An Intelligent Advisor for City Traffic Policies

Nowadays, city streets are populated not only by private vehicles but also by public transport, fleets of workers, and deliveries. Since each vehicle class has a maximum cargo capacity, we study in this article how authorities could improve the road traffic by endorsing long term policies to change the different vehicle proportions: sedans, minivans, full size vans, trucks, and motorbikes, without losing the ability of moving cargo throughout the city. We have performed our study in a realistic scenario (map, road traffic characteristics, and number of vehicles) of the city of Malaga and captured the many details into the SUMO microsimulator. After analyzing the relationship between travel times, emissions, and fuel consumption, we have defined a multiobjective optimization problem to be solved, so as to minimize these city metrics. Our results provide a scientific evidence that we can improve the delivery of goods in the city by reducing the number of heavy duty vehicles and fostering the use of vans instead.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish MINECO and FEDER projects TIN2014-57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. University of Malaga, Andalucia TECH. Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish MECD. Christian Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO

Competing biosecurity and risk rationalities in the Chittagong poultry commodity chain, Bangladesh

This paper anthropologically explores how key actors in the Chittagong live bird trading network perceive biosecurity and risk in relation to avian influenza between production sites, market maker scenes and outlets. They pay attention to the past and the present, rather than the future, downplaying the need for strict risk management, as outbreaks have not been reported frequently for a number of years. This is analysed as ‘temporalities of risk perception regarding biosecurity’, through Black Swan theory, the idea that unexpected events with major effects are often inappropriately rationalized (Taleb in The Black Swan. The impact of the highly improbable, Random House, New York, 2007). This incorporates a sociocultural perspective on risk, emphasizing the contexts in which risk is understood, lived, embodied and experienced. Their risk calculation is explained in terms of social consent, practical intelligibility and convergence of constraints and motivation. The pragmatic and practical orientation towards risk stands in contrast to how risk is calculated in the avian influenza preparedness paradigm. It is argued that disease risk on the ground has become a normalized part of everyday business, as implied in Black Swan theory. Risk which is calculated retrospectively is unlikely to encourage investment in biosecurity and, thereby, points to the danger of unpredictable outlier events

Collision Mortality Has No Discernible Effect on Population Trends of North American Birds

Avian biodiversity is threatened by numerous anthropogenic factors and migratory species are especially at risk. Migrating birds frequently collide with manmade structures and such losses are believed to represent the majority of anthropogenic mortality for North American birds. However, estimates of total collision mortality range across several orders of magnitude and effects on population dynamics remain unknown. Herein, we develop a novel method to assess relative vulnerability to anthropogenic threats, which we demonstrate using 243,103 collision records from 188 species of eastern North American landbirds. After correcting mortality estimates for variation attributable to population size and geographic overlap with potential collision structures, we found that per capita vulnerability to collision with buildings and towers varied over more than four orders of magnitude among species. Species that migrate long distances or at night were much more likely to be killed by collisions than year-round residents or diurnal migrants. However, there was no correlation between relative collision mortality and long-term population trends for these same species. Thus, although millions of North American birds are killed annually by collisions with manmade structures, this source of mortality has no discernible effect on populations

Sequential refined partitioning for probabilistic dependence assessment

Modelling dependence probabilistically is crucial for many applications in risk assessment and decision making under uncertainty. Neglecting dependence between multivariate uncertainties can distort model output and prevent a proper understanding of the overall risk. Whenever relevant data for quantifying and modelling dependence between uncertain variables is lacking, expert judgement might be sought to assess a joint distribution. Key challenges for the use of expert judgement for dependence modelling are over- and under specification. An expert can sometimes provide assessments which are not consistent with any probability distribution (over specification), and on the other hand, without making very restrictive parametric assumptions an expert cannot fully dene a full probability distribution (under specification). The Sequential Refined Partitioning method addresses over- and under specification whilst allowing for flexibility about which part of a joint distribution is assessed and its level of detail. Potential over specification is avoided by ensuring low cognitive complexity for experts through eliciting single conditioning sets and by offering feasible assessment ranges. The feasible range of any (sequential) assessment can be derived by solving a linear programming problem. Under specification is addressed by modelling the density of directly and indirectly assessed distribution parts as minimally informative given their constraints. Hence, our method allows for modelling the whole distribution feasibly and in accordance with experts' information. A non-parametric way of assessing and modelling dependence flexibly in such detail has not been presented in the expert judgement literature for probabilistic dependence models so far. We provide an example of assessing terrorism risk in insurance underwriting

Comparative Coastal Risk Index (CCRI): A multidisciplinary risk index for Latin America and the Caribbean

As the world's population grows to a projected 11.2 billion by 2100, the number of people living in low-lying areas exposed to coastal hazards is projected to increase. Critical infrastructure and valuable assets continue to be placed in vulnerable areas, and in recent years, millions of people have been displaced by natural hazards. Impacts from coastal hazards depend on the number of people, value of assets, and presence of critical resources in harm's way. Risks related to natural hazards are determined by a complex interaction between physical hazards, the vulnerability of a society or social-ecological system and its exposure to such hazards. Moreover, these risks are amplified by challenging socioeconomic dynamics, including poorly planned urban development, income inequality, and poverty. This study employs a combination of machine learning clustering techniques (Self Organizing Maps and K-Means) and a spatial index, to assess coastal risks in Latin America and the Caribbean (LAC) on a comparative scale. The proposed method meets multiple objectives, including the identification of hotspots and key drivers of coastal risk, and the ability to process large-volume multidimensional and multivariate datasets, effectively reducing sixteen variables related to coastal hazards, geographic exposure, and socioeconomic vulnerability, into a single index. Our results demonstrate that in LAC, more than 500,000 people live in areas where coastal hazards, exposure (of people, assets and ecosystems) and poverty converge, creating the ideal conditions for a perfect storm. Hotspot locations of coastal risk, identified by the proposed Comparative Coastal Risk Index (CCRI), contain more than 300,00 people and include: El Oro, Ecuador; Sinaloa, Mexico; Usulutan, El Salvador; and Chiapas, Mexico. Our results provide important insights into potential adaptation alternatives that could reduce the impacts of future hazards. Effective adaptation options must not only focus on developing coastal defenses, but also on improving practices and policies related to urban development, agricultural land use, and conservation, as well as ameliorating socioeconomic conditions