Abstract: Maritime shipping accounts for 90% of the volume of worldwide trade, making it the most important mode of cargo transportation. The global time-evolving network of cargo ship movements therefore gives an accurate representation of international trade and provides valuable insights on the development and evolution of economic relations. Here we conduct a novel longitudinal multi-layer network analysis on a unique dataset, comprising 30 years of worldwide shipping movements and making it the largest and most comprehensive of its kind. We use state-of-the-art temporal network analysis tools to study world trade from the perspectives of spatial, temporal and complex networks to uncover patterns of network growth, evolution and shifts with respect to different commodity classes. We uncover correlations between the shipping patterns of different commodities over time and investigate the extent to which these correlations can be explained by a number of different factors such as geospatial (e.g. preference to trade across shorter distances) or economic (e.g. countries’ comparative advantage policies, commodity availability and shipping tariffs) constraints. We then examine the effects of exogenous events such as the establishment or dissolution of trade agreements, embargoes and diplomatic relations has on changing the structure of the overall trade network. Moreover we investigate the structure of the overall network in order to detect possible structural weakness, conditional on the supremacy of some ports, and its resolutions.

Abstract: Visions of a walkable city will meet the objectives of much environmental legislation by reducing pollution and carbon emissions associated with highly congested city transport routes. Other desirable outcomes will emerge such as improved health and fitness through increased walking and cycling, increased personal safety through reduced accidents in highly pedestrianised areas, greater community cohesion through increased face-to-face encounters and greater vibrancy and innovation through increased diversity of contacts. But municipal planning to progressively reduce vehicle numbers in the urban environment cannot be considered in insolation from the places people need to go for jobs, and socializing; and from the goods and waste that need transporting in the urban city. Curtailing personal mobility will need to be perceived as a new measure of wealth, substituting uncertainties about congested commute times, with certainties for journey times, safety, and healthier lifestyles. City transition must reflect spatial and temporal states, i.e. the densities of different places and attractivities at different times. The spatial and temporal connectedness between origins and destinations of journeys and the wider multi-modal connectedness of the greater city will need consideration as will places to store personal vehicles. This research takes a complex systems perspective on sustainable transport planning. It identifies existing methods of transport evaluation and adapts the best method to accommodate complexity to assess whether the city is on track to meet its walkable city targets. The research then identifies areas for interventions that would help to achieve targets or adapt them. The method aims to be transferrable to other functional challenges in the city, such as sustainable energy and sustainable food systems, which are interdependent with sustainable transport. The method also aims to accommodate scenarios of city change, such as aging population, weather extremes, and smart city technologies such as the internet of things and autonomous vehicles.

Abstract: Practical application of Life Cycle Sustainability Assessment (LCSA) framework requires integration of various methods, tools, and disciplines. However, there is a lack of cohesion between environmental, social, and economics disciplines as well as methods and tools. To address these challenges, we present a complete discussion about the overarching role of systems thinking to bring tools, methods & disciplines together, and provide practical examples from the earlier studies that have employed various system-based methods. We discuss the importance of integrated system-based methods for advancement of LCSA framework in the following directions: (1) regional and global level LCSA models using multi region input-output analysis that is capable of quantitatively capturing macro-level social, environmental, and economic impacts, (2) dealing with uncertainties in LCSA results during multi-criteria decision-making process, and (3) integration of system dynamics modeling to reveal complex interconnections, dependencies, and causal relationships between sustainability indicators. We suggest that LCSA practitioners and researchers should adopt systems thinking, which is defined as the ability to see the parts of bigger mechanisms, recognizing patterns and interrelationships, and restructuring these interrelationships in more effective and efficient ways. Adoption of systems thinking can help with the dissemination of the LCSA framework, increase its applicability, and can bridge the environmental, economic, and social sciences. Developing a common system language and a shared understanding of the inherent interconnectedness and complexity of sustainable development can be very helpful for cohesion of different disciplines and adoption of systems thinking. Future direction for developing methods and tools should help the scientific community to move from approaches based on isolated disciplines towards inter/trans-disciplinarily and a holistic/systematic perspective in order to address emerging sustainability problems.

