[kand] Perustieteiden korkeakoulu / SCI

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    Machine Learning in Solar Flare Forecasting
    (2023-12-27) Ranta-Nilkku, Lauri; Weigt, Dale; Perustieteiden korkeakoulu; Savioja, Lauri
    Numerous studies have developed methods for solar flare forecasting; however, most of them provide differing predictions despite the same event of interest. This literature review evaluates the current state solar flare forecasting methods with an emphasis on machine learning techniques. The main finding are the multiple reasons to the sheer difficulty of accurately forecasting these rare events, such as the major class imbalance in the amount of strong and weak flares and the complex and dynamic nature of the Sun that often requires time-series forecasting techniques. New machine learning methods show increased accuracy and great potential in understanding complex patterns, but are greatly limited due to the complexity of implementing them as well as the poor data quality. The lack of an established performance assessment process and a shared benchmark dataset further hinder the progress due to the results from different studies being effectively incomparable. The creation and spread of such datasets and benchmarking practices as well as increased cross-disciplinary work between physicists and computer scientists are crucial next steps in improving the results from machine learning based solar flare forecasting models.
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    Usability and user acceptance of voice assistants in health apps for older adults
    (2023-12-15) Ngo, Phuong; Ghorbanian Zolbin, Maedeh; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
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    Machine Learning in Healthcare Risk Adjustment
    (2023-12-15) Liimatainen, Eero; Gröhn, Tommi; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
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    Ensemble Learning Methods for Face Presentation Attack Detection
    (2023-12-15) Seppälä, Janne; Muhammad, Usman; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
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    The Use of Neural Radiance Fields in Panoptic Segmentation
    (2023-12-15) Tran, Linh; Kannala, Juho; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
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    Private Key Vulnerabilities in Browser Wallets
    (2023-12-14) Pentinsaari, Jaakko; Vepsäläinen, Juho; Perustieteiden korkeakoulu; Savioja, Lauri
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    Naissijoittajan merkitys venture capital -yhtiössä
    (2020-07-21) Raimas, Ilona; Vänttinen, Noora; Perustieteiden korkeakoulu; Giesecke, Stina
    Venture capital -yhtiöillä on merkitystä talouskasvun ylläpitäjinä ja niiden toiminta on usein edellytys uusien yritysten menestymiselle. Pääomasijoittajien vastuu luo paineita sekä haitallisia asenteita, jotka voivat aiheuttaa erilaisia vääristymiä, kuten rahoitusmaailman muuttaminen maskuliinisempaan suuntaan. Viime aikoina perinteisen johtamisen ja vastuunkantamisen rinnalle on pikkuhiljaa noussut käsitys ihmislähtöisemmästä johtajuudesta, joka pohjautuu aktiiviselle diversiteettitutkimukselle. Tämän kandidaatintyön tarkoituksena on selvittää vaikuttaako naiseus kriittisen varhaisen vaiheen venture capital -sijoittamisessa, millaiset asiat nykytilan taustalla vaikuttavat ja että onko venture capital immuuni globaalille ongelmalle: sukupuoleen perustuville vääristymille. Tutkimuksien mukaan naisia syrjivän kulttuurin taustalla on monia seikkoja. Yksi merkittävä tekijä ovat hyvä veli -verkostot, joissa nainen koetaan usein uhkana. Vähättelevä asenne vaikuttaa naisten urakehitykseen, vaikka stardardoidussa tutkimuksessa ilmeni, että nais- ja miessijoittajilla ei ollut huomattavia kompetenssieroja. Yhtenä havaintona ilmeni, että miessijoittajat hyötyivät tiiminsä