This dissertation focuses on a comparative study of small-world characteristics in geographical, epidemic, and virtual spaces. Small-world network is the major component of the “new science of networks” that emerged recently in research related to complex networks. It has shown a great potential to model the complex networks encountered in geographical studies. This dissertation, in an attempt to understand the emergence of small-world phenomenon in spatial networks, has investigated the smallworld properties in aforementioned three spaces. Specifically, this dissertation has studied roadway transportation networks at national, metropolitan, and intra-city scales via network autocorrelation methods to investigate the distance effect on the emergence of small-world properties. This dissertation also investigated the effect of small-world network properties on the epidemic diffusion and different control strategies through agent-based simulation on social networks. The ASLevel Internet in the contiguous U.S. has been studied in its relation between local and global connections, and its correspondence with small-world characteristics. Through theoretical simulations and empirical studies on spatial networks, this dissertation has contributed to network science with a new method – network autocorrelation, and better understanding from the perspective of the relation between local and global connections and the distance effect in networks. A small-world phenomenon results from the interplay between the dynamics occurring on networks and the structure of networks; when the influencing distance of the dynamics reaches to the threshold of the network, the network will logically emerge as a small-world network. With the aid of numerical simulation a small-world network has a large number of local connections and a small number of global links. It is also found that the epidemics will take shorter time period to reach largest size on a small-world network and only particular control strategy, such as targeted control strategy, will be effective on smallworld networks. This dissertation bridges the gap between new science of networks and the network study in geography. It potentially contributes to GIScience with new modeling strategy for representing, analyzing, and modeling complexity in hazards prevention, landscape ecology, and sustainability science from a network-centric perspective.

The present thesis is devoted to an application of the ideas of complex networks theory for analysing, modelling, and, finally, optimising different processes that occur in transportation networks.

This research examines how social networking fosters the mobility of young people in a rural Spain. Generally, rural areas have been overlooked in the discourse on Globalization and Network Society, which is the foundation of the concept of “linked city”. Although many scholars have highlighted the direct link between the increase in the modes of communication of people and the increase of any kind of interaction, face-to-face included, it is necessary to stress that most of these studies are conducted in urban context where a certain grade of efficient transport exists. This study provides an approach to the impact of mediated communication on the lives of people in villages. Based on the concept of Digital Natives this study addresses the Social Network use of young people in a determined rural area in Spain and its correlation with the aim of mobility of the respondents. The results bring to light a certain degree of correlation between the increase of interaction via the Internet and the wish of mobility. Besides, this study uncovers a transportation shortage among locations in this rural area that forces young people to reduce the face-to-face interactions around specific nodes (e.g. High School or a near big city). Finally this study stresses the need for improvement of the transportation networks in terms of cost, flexibility, functionality and reach among rural population in order to avoid a cultural, economic and social backwardness in comparison to urban environments.

First issue that this thesis tries to address is setting up a practical framework of foliated transportation network. Scope of this thesis limited to several cities in Sweden to illustrate that this organization can be set up and it can perform under corresponding regulations. Beside the possibility of performance it was of high importance to consider its positive effects on whole network factors from both social and industrial perspectives. For this purpose a comparison between network results for different truck fill rates carried out but practical use of results is completely dependent of the stakeholder who is going to utilize them.

This dissertation is concerned with travel time uncertainty in transportation networks due to ephemeral phenomena such as incidents or poor weather. Such events play a major role in nonrecurring congestion, which is estimated to comprise between one-third and one-half of all delay on freeways. Although past research has considered many individual aspects of this problem, this dissertation is unique in bringing a comprehensive approach, beginning with study of its causes, moving to discussion of its effects on traveler behavior, and then demonstrating how these models can be applied to mitigate the effects of this uncertainty. In particular, two distinctive effects of uncertainty are incorporated into all aspects of these models: nonlinear traveler behavior, encompassing risk aversion, schedule delay, on-time arrival, and other user objectives that explicitly recognize travel time uncertainty; and information and adaptive routing, where travelers can adjust their routes through the network as they acquire information on its condition. In order to accurately represent uncertain events in a mathematical model, some quantitative description of these events and their impacts must be available. On freeways, a large amount of travel data is collected through intelligent transportation systems (ITS), although coverage is far from universal, and very little data is collected on arterial streets. This dissertation develops a statistical procedure for estimating probability distributions on speed, capacity, and other operational metrics by applying regression to locations where such data is available. On arterials, queueing theory is used to develop novel expressions for expected delay conditional on the signal indication. The effects of this uncertainty are considered next, both at the individual (route choice) and collective (equilibrium) levels. For individuals, the optimal strategy is no longer a path, but an adaptive policy which allows for flexible re-routing as information is acquired. Dynamic programming provides an efficient solution to this problem. Issues related to cycling in optimal policies are examined in some depth. While primarily a technical concern, the presence of cycling can be discomforting and needs to be addressed. When considering collective behavior, the simultaneous choices of many self-optimizing users (who need not share the same behavioral objective) can be expressed as the solution to a variational inequality problem, leading to existence and uniqueness results under certain regularity conditions. An improved policy loading algorithm is also provided for the case of linear traveler behavior. Finally, three network improvement strategies are considered: locating information-providing devices; adaptive congestion pricing; and network design. Each of these demonstrates how the routing and equilibrium models can be applied, using small networks as testbed locations. In particular, the information provision and adaptive congestion pricing strategies are extremely difficult to represent without an adaptive equilibrium model such as the one provided in this dissertation. / text