Abstract: The development trajectories of cities include a wide variety of uncertainties that challenge spatial planners and policy makers in guiding urban development towards socially desired outcomes (Albrechts, 2010; Woerkum et al., 2011). In response, ideas and concepts derived from complexity science are being explored in planning literature to develop enhanced ways of dealing with these uncertainties (Portugali, 2011; Batty, 2013). Taking a complexity science perspective, this paper presents a dynamic, time-sensitive understanding of spatial transformations that helps to clarify the interconnected and changeable nature of the underlying processes. The paper continuous by exploring an adaptive approach to planning that strengthens the capacity of urban areas to respond and incorporate to both the expected and unexpected changes these processes give rise to. The argument is made that adaptive planning first and foremost implies a focus on influencing and creating conditions, followed by attention to content and process. Keywords: Albrechts, L. (2010). More of the same is not enough!: How could strategic spatial planning be instrumental in dealing with the challenges ahead? Environment and planning B: Planning & design, 37: 1115- 1127. Batty, M. (2013). The new science of cities. Cambridge: Mit Press. Portugali, J. (2011). Complexity, Cognition and the City. Understanding Complex Systems. Berlin: Springer-Verlag. Van Woerkum, C., Aarts, N., & Van Herzele, A. (2011). Changed planning for planned and unplanned change. Planning Theory, 10(2): 144 - 160.

Abstract: We consider the observability model in networks with arbitrary topologies. We introduce a system of coupled nonlinear equations, valid under the locally tree-like ansatz, to describe the size of the largest observable cluster as a function of the fraction of directly observable nodes present in the network. We perform a systematic analysis on 95 real-world graphs and compare our theoretical predictions with numerical simulations of the observability model. Our method provides almost perfect predictions in the majority of the cases, even for networks with very large values of the clustering coefficient. Potential applications of our theory include the development of efficient and scalable algorithms for real-time surveillance of social networks, and monitoring of technological networks.

Abstract: The scientific understanding of human mobility has advanced in recent years due to the availability of digital traces including mobile phone call records, online social networks data and GPS trajectories from vehicles. There is consensus that distances and waiting times between consecutive locations in an individual’s trajectory are heavy tailed distributed. Mechanisms explaining the emergence of these statistical properties include individuals adopting Lévy-flight strategies and using different transportation modalities. The debate is, however, still open also because limited data resolution has hindered the understanding of human motion at all spatial and temporal scales. Here, we characterise mobility behaviour across an unprecedented range of scales, analysing 850 individuals’ digital traces sampled every ∼16 seconds for 25 months with ∼10 meters spatial resolution. We show that the distributions of distances and waiting times between consecutive locations are best approximated by log-normal distributions across several orders of magnitude and that natural time-scales result from the deep-routed regularity of human mobility. We find that log-normal distributions characterise also waiting times and distances between consecutive discoveries, implying that this property of human motion is not a simple consequence of its regularity and stability across time. Revealing the characteristic features of human trajectories across a wide range of spatial and temporal scales, our findings provide key elements to explain and model the fundamental mechanisms governing human mobility behaviour.

Abstract: Decision making processes on complex socio-technical systems can be seen as a game. Multiple actors with different incentives are involved. They are related to one another via a network of interdependencies. Various strategies are performed and information about the technical uncertainties of the system play a big role. Moreover, the decision making process is dynamic since these different elements change over time. This makes the decision making process unstructured and hard to predict. Game concepts used in Public Administration have the ability to describe the decision making process. Examples of these games concepts are the Multi-Issue and Hub-Spoke game. Game theory provides various examples of mathematical constructed cases which help in gaining insight in the situation. However, both Public Administration and Game theory received criticism. Public Administration lacks formalization in the sense that it cannot predict the actions actors should perform in order to reach an optimal solution. Game theory has analytic and predictive features, but highly simplifies the situation by leaving out too many details which makes it hard for the decision maker to apply the results. The paper is a step towards bridging those two disciplines by formalizing Public Administration game concepts using game theoretical tools. For a decision maker the result could provide useful knowledge about the existence of different (optimal) solutions/scenarios/strategies to steer the process. The contribution of this paper is to give a better understanding of the decision making process by providing an overview of game concepts that can be extracted from empirical railway cases. The mapping of Public Administration game concepts to game theory concepts is presented in a common framework such that analysis becomes feasible. Moreover, for each game concept an example from a decision making process in the railway sector is provided in order to illustrate the applicability.