diversiteetistä naiskollegoidensa kustannuksella. Löydöksistä ilmeni myös, että asenteet ja ilmapiirit luovat eräänlaisen kompetenssivääristymän, johon lankeavat miesten lisäksi myös naiset itse. Tällaiset rakenteelliset vääristymät ylläpitävät mielikuvaa naisen kompetenssin puutteesta ja ruokkivat siten naisia syrjivää työkulttuuria sijoittamisessa. Vaikka tutkimuksen mukaan nainen tiedostaa miestä useammin epätasa-arvoisen tilanteen, usein todellisessa tilanteessa voi olla haastavaa huomioida vääristymiä omassa päätöksenteossa paineen sekä odotusten johdosta. Verkostojen sekä muiden työelämänlainalaisuuksien taustalla onkin itseään ruokkiva noidankehä, jossa negatiivinen asenne vaikeuttaa venture capital -naissijoittajien menestymistä. Tärkeää olisikin selvittää, miten voisimme muuttaa haitallista asenneilmapiiriä ja nostaa siten naisten osuutta menestyvistä sijoittajista. Työn suurimpia rajoitteita sekä prosessiin vaikuttavia tekijöitä oli puutteellinen data. Datan vähyys, otoskokojen pienuus sekä naisten vähäinen absoluuttinen määrä tuovat paljon epävarmuustekijöitä tutkimukseen. Jotta voisimme ymmärtää paremmin rahoitusmaailman sosiaalisia epäkohtia, tulisi meidän ehdottomasti jatkaa huolellisen sukupuolidatan keräämistä voidaksemme ratkaista epätasa-arvon ikuisuuskysymyksen. Maailmamme tarvitsee muutosta, sillä naissijoittajien potentiaalin hukkaaminen nykyisenlaisesti on monin eri tavoin suoraan pois niin yksilöltä, pääomasijoitusyhtiöiltä kuin yhteiskunnaltakin.
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    Bayesilainen optimaalinen koeasetelma kolmiuloitteiselle magnetorelaksometriakuvantamiselle
    (2024-03-01) Veijola, Simo; Hyvönen, Nuutti; Perustieteiden korkeakoulu; Hyvönen, Nuutti
    Tässä kandidaatintyössä tutkitaan bayesilaista optimaalista koeasetelmaa magnetorelaksometriakuvantamiselle kolmessa ulottuvuudessa. Magnetorelaksometriakuvantamisen tavoitteena on selvittää magneettisten nanopartikkeleiden (MNP) jakauma kohdealueessa. Kuvantamisessa sensoreilla mitataan käämeillä magnetoidun MNP-jakauman magneettivuontiheyden relaksaatiota ja tämän perusteella rekonstruoidaan alueen MNP-jakauma. Työssä kuvantamisongelmaa approksimoitiin lineaarisena yhtälöryhmänä. Optimaalinen koeasetelma määriteltiin aktivaatiokäämien sijainteina ja suuntautumisina, joilla saavutettiin optimaalinen arvo mittausten epävarmuutta kuvaavalle A-optimaalisuusfunktiolle. Satunnaismuuttujana käsiteltävän MNP-konsentraatioon liittyvää etukäteistietoa kuvattiin työssä Gaussin priorijakaumalla tai totaalivariaatiopriorijakaumalla. Gaussin jakaumaa on helppo päivittää, mutta sen sileys estää selkeiden reunojen muodostumisen rekonstruktioon. Gaussin jakauman helppokäyttöisyyttä hyödynnettiin jakauman reunoja korostavalle TV-priorille viivästetyn diffuusion iteroinnin avulla. A-optimaaliset asetelmat ratkaistiin gradientti- ja Newtonin menetelmillä. Gaussin priorin tapauksessa A-optimaalisuus ei riipu aiemmista mittauksista, joten optimointi voitiin suorittaa kaikille aktivaatioille samanaikaisesti. Numeerisissa kokeissa kokeiltiin myös verrokkimenetelmää: ensin optimoitiin aktivaatiot yksitellen ja sitten kaikki aktivaatiot samanaikaisesti. TV-priorin numeerisissa kokeissa järjestyksessä optimoitavat aktivaatiokäämit asetettiin alussa joko tasaisesti kohdealueen ympärille tai valittiin harvalta pistehilalta lähes optimaalinen alkusijainti ennen iteratiivista optimointia. Gaussin priorin numeerisissa kokeissa todennettiin kolmeen ulottuvuuteen yleistettyjen algoritmien olevan toimivia ja optimoitujen asetelmien olevan järkeenkäypiä. Havaittiin lisäksi, että optimoimalla ensin yksittäisiä aktivaatioita päädytään optimaalisempaan koeasetelmaan. Totaalivariaatiopriorin kokeissa rekonstruktioiden laadut paranivat avustavaa hilaetsimismenetelmää käytettäessä varsinkin aktivaatioiden lukumäärän kasvaessa. Nämä yksinkertaiset menetelmät osoittautuivat toimiviksi lokaaliin optimiin juuttumisen estämisessä.