La popularité des plateformes mobiles telles que smartphones et tablettes génère un volume croissant de données à transférer. La principale raison de cette croissance est l'accès simplifié aux contenus vidéo sur ces plateformes. La future génération (5G) de téléphonie mobile est en cours de développement et a pour objectif d'offrir une bande passante suffisante pour de tels volumes de données. Néanmoins, un déploiement en masse de la 5G n'est pas envisagé avant 2020. De plus, la croissance est telle qu'il sera forcément intéressant de développer des solutions alternatives et complémentaires capables de délester le réseau cellulaire. L'exemple actuel le plus représentatif est le délestage de données cellulaires vers des réseaux d'accès WiFi par les principaux opérateurs mobiles. Dans ce contexte, nous proposons de déployer un nouveau réseau de contenus qui s'appuie sur les réseaux de transports publics urbains. Cette solution déploie des bornes sans-fil dans les bus et sur certaines stations de bus pour offrir du contenu aux passagers des bus. Les bus enregistrent et transportent les données, et se comportent donc comme des mules qui peuvent s'échanger des données dans certaines stations de bus. L'ensemble des bus créé un réseau de transport de données tolérantes au délai telles que de la vidéo à la demande. La création d'un tel réseau soulève de nombreuses questions. Les questions traitées dans les trois parties de cette thèse sont les suivantes: (i) le choix des stations de bus sur lesquelles une borne sans-fil doit être déployée, (ii) le choix du protocole de routage des données, (iii) la gestion efficace de la contention dans les stations et enfin (iv) la réduction du coût d'une telle infrastructure. La première partie de la thèse présente notre réseau de contenu dont l'objectif principal est de transporter de larges volumes de données. Nous montrons pour cela qu'il suffit de déployer des bornes sans-fil aux terminus des lignes de bus. Ce résultat provient de l'analyse des réseaux de transports publics des villes de Toulouse, Helsinki et Paris. Connaissant les horaires et la topologie de ces réseaux de transports, nous proposons de pré-calculer les routes pour transmettre les données dans ce réseau. Nous montrons que ce routage statique permet de réduire drastiquement le nombre de réplications de messages quand on le compare à un routage épidémique. La seconde contribution de cette thèse s'intéresse à l'échange des messages au niveau des bornes sans-fil déployées aux terminus des lignes de bus. En effet, les protocoles d'accès actuels partagent équitablement la bande passante entre les bus et le point d'accès. Dans notre cas, il en résulte une congestion importante que nous proposons de résoudre en introduisant un codage réseau XOR de proche en proche. Les flux qui se croisent sont alors combinés par la borne. Les bus transportent des paquets codés qui seront décodés au prochain saut par la borne suivante. Une analyse théorique de ce mode de communication montre que la probabilité de réception des messages peut-être augmentée au maximum de 50% et la surcharge diminuée au maximum de 50%. Pour les 3 villes européennes considérées, nous montrons par simulation que ce protocole permet d'augmenter de 35% à 48% le nombre de messages reçus. La dernière partie de cette thèse a pour objectif de réduire le coût de déploiement d'une telle architecture. Elle classifie les terminus des lignes de bus en trois ensembles qui sont équipés par des bornes sans fil de nature différentes. Les résultats de simulation montrent que pour les trois villes il est possible de garantir la connectivité de bout-en-bout tout en réduisant les coûts de déploiement d'un facteur 3. Cette architecture, dénommée 3-tier, transporte 30% plus de messages que le déploiement basique proposé en première partie. Nous montrons qu'il est possible de décharger un grand volume de données avec notre architecture. Par exemple, pour Paris, notre architecture permet de / Mobile data traffic is increasing at an exponential rate with the proliferation of mobile devices and easy access to large contents such as video. Traffic demand is expected to soar in the next 5 years and a new generation of mobile networks (5G) is currently being developed to address the looming bandwidth crunch. However, significant 5G deployments are not expected until 2020 or even beyond. As such, any solution that offloads cellular traffic to other available networks is of high interest, the main example being the successful offloading of cellular traffic onto WiFi. In this context, we propose to leverage public transportation networks (PTNs) created by regular bus lines in urban centers to create another offloading option for delay tolerant data such as video on demand. This PhD proposes a novel content delivery infrastructure where wireless access points (APs) are installed on both bus stops and buses. Buses act as data mules, creating a delay tolerant network capable of carrying content users can access while commuting using public transportation. Building such a network raises several core challenges such as: (i) selecting the bus stops on which it is best to install APs, (ii) efficiently routing the data, (iii) relieving congestion points in major hubs and (iv) minimizing the cost of the full architecture. These challenges are addressed in the three parts of this thesis. The first part of the thesis presents our content delivery infrastructure whose primary aim is to carry large volumes of data. We show that it is beneficial to install APs at the end stations of bus lines by analyzing the publicly available time tables of PTN providers of different cities. Knowing the underlying topology and schedule of PTNs, we propose to pre-calculate static routes between stations. This leads to a dramatic decrease in message replications and transfers compared to the state-of-the-art Epidemic delay tolerant protocol. Simulation results for three cities demonstrate that our routing policy increases by 4 to 8 times the number of delivered messages while reducing the overhead ratio. The second part of the thesis addresses the problem of relieving congestion at stations where several bus lines converge and have to exchange data through the AP. The solution proposed leverages XOR network coding where encoding and decoding are performed hop-by-hop for flows crossing at an AP. We conduct a theoretical analysis of the delivery probability and overhead ratio for a general setting. This analysis indicates that the maximum delivery probability is increased by 50% while the overhead ratio is reduced by 50%, if such network coding is applied. Simulations of this general setting corroborate these points, showing, in addition, that the average delay is reduced as well. Introducing our XOR network coding to our content delivery infrastructure using real bus timetables, we demonstrate a 35% - 48% improvement in the number of messages delivered. The third part of the thesis proposes a cost-effective architecture. It classifies PTN bus stops into three categories, each equipped with different types of wireless APs, allowing for a fine-grained cost control. Simulation results demonstrate the viability of our design choices. In particular, the 3-Tier architecture is shown to guarantee end-to-end connectivity and reduce the deployment cost by a factor of 3 while delivering 30% more packets than a baseline architecture. It can offload a large amount of mobile data, as for instance 4.7 terabytes within 12 hours in the Paris topology.