Abstract: Understanding human mobility patterns and be able to reproduce them accurately is crucial in a wide range of applications in public health, transport and urban planning and anthropology. However, most of the studies and models proposed in the recent literature focus on long-term mobility, and, most importantly, the travel purpose and the importance given to it are rarely taken into account. Indeed, an individual is not willing to invest the same amount of time or money, more generally, the same amount of 'energy' into a travel according to the value attached to the purpose/objective of this travel. In this work, we test the assumption that it exists a relationship between the cost associated to a travel and the value given to its purpose by analyzing a credit card dataset contains information about 40 million bank card transactions made by customers of the Banco Bilbao Vizcaya Argentaria (BBVA) in the provinces of Madrid and Barcelona in 2011. We finally propose an individual human mobility patterns model which is able to explain and reproduce the properties observed in the data.

Abstract: Motor vehicle accidents contribute to over 1.2 million fatalities and are the leading cause of death for young people aged 15 to 29 years. The majority of accidents are caused by human error with an array of strategies implemented to mitigate such errors. The crash risk for young and novice drivers learning to negotiate the complexities of the road environment is considerable. In the absence of direct feedback, abhorrent driving behaviours (e.g., excess speeding, braking, acceleration) may become prevalent in the early stages of licensing and hence, prolonging the time over which they remain at high risk. The aim of this research is to prevent the adoption of early stage undesirable transitions in driving behaviour, and positively reinforce safer driving by providing direct feedback to drivers about their abhorrent behaviours. The presentation will present a novel way of using neural networks for the early identification of state transitions in an applied area of significant global importance, transport safety. Results of our research show that, using telematics data, driver behaviours can be mathematically represented by frequencies related to specific breaking, acceleration and speeding thresholds, providing a personalised profile of each driver’s use of the car’s throttle and brakes. The evolution of this profile is modelled using a Long Short-Term Memory recurrent neural network (LSTM). One LSTM per driver assesses whether a new data sample belongs to the current driver or not. Positive or negative behavioural change is identified by monitoring the prediction error and LSTM gate activity, which indicate whether the internal memory of the LSTM requires updating to accurately classify the latest instance of telematics data. Direct feedback to the driver can then be provided if the change in behaviour is negative, potentially preventing the adoption of the new behaviour and reducing deaths and injuries arising from crashes.

Abstract: The propagation of delays across airport networks, and more generally the resilience of such systems against perturbations such as bad weather, strikes or terrorist attacks, are problems of self-evident economic and social importance, as well as interesting from a theoretical point of view due to their rich complex behavior. Delay can be propagated from one flight to another when a dependency between the two flights exists because they use the same aircraft, passengers and crew members connect from one another, or more indirectly as the result of airport or airspace congestion. In this work, we characterize the delay propagation patterns in the US and European air transportation networks by following delays backwards in time to their possible original source(s). Using data obtained from the Bureau of Transportation Statics of the United States Department of Transportation and the Flightradar24 web-based service, we identify the initial sources of delay as well as the weak point of the network typically corresponding to airports affected early in the morning and in a recurrent way along the day. The analysis is performed at the flight and airport level for different seasons. Finally, the impact of intercontinental flights, which provide a coupling between the US and European networks, is also assessed.