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    Bayesian optimization with discrete variables for materials
    (2023-12-21) Ristolainen, Jussi; Löfgren, Joakim; Perustieteiden korkeakoulu; Rinke, Patrick
    A frequently encountered problem in material science is the global optimization of expensive black-box functions. Many of them contain discrete variables, either in the form of categorical or integer values. An iterative machine learning method known as Bayesian optimization is suited for optimizing such complex black-box functions. However, this strength comes with a significant limitation as Bayesian optimization struggles with discrete variables. This limitation comes from the use of continuous Gaussian processes to model the objective function. To solve this problem this thesis investigates three distinct rounding methods designed to adapt Bayesian optimization to better align with the actual objective function. In this thesis we focus on ordered discrete variables ignoring categorical ones. These rounding methods have been combined with a tailor-made optimization algorithm that is suitable for mixed-variable optimization. As part of my work, these methods have been implemented into the Aalto BOSS code for Bayesian optimization. The strategies proposed in this thesis have been tested on two carefully selected benchmark functions and a material science problem. Here we find that a rounding transformation applied to the kernel significantly improves the efficiency and accuracy of Bayesian Optimization in handling mixed-variable benchmarks. These improvements are most noticeable when the number of values assumed by the discrete variables are small. The choice of acquisition function is also shown to be important as the ELCB acquisition function outperformed LCB in most benchmarks. The findings from this thesis are expected to be widely applicable to optimization problems in material science. In particular, we provide a description of how these methods can be effectively applied to the optimization of nylon actuators with a focus on maximizing their actuation length.
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    Contact angle goniometry: From wetting theory to instrument automation
    (2024-02-29) Saine, Reetta; Vuckovac, Maja; Perustieteiden korkeakoulu; Martikainen, Jani-Petri
    Wetting is a multiscale phenomenon describing the behavior of a liquid in contact with a solid surface, which has significance in numerous biological systems and technological applications. Traditionally, surface wettability is characterized by a contact angle, a quantity arising from the interfacial interactions of a liquid, a gas and a surface. The predominant method used to measure contact angles is contact angle goniometry, where an optical subsystem captures the profile of a droplet. Despite its apparent simplicity, the method is highly susceptible to errors and has inherent limitations, which proposes a need for more sophisticated experimental setups than those commercially available. Incorporating automation and developing control software become crucial in instrumentation development, allowing instrument capabilities to be pushed beyond the current state-of-the-art. Increased precision and reporoducibility of the measurement techniques advances wetting characterization, improving our understanding of surface properties and aiding the design of functional surfaces. The aim of this thesis is to develop operational control software for a custom-made contact angle goniometer. Contact angles are discussed from a theoretical perspective as a background for the technique, and an overview of the experimental methodology is given. The custom-made setup and the implemented control software are introduced. To demonstrate the operability of the instrument, three surfaces with distinct wetting properties are measured. The results are validated against those measured with a commercial instrument and the performance of the software is evaluated. The control software developed in this thesis allows intentional interaction between the user and the measurement and meets the responsibilities for conducting reproducible and reliable contact angle measurements. As a final result, the experimental setup is operational and the conducted measurements are comparable to those performed with a commercial instrument. In addition to being more convenient to operate, it demonstrates more favorable features than a commercial one. Overall, the setup appears promising and holds potential for advanced wetting measurements.