There is a growing need to address the effects of roadway presence on wildlife. Not only do roads directly impact gene dispersal from a movement perspective, but they limit movement of the individual animal from a habitat perspective by presenting an artificial barrier between one area of viable habitat and another. For this reason it is becoming increasingly important to quantify contact between humans and wildlife and to develop better methods for mitigating these types of conflicts. Studying habitat connectivity and animal mobility in the context of roads can provide actionable information on how, where, and when these encounters might occur in order to minimize the effects transportation networks have on wildlife. This study uses two different approaches for studying wildlife-road interactions: (1) quantifying habitat fragmentation caused by roads and (2) directly quantifying wildlife interaction with roadways. This was achieved through the development and extension of methods found in the fields of landscape ecology and time geography. First, this study demonstrates the utility of one newly created road-based landscape metric through a detailed case study via the creation of an original ArcGIS toolbox. Second, this study develops a new time-geographic methodology to probabilistically measure and predict where wildlife interactions are most likely to occur on road networks. Additionally, it is important to ensure these methods not only quantify effects of roads from habitat and movement perspectives but can be used to mitigate these conflicts in real world conservation settings. Each of these approaches individually leverages techniques found in the field of spatial optimization to strategically locate wildlife crossing structures. This study developed two new methodologies to quantify where, when, and how wildlife interactions with roads are most likely to occur: the first using road-based landscape metrics and the second using a probabilistic voxel-based time-geographic approach. To address habitat connectivity issues, one road-based landscape metric was validated on a real world data set and further advanced by developing a GIS-based tool for real world applications. Utilizing landuse and roadway layers in combination with user specified parameters, the script tools developed here readily calculate this road-based landscape metric for a given study area. To address wildlife mobility issues, probabilistic space-time prisms were used to quantify interaction probabilities between wildlife and roads. These prisms were generated for a given set of tracking points and overlaid with an intersecting roads layer in GIS. Summing the probabilities at prism-roadway intersections revealed a pattern in the likelihood of animal-roadway interactions. Finally, each method was expanded to capture habitat fragmentation and animal movement in the presence of roads over large spatial scales using location analysis techniques. This research also develops and implements new methods that explicitly address wildlife-road interactions and aid in siting potential wildlife crossing structures. Since this study directly addresses effects of roadway presence on wildlife, the techniques developed here offer an alternative approach versus existing methods from a habitat and wildlife movement perspective. These methods can aid planners in the conservation of wildlife whose habitat has been impacted by road development by identifying and targeting areas of high impact.