Abstract: Each year, 1.25 million people are killed and a further 50 million are injured in road crashes. Approximately half of these deaths and injuries occur among vulnerable road users including pedestrians and cyclists. Separated cycling infrastructure that reduces physical interaction between cars and cyclists is one strategy employed by urban planners to improve safety. However, the effect of separated infrastructure on behavioural adaptation by drivers has not been extensively investigated. We constructed an agent-based model to explore the effects of introducing separated cycling infrastructure into a transportation network under conditions where simulated drivers demonstrated various levels of behavioural adaptation in responses to increased exposure to cyclists. We then compared the results generated by this model to that expected under a conventional mathematical model. The agent-based model showed that the introduction of low levels of separated cycling infrastructure provided little or no reduction in car vs cyclist crashes when behavioural adaptation was among drivers was also modelled. This finding differed from the conventional mathematical model, which predicts safety benefits at all levels of additional cycling infrastructure. The study demonstrates the importance of modelling behavioural mechanisms associated with cyclist and vehicle interaction when estimating potential safety benefits of separated cycling infrastructure. It suggests that if behavioural adaptation is a genuine mechanism contributing to cyclist safety, critical levels of separated infrastructure beyond those currently present in many highly motorized cities are likely to be required before observable reductions in cyclist deaths and injuries are realised.

Abstract: Transitions observed in the dynamical patterns of vehicular traffic, for instance, as a result of changes in traffic density, form an important class of phenomena that is sought to be explained by large-scale modeling using many interacting agents. While the dynamics of highway traffic has been the subject of intense investigation over the last few decades, there is as yet comparatively little understanding of the patterns of urban traffic. The macroscopic collective behavior of cars in the network of roads inside a city is marked by relatively high vehicular densities and the presence of signals that coordinate movement of cross-flowing traffic traveling along several directions. We have devised a novel kinetic Monte Carlo simulation approach for studying the dynamics of urban traffic congestion, allowing study of continuous-time, continuous-space traffic flow, which contrast with the dominant paradigm of cellular automata models. Well-known results of such discrete models for traffic flow in the absence of any intersections can be easily reproduced in the framework. More importantly, the behavior in the presence of an intersection where cross-flowing traffic is regulated by a signal is seen to produce novel features. The fundamental diagram of traffic flow in the presence of a signal shows a broad plateau indicating that the flow is almost independent of small variations in vehicle density for an intermediate range of densities. This is unlike the case where there are no intersections, where a sharp transition is observed between free flow behavior and jamming on changing vehicle density. The distribution of congestion times shows a power-law scaling regime over an extended range for the stochastic case when exponential-like right skewed probability distributions are used. These results are then compared with empirically observed power-law behavior in congestion time distributions for urban traffic obtained from the cities of Delhi, Bengaluru and Mumbai.

Abstract: Understanding growth patterns in complex systems of cities through modeling is an intensive branch of quantitative geography. Complex agent-based models have been recently provided promising results by multi- modeling and intensive computation for pattern discovery and calibration. However simple interaction-based extensions of seminal models of growth (such as the Gibrat model) have not yet been tested and calibrated against real datasets. We propose a spatial model of urban growth extending the Gibrat model by adding the contributions of gravity-based interactions to expected growth rates. Moments derivation for the stochastic model allows to implement a deterministic version on expectancies. Working with the Pumain-INED harmonized database for French cities (population of urban areas for 1831-1999), the 4-parameter interaction model is calibrated through intensive computation on grid, using the OpenMole software, yielding e.g. the characteristic interaction distance at different periods. We then add a second order term aimed at integrating interactions between physical transportation networks and cities, through a feedback of physical flows on traversed cities.It allows to obtain better fits and reproduce stylized facts such as hierarchy inversions and apparition of the “tunnel effect” with the development of railway network. We furthermore introduce a novel method to assess the impact of adding parameters to a simulation model on the effectively gained information, as an extension of Akaike Information Criterion to simulation models. This empirical AIC is estimated by comparing AICs for statistical models, with same parameter number, fitting best behavior space obtained by exploration. It confirms that our extension provide a gain of information on the French city system. This contribution provides a renewing insight on simple models of urban growth for system of cities, that proves to have good explicative potentialities. It also introduce a methodology to tackle the open question of quantifying overfitting in simulation models.

