Second order traffic flow models on road networks and real data applications

This thesis concerns macroscopic traffic models and data-driven models. In the first part we deal with the extension of Generic Second Order Models (GSOM) for traffic flow to road networks. We define a Riemann Solver at the junction based on a priority rule, providing an iterative algorithm able to build the solution to junctions with n incoming and m outgoing roads. The logic underlying our solver is the following: the flow is maximised respecting the priority rule, but the latter can be modified if the outgoing road supply exceeds the demand of the road with higher priority. We provide bounds on the total variation of waves interacting with the junction, giving explicit computations for intersections with two incoming and two outgoing roads. These estimates are fundamental to prove the existence of weak solutions to Cauchy problems on networks via Wave-Front-Tracking. GSOM are used to analyse traffic dynamics and their effects on the production of pollutant emissions. First we apply the proposed Riemann Solver to simulate traffic dynamics on diverge and merge junctions and on roundabouts obtained by combining these two types of intersection. Then, we propose a methodology to estimate the pollutant emissions deriving from traffic dynamics. The emission model is calibrated and validated using the NGSIM dataset of real trajectory data. Furthermore, we set up a minimisation problem aimed at finding the optimal priority rule for our Riemann Solver that reduces the emission rates due to the traffic dynamic. Finally, we analyse some chemical reactions which lead to the production of ozone, focusing on the effects on pollution of the presence of traffic lights on the road. Next, we introduce a macroscopic two-dimensional multi-class traffic model on a single road, aimed at including lane-changes and different types of vehicles. The multi-class model consists of a coupled system of conservation laws in two space dimensions. Besides the study of the Riemann problems, we present a Lax-Friedrichs type discretisation scheme and we recover the theoretical results by means of numerical tests. We then calibrate and validate the multi-class model with real trajectory data and we test its ability of simulating vehicles overtaking. Finally, we present a new methodology to recover mass movements from snapshots of its distribution. To this end we put in place an algorithm based on the combination of two methods: first, we use the dynamic mode decomposition to create a system of equations describing the mass transfer; second, we use the Wasserstein distance to reconstruct the underlying velocity field that is responsible for the displacement. We conclude this part with a real-life application: the algorithm is employed to study the travel flows of people in large populated areas using, as input, presence data of people in given region domains derived from the mobile phone network, at different time instants

To boldly go:an occam-π mission to engineer emergence

Future systems will be too complex to design and implement explicitly. Instead, we will have to learn to engineer complex behaviours indirectly: through the discovery and application of local rules of behaviour, applied to simple process components, from which desired behaviours predictably emerge through dynamic interactions between massive numbers of instances. This paper describes a process-oriented architecture for fine-grained concurrent systems that enables experiments with such indirect engineering. Examples are presented showing the differing complex behaviours that can arise from minor (non-linear) adjustments to low-level parameters, the difficulties in suppressing the emergence of unwanted (bad) behaviour, the unexpected relationships between apparently unrelated physical phenomena (shown up by their separate emergence from the same primordial process swamp) and the ability to explore and engineer completely new physics (such as force fields) by their emergence from low-level process interactions whose mechanisms can only be imagined, but not built, at the current time

AI and IoT Meet Mobile Machines

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

A Structural Approach to the Design of Domain Specific Neural Network Architectures

This is a master's thesis concerning the theoretical ideas of geometric deep learning. Geometric deep learning aims to provide a structured characterization of neural network architectures, specifically focused on the ideas of invariance and equivariance of data with respect to given transformations. This thesis aims to provide a theoretical evaluation of geometric deep learning, compiling theoretical results that characterize the properties of invariant neural networks with respect to learning performance.Comment: 94 pages and 16 Figures Upload of my Master's thesis. Not peer reviewed and potentially contains error

Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful

This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data analysts with respect to the technical and statistical aspects of market segmentation analysis. Even market segmentation experts will find something new, including a vast array of useful visualisation techniques that make interpretation of market segments and selection of target segments easier. All calculations are accompanied not only with a detailed explanation, but also with R code that allows readers to replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics.Dieses Open Access Buch offeriert allen etwas, die mit Marktsegmentierung zu tun haben: praktische Anleitungen für Anwender von Marktsegmentierungslösungen, organisatorische Hilfe zur Umsetzung und Datensammlung, sowie Hilfe zur technischen und statistischen Umsetzung von Marktsegmentierungsanalysen. Auch Experten der Marktsegmentierung finden neue Werkzeuge, inbesonders eine umfangreiche Sammlung von Visualisierungsmethoden zur einfacheren Interpretation und Selektion von Marktsegmenten. Alle Berechnungen werden nicht nur detailliert erklärt, sondern von R Code begleitet, welcher es dem Leser erlaubt, alle Analysen im Buch mit Hilfe der Open Source Statistiksoftware R zu replizieren

Market Segmentation Analysis

Business; Management science; Market research; Statistics

Sense and Respond

Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization); and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively). The next natural evolutionary step is to provide value by creating industrial cyber-physical assets with human-like intelligence. This will only be possible by further integrating strategic smart sensor technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards. This reprint of “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensor technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes

Advances in flexible manipulation through the application of AI-based techniques

282 p.Objektuak hartu eta uztea oinarrizko bi eragiketa dira ia edozein aplikazio robotikotan. Gaur egun, "pick and place" aplikazioetarako erabiltzen diren robot industrialek zeregin sinpleak eta errepikakorrak egiteko duten eraginkortasuna dute ezaugarri. Hala ere, sistema horiek oso zurrunak dira, erabat kontrolatutako inguruneetan lan egiten dute, eta oso kostu handia dakarte beste zeregin batzuk egiteko birprogramatzeak. Gaur egun, industria-ingurune desberdinetako zereginak daude (adibidez, logistika-ingurune batean eskaerak prestatzea), zeinak objektuak malgutasunez manipulatzea eskatzen duten, eta oraindik ezin izan dira automatizatu beren izaera dela-eta. Automatizazioa zailtzen duten botila-lepo nagusiak manipulatu beharreko objektuen aniztasuna, roboten trebetasun falta eta kontrolatu gabeko ingurune dinamikoen ziurgabetasuna dira.Adimen artifizialak (AA) gero eta paper garrantzitsuagoa betetzen du robotikaren barruan, robotei zeregin konplexuak betetzeko beharrezko adimena ematen baitie. Gainera, AAk benetako esperientzia erabiliz portaera konplexuak ikasteko aukera ematen du, programazioaren kostua nabarmen murriztuz. Objektuak manipulatzeko egungo sistema robotikoen mugak ikusita, lan honen helburu nagusia manipulazio-sistemen malgutasuna handitzea da AAn oinarritutako algoritmoak erabiliz, birprogramatu beharrik gabe ingurune dinamikoetara egokitzeko beharrezko gaitasunak emanez