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    Reinforcement Learning from Human Feedback and Russell’s 3 Principles for Beneficial Machines
    (2024-02-20) Maunu, Aleksi; De Peuter, Sebastiaan; Perustieteiden korkeakoulu; Savioja, Lauri
    This Bachelor's thesis acts as a reference point for background concepts related to the \textit{learning from human feedback} research cluster in \textit{AI alignment}, and contributes to the field by specifying properties that cause an idealized form of \textit{Reinforcement Learning from Human Feedback} (RLHF) to fail to satisfy Russell's three principles for \textit{Beneficial Machines}, providing information on the extent to which RLHF will be relevant in the building of Beneficial Machines. The thesis found that, unless specifically implemented in a way that avoids certain pitfalls, there are multiple reasons the RLHF architecture fails to satisfy Russell's three principles. A key reason is the lack of an explicit distinction between the \textit{reward signal} and the \textit{actual reward} of a human, leading to models that are unaware of the distinction between maximizing the realization of human preferences, and maximizing the realization of a neural network's \textit{learned model} of human preferences. The thesis also found that some of the wordings in the principles can be interpreted in several ways, sometimes leading to ambiguity about whether or not a given system satisfies Russell's first and second principle. The thesis remarked that Russell's work seems to focus on the problem of correctly \textit{specifying complex goals}, sometimes referred to as \textit{outer alignment}, while neglecting the possibility of \textit{goal misgeneralization due to misalignment}, sometimes referred to as \textit{inner misalignment}, potentially limiting the work's relevance to the problem of aligning current and future AI systems with human values. The above issues limit the extent to which theoretical alignment benefits of learning from human feedback should be expected to be present in actual implementations of models that learn from human feedback. Further research on the extent to which Russell's principles are satisfied by different classes of AI architectures could aid in reaching a more comprehensive evaluation of the issue, and could thus help pave the way for the building of Beneficial Machines.
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    Persuasion and opinion change on Reddit r/ChangeMyView
    (2023-12-30) Lappalainen, Lauri; Keller, Barbara; Perustieteiden korkeakoulu; Savioja, Lauri
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    Adversiaalinen haku täydellisen tiedon peleissä
    (2023-12-15) Pollari, Visa; Hirvonen, Juho; Perustieteiden korkeakoulu; Kannala, Juho
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    Experimental research on hand-painted dye solar cells
    (2022-07-27) Kause, Iris; Halme, Janne; Perustieteiden korkeakoulu; Halme, Janne
    This research delves into the fabrication process of natural dye solar cells, especially dye preparation and application. Novel methods for dye application by hand-painting, preparing dye solutions from plants and sealing the cells were explored. The painting technique was improved by adjusting the content of ethanol and using a more concentrated dye solution. This was achieved by longer heating time, more plant mass and evaporation. The use of hand-painting as a method for studying multiple dyes inside one cell was demonstrated. Sun dyeing was also found to work as a method of dye extraction. In this method the plant matter was soaked for a week in a jar that was kept on a sunny windowsill. The plants used in this study were the common reed and aronia plant. Two kinds of dyes were extracted from the common reed – bright yellow dye from the leaves and dark red from the flowers. The red dye was observed to turn green when adsorbed. Leaf-based dyes did not stay in place when painted onto the cell – the painted pattern started to dissappear as the dye spread out into the electrolyte and dyed the surrounding areas. This spreading effect was studied with different dyes using photographic imaging. Traditional methods from natural textile dyeing were applied. Alum, a mordant used in textile dyeing was added to the dye solutions. The solution then flocculated due to alum, making it clearer. This improved cell performance especially in antho-cyanin dye cells – in best cases, efficiency tripled. The effect wasn’t notable in cells with flavonoid dyes. Sealing of the cells was improved by adjusting the temperature and pressure of the hot press, and by removing moisture from the frame foils used for sealing by storing them in a moisture-controlled container. This achieved better sealing and less bubbles in the frame foil, leading to fewer leakages.