Abstract: In recent years, it has been shown that many seemingly unrelated natural phenomena; earthquake magnitudes, word frequency, astronomical masses and city sizes to name a few, can be asymptotically described by a small collection of empirical distributions. Of these distributions, perhaps the most prolific is Zipf’s Law. When applied to the size distribution of cities, Zipf’s Law states that the population of a city is inversely proportional to its rank and this has been shown to apply to city sizes both globally and historically. The existence of this global distribution of city sizes places a constraint on models of city growth. The most widely accepted model is proportionate random growth which constrains growth rates to be identically distributed and independent of city size. Despite proportionate random growth being the accepted mechanism behind the evolution of city sizes, there is no consensus on a model that describes the underlying stochastic processes governing city growth rates. Furthermore, it is noted that Zipf’s Law is only present in the tail of the distribution of city sizes and does not fit the distribution as a whole suggesting proportionate random growth alone is not a complete model. Here we present a model of births and deaths that is able to both reproduce Zipf’s Law in the upper tail and account for its absence in the distribution of smaller cities. We demonstrate that the observed proportionate random growth is a consequence of the interaction of these processes. The model is validated using census data on counties in the United States. Our results can be applied to other systems in which Zipf’s law arises from the interaction of underlying processes and may provide an explanation as to why this distribution occurs across such a diverse set of natural phenomena.

Abstract: Dengue fever is a multi-serotype mosquito-borne disease that is steadily increasing in incidence worldwide and sharing animal vectors with other rapidly spreading viruses like Zika virs. In an Epidemic Prediction Initiative context, a new computational method is proposed as a new approach for constructing Stochastic Generalized Linear Models (SGLM) with multiple diversely lagged input factors based on the aim to improve prediction accuracy. The proposed computational method uses mutual information (MI) to evaluate the dependencies between predictive and outcome variables at different time lags. The window with the highest MI in the time series of each predictive variable is selected as the input of a negative binomial SGLM that predicts the weekly incidence of DF. More precisely, total cases, outbreak timing and magnitude are the variables used to design the most accurate predictive model. Global Sensitivity and Uncertainty Analysis (GSUA) is applied to attribute the variability of the output to each predictive factor and their interactions. Results reflect the micro/meso ecosystem dependence on Dengue fever incidence. For instance, temperature and humidity are more important in urban settings like in San Juan; NDVI is more important in rural settings like in Iquitos, Peru. For both study sites, annual and inter-annual trends and autoregressive components are the most influential independent variables. MI allows one to construct a varied lag factor model that can both investigate the universal epidemiology of a disease and make useful and site-dependent fine-resolution predictions. Yet, the mutual information based SGLM is proposed as a powerful epidemic prediction model not just for Dengue fever but also for any other environmental dependent infectious diseases.

Abstract: Ecosystem health issues abound worldwide with environmental implications, and impact for animal and human populations. The complexity of addressing problems systemically in the policy arena on one side, and the lack of use of computational technologies for quantitative public policy on the other side have determined a worsening of ecosystem health. We propose to enhance existing adaptive management efforts with an integrated decision-analytical and environmental dynamic model that can guide the strategic selection of robust ecosystem restoration alternative plans. The model can inform the need to adjust these alternatives in the course of action based on continuously acquired monitoring information and changing stakeholder values. We demonstrate an application of enhanced adaptive management for a wetland restoration case study inspired by the Florida Everglades restoration effort. This has implication for the environment, animal and human health and embraces the sustainability paradigm quantitatively. In terms of diseases we particularly look into waterborne and water-based diseases (Zika, Dengue Chickengunya, West Nile and Yellow Fever for instance). In relation to the Everglades, we find that alternatives designed to reconstruct the pre-drainage flow may have a positive ecological and animal health impact, but may also have high operational costs and only marginally contribute to meeting other objectives such as reduction of flooding that has catastrophic human impact and morbidities in terms of deaths and infectious disease symptoms. Enhanced adaptive management allows managers to guide investment in ecosystem modeling and monitoring efforts through scenario and value of information analyses to support optimal restoration strategies in the face of uncertain and changing information. Thus, the model allows decision makers to explore the full landscape of possible scenarios before taking decisions and to dynamically design the system considering stakeholder values, economical and political constraints, ecosystem dynamics and surprises.

