AN INTEGRATED CONTROL MODEL FOR FREEWAY INTERCHANGES

This dissertation proposes an integrated control framework to deal with traffic congestion at freeway interchanges. In the neighborhood of freeway interchanges, there are six potential problems that could cause severe congestion, namely lane-blockage, link-blockage, green time starvation, on-ramp queue spillback to the upstream arterial, off-ramp queue spillback to the upstream freeway segments, and freeway mainline queue spillback to the upstream interchange. The congestion problem around freeway interchanges cannot be solved separately either on the freeways or on the arterials side. To eliminate this congestion, we should balance the delays of freeways and arterials and improve the overall system performance instead of individual subsystem performance. This dissertation proposes an integrated framework which handles interchange congestion according to its severity level with different models. These models can generate effective control strategies to achieve near optimal system performance by balancing the freeway and arterial delays. The following key contributions were made in this dissertation: 1. Formulated the lane-blockage problem between the movements of an arterial intersection approach as an linear program with the proposed sub-cell concept, and proposed an arterial signal optimization model under oversaturated traffic conditions; 2. Formulated the traffic dynamics of a freeway segment with cell-transmission concept, while considering the exit queue effects on its neighboring through lane traffic with the proposed capacity model, which is able to take the lateral friction into account; 3. Developed an integrated control model for multiple freeway interchanges, which can capture the off-ramp spillback, freeway mainline spillback, and arterial lane and link blockage simultaneously; 4. Explored the effectiveness of different solution algorithms (GA, SA, and SA-GA) for the proposed integrated control models, and conducted a statistical goodness check for the proposed algorithms, which has demonstrated the advantages of the proposed model; 5. Conducted intensive numerical experiments for the proposed control models, and compared the performance of the optimized signal timings from the proposed models with those from Transyt-7F by CORSIM simulations. These comparisons have demonstrated the advantages of the proposed models, especially under oversaturated traffic conditions

Microscopic Modeling of Human and Automated Driving: Towards Traffic-Adaptive Cruise Control