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    Deep Learning Model Training for 3D Molecules in Atomic Force Microscopy
    (2024-02-07) Turpeinen, Aleksi; Oinonen, Niko; Perustieteiden korkeakoulu; Liljeroth, Peter
    Atomic Force Microscopy (AFM) is a nanoscale technique that offers the capability to capture high-resolution images of single atoms or molecules. This is done by bringing a small, metallic cantilever with a carbon monoxide (CO) molecule attached to its tip close to the examined surface in ultra-vacuum conditions close to absolute zero temperature. The cantilever is driven to oscillate with a specific frequency near the surface. The interaction forces between the atoms on the surface and the CO molecule at the cantilever's tip induce modifications to the oscillation frequency. These changes provide information about the surface's molecular and atomic structure. In this work, deep learning was used with neural networks in order to improve the resolution and clarity of simulated AFM images and gain more information about their 3D molecular structure. The accuracy of the neural network was measured with a loss function computed with the mean squared error method, which was minimized with gradient descent. Instead of real-life AFM images, a Probe Particle Model was used to simulate an AFM system using the Lennard-Jones potential and the Coulomb force. Two large datasets of simulated AFM images were given to a neural network to train it. One dataset was smaller than the other, but had a large amount of rotations for the molecules. The other dataset was larger and contained molecules with heavier elements such as bromine and chlorine. After this phase, separate simulated AFM images were fed to the trained neural network to test the model. The neural network training required significant computational resources, but using graphics processing units (GPU) on the Aalto University Triton server greatly sped up the training process. The neural network demonstrated a significant improvement in the simulated AFM images. This enhancement made individual atoms within the molecules distinctly visible, and the geometric configuration of the observed molecules easily ascertainable. By combining the precision of AFM with the computational power of neural networks, this work advances our understanding of molecular and atomic landscapes at the nanoscale.
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    Tietoturvan käytettävyys nuorten aikuisten tietokoneen käytössä
    (2024-02-13) Astikainen, Emma; Suoranta, Sanna; Perustieteiden korkeakoulu; Savioja, Lauri
    Tämä kandidaatintyö käsittelee nuorten aikuisten tietoturvatietoisuutta sekä -käytöstä, kun päätelaitteena on tietokone. Työssä tarkastellaan nuorten aikuisten tietoisuutta sen tason, siihen vaikuttavien tekijöiden ja sitä lisäävien keinojen näkökulmista. Tietoturvan tietoisuus sekä oletukset, asenteet ja kokemukset vaikuttavat nuorten aikuisten tietoturvalliseen käytökseen. Koska tietoisuus on paikoitellen puutteellista, työssä tarkastellaan myös nuorten aikuisten kohtaamia tietoturvauhkia ohjelmien tai järjestelmien sekä fyysisten ja sosiaalisten tekijöiden näkökulmista. Nuorten aikuisten tietoturvatietoisuutta ja turvallista käytöstä pyritään lisäämään koulutusten avulla. Koulutuksissa tulee panostaa konkreettisiin käytännön esimerkkeihin aiheellisista tietoturvauhista sekä kommunikaatioon nuoren ja kouluttajan välillä. Tietoisuuden nostaminen auttaa nuoria riskialttiin käytöksen ehkäisemisessä. Työ on toteutettu kirjallisuustutkimuksena. Työssä tietoturva mielletään käyttäjän omien tietojen ja yksityisyyden suojaamisena. Käytettävyys mielletään käyttäjälle helppokäyttöisenä ja intuitiivisena käyttöliittymänä tai järjestelmänä. Järjestelmätekijöiden tehtävänä on varmistaa tietoturvan ja käytettävyyden onnistunut yhdistäminen tekemällä järjestelmistä käyttäjälle käyttökelpoisia tietoturvan puolesta.
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    A Decision Model for Electricity Contract Selection Under Uncertainty
    (2024-02-05) Korhonen, Ahti; Olander, Leevi; Perustieteiden korkeakoulu; Salo, Ahti
    Electricity markets have significantly changed over the recent years. These changes have led to volatile spot markets, which has drawn attention to risk management practices, and how they can be further developed to tackle these new challenges. Consumers have also been heavily influenced by the aforementioned developments, but research on consumers in the energy markets is scarce. This thesis applies the concept of stochastic dominance to a risk-averse consumer in the Finnish retail electricity markets. The implemented four period decision model considers the uncertainty of the electricity spot price and determines a set of optimal electricity contracts under the assumed risk preferences that minimize the costs associated with these contracts. The uncertainty arising from the electricity spot price is modeled using a binomial lattice. Once the optimal contracts are identified, the results are compared to other decision alternatives. According to the results, the use of stochastic dominance in choosing electricity contracts proved worthwhile. The model provided clear results that could eas- ily be incorporated into practical decision making. The main result showed that mean-variance optimization did not fully represent the preferences of a risk averse decision maker. Another notable result was that some contract alternatives offered to customers were never preferred by risk averse decision makers.