Abstract: Study of urban systems---how they form and develop---constitutes an important portion of human knowledge, not only because it is about our own physical space of daily living but also for understanding the underlying mechanisms of human settlement and civilisation on the Earth's surface that may be fundamentally similar to other forms of organisation like biological cells in our body or animal colonies. Among the physical features of an urban system, the complex patterns delineated by the physical locations and shapes of urban entities like buildings, parks, lakes or infrastructure can provide us with the comprehension of its current status of development or even the living condition of people inside it. In this study, we explore the spatial patterns encompassed in urban systems by analysing the pattern of spatial distribution of transport points in their public transport network of 73 cities around the world. The analysis reveals that different spatial distributions of points can be quantified and shown to belong to two main groups in which the points are either approximately equidistant or they are distributed apart with multiple length scales. The first group contains cities that appear to be well-planned, i.e. organised type, while the second consists of cities that tend to spread themselves over a large area and possess non-uniform spatial density of urban entities at different length scales, i.e. organic type. In addition to public transport network, we also look at the distribution of amenities within each city to investigate the relation between these two types of urban entity, and find that it possesses universal properties regardless of the city's spatial pattern type. This result has one important implication that at small scale of locality, the urban dynamics cannot be controlled even though the regulation can be done at large scale of the entire urban system.

Abstract: Power-grid nodes are coupled oscillators in electric power systems. In the normal operational state of power grids, the phase frequencies of the power-grid nodes are synchronized. The synchronization is self-sustained such that it recovers the synchrony against small perturbations. However, large perturbations can break the syncronization, and the synchronization stability varies for the topological position of nodes and the network parameters such as transmission strength. In this study, we investigate how the synchronization stability undergoes transition according to the network topology and the transmission strength between nodes. We track the stability transition by using Kuramoto-type model as a function of the transmission strength. Based on the transition shapes of the synchronization stability, we reveal the width of the transition curve is correlated with community consistency that represents how consistently a node associates with other nodes. In addition, we find that the transition shapes are distinguished by few patterns. Through the analysis of 598 isomorphically distinct topologies, we classify the transition patterns into four groups. Neither macro- nor micro- network characteristics well predict the transition patters. However, we find that the pathway-based nodal centrality such as betweenness is a good indicator for the synchronization stability transition.

Abstract: Interactions and movements in urban systems are controlled by their transport networks. For these systems to function efficiently, there is a need to build a robust network. We present here how we can couple land use and network structure to model the travel demand of stations in a transport network. This gives us insights as to how transport networks should evolve with changing land use patterns. We apply the model to the Singapore Rapid Transit System (RTS). We find that considering network structure and using the entropy of the gross plot ratio as the potential measure of how likely a commuter will be attracted to travel to specific areas, the model predicts the data well with a correlation of 0.76.

Abstract: Within the realm of urban studies and spatial planning, the concept of self-organization receives increasing attention in understanding spatial transformations and related planning interventions (De Roo et al, 2012; Portugali, 2011). In exploring the potential of self-organization, various scholars however introduce diverging interpretations of the concept, consequentially leading to different interpretations of what the concept of self-organization can offer to planners. In the first part of the paper, we show that these different interpretations have their foundation in two distinct epistemic positions: One is a critical-realist interpretation of complex adaptive systems (Byrne, 2005), resulting in a planning focused on pattern recognition and formulating guiding conditions (Portugali, 2011; Rauws, 2015). The other includes a post-structuralist interpretation of emerging assemblages (Cilliers, 1998; DeLanda, 2006), leading to a planning focused on personal style and situational behavior (Boonstra, 2015). Although both contribute to further explications of what self-organization can offer to planners, the potential synergies between the two epistemic positions has so far remained unexplored. Therefore, the second part of the paper explores their complementary in dealing with urban transformations and discusses how to turn them into consistency with one another – meaning how they can mutually reinforce each other without losing their individual epistemic strengths. Based on this exploration we suggest a style of spatial planning in which the planner is able to act adaptively and differentiate in style in response to the situation at stake, among others by means of pattern recognition. On a conceptual level the paper shows how planner scholars can make sense of the diversity of ongoing processes of self-organization in the context of spatial transformations.

