations were also found. Although this study assumed the same level of air pollution across a particular London borough, it demonstrates the possibility to employ air pollution as an indicator to rapidly identify the city's vulnerable regions. Such an approach can inform the decisions to suspend or reduce the operation of different public transport modes within a city. The methodology and learnings from the study can thus aid in public 20 transport's response to COVID-19 outbreak by adopting different levels of human-mobility reduction strategies based on the vulnerability of a given region.

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Scour is one of the main causes of bridge failures resulting in significant macroeconomic impacts, often beyond the direct costs of infrastructure damage. Given the pressure to increase the resilience of transport networks, ageing bridge infrastructure, constrained budgets, variable knowledge of asset conditions and limited data, mixed ownership and operation of bridges, and concerns about the risks of climate change, there is a need to implement cost-effective monitoring and maintenance strategies. To this end, this study aims to set the scene for a risk-informed approach for tackling bridge scour, while considering the socioeconomic impacts of disruptions due to bridge failures or closures. This study reviews the current practices in predicting, monitoring and managing bridge scour. It discusses the development of a risk-informed approach to aid the whole-life appraisal of bridges while considering the direct and indirect costs associated with bridge failure or closures. The approach provides a rational means to enable asset managers to evaluate the factors that affect bridge failure risk, select and prioritise appropriate mitigation measures, thereby improving the allocation of scarce monitoring and maintenance resources.

Railway track infrastructure asset management is a challenging problem with added values on safety, society and environment. With railways serving as a key sustainable mode of transportation for passengers and freight, the industry is facing an increasing demand to expand its capacity, availability and speed, resulting in faster deterioration of the ageing railway track infrastructure. Given the constrained maintenance budgets and the environmental challenges posed by climate change, railway asset managers have to identify economically and environmentally justifiable track maintenance strategies without compromising on safety. To this end, this paper proposes a risk-informed approach to arrive at sustainable railway track maintenance strategies while considering the associated track maintenance costs and impacts on train operation (environmental emissions and risk of derailments). Monte Carlo simulation is employed to address data uncertainties associated with track quality data, the costs and benefits of track maintenance and train operation. The proposed approach is successfully applied to the heavy-haul railway lines in Sweden and Australia to compare some alternative maintenance strategies and identify the sustainable one.

The provision of safe, efficient, reliable and affordable railway transport requires the railway track infrastructure to be maintained to an appropriate condition. Given the constrained budgets under which the infrastructure is managed, maintenance needs to be predicted in advance of track failure, prioritized and identified risks and uncertainties need to be considered within the decision-making process. This paper describes a risk-informed approach that can be used to economically justify railway track infrastructure conditions by comparing on a life-cycle basis infrastructure maintenance costs, train operating costs, travel time costs, safety, social and environmental impacts. The approach represents a step-change for the railway industry as it will enable economic maintenance standards to be derived which considers the needs of the infrastructure operator, but also those of users, train operating companies and the environment. Further, the risk-informed capability of the tool enables asset managers to deal with uncertainties associated with forecasting costs and the effects of track maintenance, and unavailability of data. The Monte Carlo simulation technique and a Fuzzy reasoning approach are used to address safety data uncertainties through probabilistic risk assessment allied to expert opinion. The approach is illustrated using data from three routes on the UK mainline railway network. The results demonstrate that the approach can be used to support strategic and tactical levels of railway asset management to inform plausible design and maintenance strategies that realise the maximum benefit for the available budget.

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