The thesis is composed of two main parts. The first part deals with a microscopic traffic flow theory. Models describing the individual acceleration, deceleration and lane-changing behavior are formulated and the emerging collective traffic dynamics are investigated by means of numerical simulations. The models and simulation tools presented provide the methodical prerequisites for the second part of the thesis in which a novel concept of a traffic-adaptive control strategy for ACC systems is presented. The impact of such systems on the traffic dynamics can solely be investigated and assessed by traffic simulations. The focus is on future adaptive cruise control (ACC) systems and their potential applications in the context of vehicle-based intelligent transportation systems. In order to ensure that ACC systems are implemented in ways that improve rather than degrade traffic conditions, the thesis proposes an extension of ACC systems towards traffic-adaptive cruise control by means of implementing an actively jam-avoiding driving strategy. The newly developed traffic assistance system introduces a driving strategy layer which modifies the driver's individual settings of the ACC driving parameters depending on the local traffic situation. Whilst the conventional operational control layer of an ACC system calculates the response to the input sensor data in terms of accelerations and decelerations on a short time scale, the automated adaptation of the ACC driving parameters happens on a somewhat longer time scale of, typically, minutes. By changing only temporarily the comfortable parameter settings of the ACC system in specific traffic situations, the driving strategy is capable of improving the traffic flow efficiency whilst retaining the comfort for the driver. The traffic-adaptive modifications are specified relative to the driver settings in order to maintain the individual preferences. The proposed system requires an autonomous real-time detection of the five traffic states by each ACC-equipped vehicle. The formulated algorithm is based on the evaluation of the locally available data such as the vehicle's velocity time series and its geo-referenced position (GPS) in conjunction with a digital map. It is assumed that the digital map is complemented by information about stationary bottlenecks as most of the observed traffic flow breakdowns occur at these fixed locations. By means of a heuristic, the algorithm determines which of the five traffic states mentioned above applies best to the actual traffic situation. Optionally, inter-vehicle and infrastructure-to-car communication technologies can be used to further improve the accuracy of determining the respective traffic state by providing non-local information. By means of simulation, we found that the automatic traffic-adaptive driving strategy improves traffic stability and increases the effective road capacity. Depending on the fraction of ACC vehicles, the driving strategy "passing a bottleneck" effects a reduction of the bottleneck strength and therefore delays (or even prevents) the breakdown of traffic flow. Changing to the driving mode "leaving the traffic jam" increases the outflow from congestion resulting in reduced queue lengths in congested traffic and, consequently, a faster recovery to free flow conditions. The current travel time (as most important criterion for road users) and the cumulated travel time (as an indicator of the system performance) are used to evaluate the impact on the quality of service. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were significantly reduced even with much lower penetration rates. Moreover, the cumulated travel times decreased consistently with the increase in the proportion of ACC vehicles.In der Arbeit wird ein neues verkehrstelematisches Konzept für ein verkehrseffizientes Fahrverhalten entwickelt und als dezentrale Strategie zur Vermeidung und Auflösung von Verkehrsstaus auf Richtungsfahrbahnen vorgestellt. Die operative Umsetzung erfolgt durch ein ACC-System, das um eine, auf Informationen über die lokale Verkehrssituation basierende, automatisierte Fahrstrategie erweitert wird. Die Herausforderung bei einem Eingriff in das individuelle Fahrverhalten besteht - unter Berücksichtigung von Sicherheits-, Akzeptanz- und rechtlichen Aspekten - im Ausgleich der Gegensätze Fahrkomfort und Verkehrseffizienz. Während sich ein komfortables Fahren durch große Abstände bei geringen Fahrzeugbeschleunigungen auszeichnet, erfordert ein verkehrsoptimierendes Verhalten kleinere Abstände und eine schnellere Anpassung an Geschwindigkeitsänderungen der umgebenden Fahrzeuge. Als allgemeiner Lösungsansatz wird eine verkehrsadaptive Fahrstrategie vorgeschlagen, die ein ACC-System mittels Anpassung der das Fahrverhalten charakterisierenden Parameter umsetzt. Die Wahl der Parameter erfolgt in Abhängigkeit von der lokalen Verkehrssituation, die auf der Basis der im Fahrzeug zur Verfügung stehenden Informationen automatisch detektiert wird. Durch die Unterscheidung verschiedener Verkehrssituationen wird ein temporärer Wechsel in ein verkehrseffizientes Fahrregime (zum Beispiel beim Herausfahren aus einem Stau) ermöglicht. Machbarkeit und Wirkungspotenzial der verkehrsadaptiven Fahrstrategie werden im Rahmen eines mikroskopischen Modellierungsansatzes simuliert und hinsichtlich der kollektiven Verkehrsdynamik, insbesondere der Stauentstehung und Stauauflösung, auf mehrspurigen Richtungsfahrbahnen bewertet. Die durchgeführte Modellbildung, insbesondere die Formulierung eines komplexen Modells des menschlichen Fahrverhaltens, ermöglicht eine detaillierte Analyse der im Verkehr relevanten kollektiven Stabilität und einer von der Stabilität abhängigen stochastischen Streckenkapazität. Ein tieferes Verständnis der Stauentstehung und -ausbildung wird durch das allgemeine Konzept der Engstelle erreicht. Dieses findet auch bei der Entwicklung der Strategie für ein stauvermeidendes Fahrverhalten Anwendung. In der Arbeit wird die stauvermeidende und stauauflösende Wirkung eines individuellen, verkehrsadaptiven Fahrverhaltens bereits für geringe Ausstattungsgrade nachgewiesen. Vor dem Hintergrund einer zu erwartenden Verbreitung von ACC-Systemen ergibt sich damit eine vielversprechende Option für die Steigerung der Verkehrsleistung durch ein teilautomatisiertes Fahren. Der entwickelte Ansatz einer verkehrsadaptiven Fahrstrategie ist unabhängig vom ACC-System. Er erweitert dessen Funktionalität im Hinblick auf zukünftige, informationsbasierte Fahrerassistenzsysteme um eine neue fahrstrategische Dimension. Die lokale Interpretation der Verkehrssituation kann neben einer verkehrsadaptiven ACC-Regelung auch der Entwicklung zukünftiger Fahrerinformationssysteme dienen

Pickering Emulsion and Derived Materials

Particle-stabilized emulsions, today often referred to as Pickering/Ramsden emulsions, are vital in many fields, including personal care products, foods, pharmaceuticals, and oil recovery. The exploitation of these Pickering emulsions for the manufacture of new functional materials has also recently become the subject of intense investigation. While much progress has been made over the past decade, Pickering emulsion still remains a rich topic since many aspects of their behavior have yet to be investigated. The present “Pickering Emulsion and Derived Materials” Special Issue aims to bring together research and review papers pertaining to the recent developments in the design, fabrication, and application of Pickering emulsions. The themes include, but are not limited to: 1. Interactions of colloidal particles confined at fluid interfaces 2. Pickering emulsion-based polymerization 3. Interfacial assembly and emulsion stabilization 4. Rheology of particle laden interfaces and Pickering emulsions 5. Functional materials templated from Pickering emulsion

Self-Assembly of Azobenzene Photosurfactants and the Relationship with Macroscopic Properties