Landscape metrics measure the composition and configuration of habitats within landscapes; often the goal is to measure fragmentation. While a variety of existing metrics characterize the connectivity and contiguity of habitat patches, most do not explicitly consider the fragmenting effects of roads in their formulations. This research develops a set of new landscape metrics that explicitly quantify how roads disconnect and break apart habitat patches. This research introduces the following four metrics to consider the fragmenting effects of transportation networks: (1) Number of Connected Patches, a measure of connectivity; (2) Euclidean Nearest Neighbor-Roads, a measure of proximity; (3) Road Density, a measure of dispersion, and (4) Distance to Roads, a measure of division. Each of these formulations explicitly considers the presence of roadways. The metrics are applicable at three spatial scales: patch, class, and landscape. Number of Connected Patches (NCP) provides a new roadway-sensitive measure of patch connectivity by computing the number of patches of identical cover type in a landscape that can be traversed on the shortest straight line distance between them without crossing a road. Euclidean Nearest Neighbor-Roads (ENNR) calculates the distance to the Euclidean nearest neighbor of a patch of the same cover type that is not separated by a roadway. Road Density (RD) leverages the ratio of total roadway network length intersecting a patch, class, or landscape versus respective total unit area. Distance to Roads (DR) provides a new measure of division by taking the shortest Euclidean distance in meters of any patch to the nearest roadway. The performance of the new metrics is evaluated using simulated landscape data with different transportation network structures and habitat patch configurations. This is accomplished by comparing output from the road-based metrics to existing metrics that quantify habitat density, isolation, dispersion, and division. The results of the study demonstrate that the new road-based landscape metrics provide an improved means of quantifying habitat fragmentation caused by transportation networks. This is especially evident as simulated transit network increases for each landscape; response of new metrics to increased road presence is linear and as expected given metric design. These metrics have successfully captured notable patch, class, and landscape level characteristics and their associated responses which are not available with treatment by conventional measures of landscape fragmentation.

Transportation agencies face a challenging task to repair damaged roads in an aging transportation network with limited funding. In addition, the funding gap is forecasted to continue widening, which has direct impacts on the performance of surface transportation networks and the nation’s economy in the long run. Recently, transportation agencies were required by a newly enacted law to include national performance-based goals, such as environmental sustainability, in their programming and planning efforts for highway repair and rehabilitation. Therefore, the current practice in the area of highway rehabilitation planning is inadequate to handle this task and new practices are needed to improve the performance of transportation networks while maintain the national goal of maximizing environmental sustainability. Accordingly, this dissertation presents an innovative environmental-based decision-support model for planning highway construction programs. The model is developed in three main parts that are designed to: (1) model total vehicle fuel consumption and public benefits/costs of traveling on transportation networks; (2) evaluate the economic and environmental impacts of highway rehabilitation efforts; and (3) develop a multi-objective optimization model to identify and evaluate highway rehabilitation program(s) that are capable of simultaneously minimizing environmental impact and maximizing public benefits of rehabilitation decisions. First, mathematical models were developed to facilitate estimating the total vehicle fuel consumption and public benefits/cost for road users at the network-level. These models are deigned to estimate vehicle fuel consumption rate, tire depreciation cost, and vehicle repair and maintenance cost rate, in terms of major vehicle–road interaction factors, such as vehicle type, speed, and pavement conditions. The developed and statistically validated models are then used to estimate total vehicle fuel consumption and public benefits/costs at the network-level. Second, a new model was developed for evaluating the impact of decision making in highway rehabilitation efforts on greenhouse gas emissions and public travel costs. The model has the capabilities of: (1) identifying candidate rehabilitation treatment alternatives for damaged or aging pavement; (2) evaluating the impact of these treatments on pavement performance; (3) estimating network fuel consumption due to highway rehabilitation decisions; (4) estimating additional public costs as a result of travel-delay during road construction operations; and (5) evaluating the impact of rehabilitation efforts on public benefits expressed as expected savings in road user costs. Third, a multi-objective optimization model was developed to search for and identify highway rehabilitation programs that are capable of minimizing environmental impact in terms of CO2 emissions while maximizing public benefits under budget constraints. This newly developed model enables planners and decision makers to design and implement highway rehabilitation programs that are cost-effective and environmentally-conscious.