Abstract: The planning and realization of transport infrastructure occurs in a continuously changing environment. The climate changes, our economy is circulating, environmental requirements and restrictions grow and our society becomes more energetic and participative. Dealing with these changes is a major challenge in infrastructure planning and implementation nowadays. Traditionally, infrastructure planning focused on modelling and forecasting future developments based on historical data and socio-economic scenarios. Complexity and dynamics of infrastructure and its environment are reduced to something concrete and manageable. In practice, this leads to a reactive way of working. With the emergence of complexity theory and the realization that the object of planning behaves as a complex system, a system of many actors with mutual reciprocal relationships, the emphasis in infrastructure planning is shifting from pre-scribing to creating a context that allows and stimulates variation to occur. Planning becomes more adaptive and aims the ability to develop variation and choosing the "best fits" given changing circumstances. This is a major transition in the context of traditional infrastructure planning with its specific institutional design and specific relational and contractual characteristics. Considering the infrastructure sector as a complex adaptive social system, the paper analyses adaptability from a complexity theory perspective and confronts this with current practice in (Dutch) infrastructure planning, implementation and exploitation through the analysis of cases. Focus is thereby on conditions that allow variation through interaction between related actors in the system. As most important relations, the public-public and the public-private relationships are analysed. From the confrontation of theory and practice dilemmas for further discussion are formulated and recommendations are made - to infrastructure authorities and markets involved in infrastructure development - how to facilitate the above described transition from the traditional technical-rational planning to a more adaptive planning.

Abstract: Throughout the last decades, several simulation models have been proposed in an attempt to reproduce aircraft evacuation scenarios and provide an alternative to real certification trials. Furthermore, simulation models have been proven to be a useful tool when designing new aircraft enclosures. Among these, the airEXODUS model has seen widespread application and validation, being successful in predicting past certification trials and examining issues related to aircraft enclosure layout. This work introduces EvacSafeX, a multi-agent model centered around proper representation of human behaviour in aircraft evacuation scenarios. EvacSafeX finds its inspiration in complex systems and in the airEXODUS model, seeking to make complex interactions and behaviour emergence from individual modeling of human passengers. The model also takes novel approaches to represent human behaviour and passengers movement along the aircraft cabin. EvacSafeX adopts a perception-action approach to represent agent's capabilities, following a behavioral model defined by a rule list brain. Passengers are also characterised by both physical and psychological attributes identified as the most relevant in evacuation scenarios. In addition to the many components already included in the model, its generic architecture allows other ones to be easily incorporated. The components implemented beforehand in EvacSafeX prototype were verified through a set of validation experiments. At first sight, it was observed a significant sensitivity of some passenger's personal attributes, representative of their influence in real life cases. Furthermore, the proposed model demonstrated a high flexibility and diversity in the representation of passenger's behaviour, leading to an emergence of several different phenomena observed in real evacuation scenarios. Finally, promising results were obtained in an attempt to reproduce real certification demonstrations results and other experiments conducted with state of the art models.

Abstract: Railways are a key infrastructure for any modern country, so that their state of development has even been used as a significant indicator of a country's economic advancement. Moreover, their importance has been growing in the last decades either because of the growing Railway Traffic and to governments investments, aiming at exploiting railways means to reduce CO2 emissions and hence global warming. To the present day, many extreme events (i.e. major disruptions and large delays compromising the correct functioning of the system) occurs on a daily basis. However these phenomena have been approached, so far, from a transportation engineering point of view while a general theoretical understanding is still lacking. A better comprehension of these critical situation from a theoretical point of view could be undoubtedly useful in order to improve traffic handling policies. In this work we move toward this comprehension by proposing a model about train dynamics on railways network aiming to unveil how delays spawn and spread among the network. Inspired by models for epidemic spreading, we model the diffusion of delays among train as the diffusion of a contagion among a population of moving individuals. We built and tested our model using two large dataset about Italian and German railway traffic, collected using APIs intended to give passengers information about the trains, the state of the service and train delays. The model reproduces adequately delays dynamics in both systems, meaning that it captures the underlying key factors. In particular, our model predicts that the insurgence of clusters of stations with large delays is not due to external factors, but mainly to the interaction between different trains. Also, through our model is capable to give a quantitative account of the difference between the two considered railway systems in terms of probability of contagion and delays dynamics.