Azobenzene photosurfactants (AzoPS) demonstrate the combined ability to change their shape upon irradiation with light and to self-organise into polymolecular assemblies. Photoisomerisation changes the polarity and shape of the surfactant on demand, which has led to the exploration of these materials in a diverse range of applications. The interplay between these properties also affects the concentration-dependent self-assembly of azobenzene photosurfactant into micelles. This thesis focuses on four non-ionic azobenzene photosurfactants, which have been systematically varied in terms of their structure to study their self-assembly behaviour as a function of molecular structure, isomeric form, concentration, temperature and applied shear. The reciprocal effect of self-assembly behaviour on macroscopic properties such as viscosity, viscoelasticity, optical anisotropy and soft-templating ability is also investigated, emphasising how control over self-assembled structure can be used to modulate key properties and applications. In general, non-ionic photosurfactants have been less studied compared to their ionic counterparts and there are very few reports of their behaviour under shear, formation of lyotropic liquid crystal phases and use as templating agents. In this work, small-angle scattering is used extensively to probe the shape and dimensions of the self-assembled surfactant aggregates in solution. It will become apparent this is a key technique in the characterisation of dynamic and soft matter systems. Chapter 3 of this thesis will focus on the dynamic self-assembly behaviour of two AzoPS under photoisomerisation, with an initial study on the flow behaviour. Chapter 4 will take a more detailed look into the relationship between the self-assembled structure and flow behaviour of an AzoPS, using combined rheology and small-angle scattering measurements. In Chapter 5, the formation of lyotropic liquid crystal phases by all four AzoPS as a function of concentration, temperature and isomeric form, will be investigated, and binary concentration-temperature phase diagrams for each AzoPS constructed. In Chapter 6, the ability of AzoPS to act as soft-templating agents to form porous titania nanoparticles will be proven, along with an investigation on the effect of template structure and irradiation conditions on the photocatalytic ability of the product titania. Finally, conclusions will be drawn on the basis of the above results, along with a discussion of the evolution of the field during the time this work took place, with signposting for potential future work

Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events

Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents

A novel method to rapidly fit conductance-based models to individual neurons

In this thesis, I present a new method of model optimisation that allows the calibration of conductance-based models of neuronal membrane potential to data from just a single neuron, and achieves good correspondence with the reference data in mere minutes. These properties are desirable because they allow investigations of individual variability among neurons of a given type, of homoeostatic processes and non-synaptic plasticity events, as well as of the contribution of particular neuronal properties to the dynamics of small circuits. In the first chapter, the thesis introduces in detail the working principle of the method, which can be summed up as model optimisation using stimuli to isolate parameter subsets (“MOSTIPS”), and represents a major part of the work and novelty of this project. The second chapter focusses on the construction of accurate models of two mammalian potassium channels which, being ectopically expressed in Xenopus laevis oocytes, served as a validation tool for the new method. In the third chapter, I evaluate the new method, presenting results from fitting models to data from synthetic sources as well as the above-mentioned oocytes. Finally, the fourth chapter contains a number of related results from closed-loop electrophysiology approaches, including extensions to the dynamic clamp protocol for both single neurons and hybrid circuits composed of live and simulated neurons, as well as preliminary results from a closed-loop model fitting approach closely related to the main work presented above. The thesis concludes that the newly developed approaches to model fitting constitute valuable additions to existing methods. The MOSTIPS method achieves tightly constrained parametrisations using both less data and less processing time than classical methods, while the related closed-loop fitting approach produces results that closely follow ongoing changes in evoked activity patterns in real time. Conversely, some issues have been left unanswered, including the contribution of the stimulus generation and selection algorithm, the success of which I have been unable to establish, as well as whether the methods developed herein can reliably identify relevant properties of individual cells. Nevertheless, both the particular methods and the general approach of using prior estimates of the model and its parameter values to propose stimulus patterns represent major advances in the field of neuron model optimisation

Engineered environments for biomedical applications: anisotropic nanotopographies and microfluidic devices

During the last two decades micro- and nano-fabrication techniques originally developed for electronic engineering have directed their attention towards life sciences. The increase of analytical power of diagnostic devices and the creation of more biomimetic scaffolds have been strongly desired by these fields, in order to have a better insight into the complexity of physiological systems, while improving the ability to model them in vitro. Technological innovations worked to fill such a gap, but the integration of these fields of science is not progressing fast enough to satisfy the expectations. In this thesis I present novel devices which exploit the unique features of the micro- and nanoscale and, at the same time, match the requirements for successful application in biomedical research. Such biochips were used for optical detection of water-dispersed nanoparticles in microchannels, for highly controlled cell-patterning in closed microreactors, and for topography-mediated regulation of cell morphology and migration. Moreover, pilot experiments on the pre-clinical translation of micropatterned scaffolds in a rat model of peripheral nerve transaction were initiated and are ongoing. Given these results, the devices presented here have the potential to achieve clinical translation in a short/medium time, contributing to the improvement of biomedical technologies