Das Verständnis der komplexen Interaktionen innerhalb des weltweiten Transportnetzes ist ein essentieller Schritt auf dem Weg zur Vorhersage der Krankheitsausbreitung und Entwicklung von effektiven Gegenmaßnahmen. Ungeachtet der weltweiten Vernetzung werden die politischen Entscheidungen oft von nationaler, regionaler und egozentrischen Denkweise geleitet. Die Ebola-Epidemie in 2014 demonstrierte deutlich, dass solche Herangehensweise modernen Epidemien nicht gerecht werden kann. In dieser Dissertation werden mehrere Methoden entwickelt, welche es ermöglichen während einer Epidemie die globalen Teilnehmer entsprechend ihrer Rolle einzustufen und das Risiko des Krankheitsexports zu berechnen. Die Methodik wird analytisch und numerisch ausführlich diskutiert. Darüber hinaus werden Lösungen für hypothetische und reale Epidemien auf dem Flugverkehrsnetz vorgestellt. Im zweiten Teil wird mit Hilfe eines vereinfachten spieltheoretischen Modells der Prozess der Ressourcenverteilung zur Epidemieeindämmung untersucht. Dabei erfolgt die Verteilung der Ressourcen von den Knoten und kann in egozentrischer oder altruistischer Weise erfolgen. Im Rahmen der Modells liegen die Optima für das altruistische und egozentrische System eng beieinander, solange der ausbruch räumlich konzentriert ist. In diesem Fall ist es optimal alle Ressourcen in den Ausbruchsknoten zu investieren. Bei lokal getrennten Ausbrüchen streben die Systeme verschiedene Gleichgewichte an. In allen Aspekten der Arbeit wird netzwerkbasierte Repräsentation des Systems verwendet, so dass die Orte durch Knoten innerhalb eines Transportnetzwerkes abgebildet werden. Die vorliegende Arbeit vereint mehrere Vorteileder etablierten Methoden: während der Schwerpunkt auf der Topologie des Netzwerkes liegt, berücksichtigt die vorgestellte Methodik den Ursprungsort der Epidemie. / The growing complexity of the global mobility is a key challenge for the understanding of the worldwide spread of emergent infectious diseases and the design of effective containment strategies. Despite global connectivity, containment policies are often based on national, regional and ’egocentric’ assessments of outbreak situations that are no longer effective or meaningful, as recently demonstrated by 2014 Ebola outbreak in West Africa, where months passed before a concerted, international effort followed. Despite the importance of the matter, optimal strategies in highly connected non-local settings are poorly understood. In the work at hand we propose a set of methods for more informed decision making during and prior to a pandemic. All of the studied systems are represented by networks in analogz the traffic networks, which play a dominant role during the global disease spreading. We introduce methods to calculate the risk of disease importation in a specific location and to determine the role of a node during an outbreak. Using the world aviation network, we demonstrate how the methods can be applied on real and hypothetical pandemics. We show that the airports can be divided into two distinct groups according to the role they take on in distributing the disease. Further, investigate the allocation of resources as a game theoretic dilemma. We embrace a bottom-up approach to this question, allowing the nodes of the network to distribute the resourses. We investigate egocentric and altruistic strategies and conclude that the optimal state of both systems are very similar if the outbreak is spacially confined. In this case allocation of resources ti the affected nodes is the optimal strategy. When there are multiple independent outbreaks, the optima diverge substantially. To foster the benefits of multiple approaches, the work at hand combines the information on the network topology but also regard some specifics of the outbreak at hand outbreak.