Abstract: Recent studies of human mobility largely focus on displacements patterns. Power-law fits of empirical long-tailed distributions of distances have been associated to scale-free super-diffusive random walks called Levy flights. However, drawing conclusions about a complex system from a fit, without any further knowledge of the underlying dynamics, might lead to erroneous interpretations. We show on a dataset describing the trajectories of 780,000 private vehicles in Italy, that the Levy flight model cannot explain the behavior of travel-times and speeds. We therefore introduce a new class of accelerated random walks, validated by empirical observations, where the velocity changes due to acceleration kicks at random times. Combining this mechanism with an exponentially decaying distribution of travel-times leads to a short-tailed distribution of distances which could indeed be mistaken with a truncated power-law. These results illustrate the limits of purely descriptive models and provide a mechanistic view of human mobility.

Abstract: This paper studies the coevolution of opinion dynamics in growing networks with attachment rule that depends on the opinion updating process. We propose that individuals choose to link with others according to the Hegselmann-Krause opinion dynamics model; each individual forms its neighborhood with others whose opinions are close to its own in an interval minor to some confidence level. Since individuals hold an opinion value in the continuous interval [0,1], then for a new agent on the network, the neighborhood will depend not only on her confidence level but also on her initial opinion value. We analyze the network structure when the initial opinion value is selected with: i) an uniform probability, ii) a probability as a function of the degree of the new agent, and iii) a probability as a function of the cluster coefficient of the new agent. Since the confidence value and the initial opinion selection influence the network structure, we then present a method to approximate the degree distribution and the number of cluster based on these two variables. In order to complete the coevolution analysis, we also study the convergence of opinions. When a new agent is added to the network, the opinion update for all individual process could take place immediately (all agents change their opinion as the average of their neighborhood) or could present a delay (agents change their opinion when they detect a variation in at least one of their neighbor opinion value). We then demonstrate that the convergence is affected by the confidence level and the initial opinion value selection, but the convergence time depends on when the updating process occurs.

Abstract: Variations in seasonal influenza epidemic initiation, timing, and magnitude yield highly variable illness data that can help researchers to understand the spatial spread of influenza. For the United States, predictable spatial patterns will contribute to more accurate predictive models for ascertaining when influenza infection will occur and to understand long distance connections. In order to evaluate the interdependence of cases we propose the use of a transfer entropy model (TE) that measures the amount of information transfer from one variable to the other; yet, in this context the number of cases ‘’transferred’’ from one region to another. TE is a non-parametric and non-linear model that offers an alternative measure of effective connectivity based on information theory, more powerful than Granger causality or assumption-based dynamic causal models. More precisely, TE is quantifying causal networks between time series where node/variable distance, node connectivity, and link weights are related to variable undirected statistical closeness, dependence, and directional entropy reduction. Furthermore, transfer entropy is an asymmetric measure that conveys directional information. Considering TE on CDC data it results that Northeast and Northwest US are the most influential nodes in the network. Conversely, Midwest and Southwest regions are strongly affected by other regions. There are long-distance connections between Northeast and Midwest, and between Mid-Atlantic and Southwest regions. Some pairs regions that are very far from each other (~1500-3000km) still show significant correlation with each other (r=0.45-0.65) that emphasizes the importance to assess effective connections rather than geographical connections. The results allow us to conclude that long-distance effects are relevant in the dispersion of influenza cases and to infer locally generated cases. The TE model can be useful in analyzing any other complex disease where interactions among sub-systems/regions are expected to be non-linear and where minimal a priori knowledge is available.