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    Fabrication and characterization of two-dimensional van der Waals heterostructures
    (2024-01-30) Ranimäki, Niklas; Ali, Fida; Perustieteiden korkeakoulu; Kinnunen, Jami
    Two-dimensional (2D) materials are atomically thin materials. This causes the charge carries to be confined to two spatial dimensions, which in turn causes the material to exhibit special properties. 2D materials such as transition metal dichalcogenides (TMD) exhibit strongly bounded electron-hole pairs at room temperature. These quasiparticles known as excitons dominate the optical properties of the material. Mechanically placing differing 2D materials on top of one another creates 2D heterostructures, where the materials are held together by the van der Waals forces. The properties of heterostructures depend on the constituent layers as well as their stacking order. This and the straightforward fabrication method allow for the creation of materials with desired properties. 2D van der Waals heterostructures have potential as nanomachines of the future such as atomically thin TMD-based field-effect transistors and solar cells. The number of layers in 2D materials has an impact on their properties. Thus it is important to be able to determine the number of layers of a material. In this thesis, the possibility of determining the number of layers in a 2D material with characterization methods is investigated. Optical microscopy, atomic force microscopy, Raman and photoluminescence spectroscopy are used to probe how different materials, number of layers and substrates affect the geometric, vibrational and excitonic properties. In addition to this, the effectiveness of these methods for characterizing and probing the properties of heterostructures will be assessed. In this work, it was found that atomic force microscopy could be used to determine the number of layers from the measured thickness of the material. It was discovered that the number of layers influence the Raman and photoluminescence spectra of the material enabling the possibility of determining the number of layers in a material with these optical characterization methods. Specifically, the number of layers was observed to affect the number, intensity and location of peaks of the Raman spectra caused by vibrational modes. In the photoluminescence spectra, the effect of temperature on the location and intensity of peaks caused by excitonic complexes was observed. The Raman and photoluminescence spectra of heterostructures were noticed to consist of peaks from the spectra of its constituent layers for the most part. This would make the characterization of complex heterostructures via Raman and photoluminescence spectroscopy difficult. However, it was noticed that the Raman peaks caused by low frequency vibrational modes did not behave this way for two-layered heterostructures. By studying the constituent layers with multiple characterization methods, the heterostructure can be characterized effectively.
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    Pienydinreaktoritekniikan hyödyntäminen Helsingin kaupungin kaukolämmityksessä
    (2023-08-23) Munukka, Joni; Tulkki, Ville; Perustieteiden korkeakoulu; Sand, Andrea
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    Travel-time Optimal Line Plans on Trees
    (2024-01-28) Kangaslahti, Anna-Maija; Schiewe, Philine; Perustieteiden korkeakoulu; Schiewe, Philine
    Due to the urban transformation of past decades, the need for efficient and effective public transportation systems continues to increase. Consequently, many methods have been developed to optimize different components of public transportation systems using various criteria. One often desired result for these methods is to create a line plan where the travel time of the passengers is minimal. Schobel and Scholl developed a formulation for the line optimization problem that achieves this result and manages to consider the total travel time for all passengers. However, due to the large size and the high complexity of the formulation, it cannot be solved in any straightforward manner. This thesis aims to expand on the work of Schöbel and Scholl by developing a formulation for star-shaped station networks that acts as a simpler alternative to their binary frequency formulation. The alternative formulation is developed by leveraging the special features of star-shaped networks and tree networks in general: There exists only one simple path between any two nodes of a tree. Thus, we show that the driving time of any passenger is constant for their trip, and the travel time only depends on the passenger’s transfers. Consequently, the developed formulation tracks only the transfers, not the entire route of the passengers, and optimizes the total travel time by minimizing the total number of said transfers. As part of this thesis, we prove the developed formulation to be equivalent to the binary frequency formulation of Schöbel and Scholl in star-shaped trees. It is also found that the developed formulation is significantly smaller than the formulation of Schöbel and Scholl, especially for larger star networks. This implies that the developed formulation is simpler to solve, as desired. The successfully developed formulation thus proves that the formulations of the line planning problems can be simplified by limiting the underlying networks to trees. Additionally, the developed formulation has limited practical applications and benefits.