Preventive maintenance is a process employed to reduce probability of failure or degradation of equipment. When such a process is performed by humans without a computerized solution, human errors are bound to take place. A computerized solution could reduce human errors by automatically performing tasks which are prone to error. A React web and a React Native application are created to evaluate which framework is most suitable for use in a preventive maintenance process based on three criteria. The criteria are round-trip request times, lines of code and delivery time based on build size. Results show that the React web application outperforms the React Native application in all three categories.

Complex concentrated alloys (CCAs) offer the unique ability to tune composition and microstructure to achieve a wide range of mechanical performance. Recently, the development of metastable CCAs has led to the creation of transformation-induced plasticity (TRIP) CCAs. Similar to TRIP steels, TRIP CCAs are more effective at absorbing high strain rate loads when TRIP is activated during the loading process. The objective of our study is to investigate the effect of copper on the critical pressure for activating TRIP and the high pressure stability of a Fe(40-X)Mn20Cr15Co20Si5CuX TRIP CCA, where x varies from 0 to 3 at.% Cu. To achieve this goal, diamond anvil cell testing during in-situ synchrotron radiation X-ray diffraction was performed using both a monochromatic wide angle X-ray scattering (WAXS) beam and, for the first time ever, a polychromatic Laue diffraction beam on a CCA. Laue diffraction allows for real-time phase evolution tracking of the γ-fcc → ε-hcp transformation in a high pressure environment. Based on the results, a new method for processing and preparation of high pressure samples without changing the microstructure of sample was developed. This new method can be used to prepare any CCA samples for high pressure testing.

The purpose of this study is to understand the non home-based travel behavior of urban market traders in Onitsha, Nigeria, where 58-70% of the labor force is engaged in trading. Relevant trip-inducing variables--gender, location, a means of personal transportation, size of stall and, type of good sold--were selected to assist in theorizing about the travel behavior of traders. Using a systematic sampling methodology, 246 stalls were selected and the MLR model was applied in data analyses. The results show that a typical stall is occupied by either one or two traders. There are three male traders for every female trader and 89.4% of stalls are occupied exclusively by one gender. The mean trips produced by a stall in a typical business day is 2.7 trips, with a variation from zero to ten trips. In the CBD, an average stall produced 4.4 trips while stalls in the periphery generated an average of 2.1 trips. Small stalls generate two trips, while medium (three occupants) to large stalls generate twice as many trips. While stalls occupied exclusively by males have a mean of 3.0 trips, female-occupied stalls generate only 2.0 trips. On the other hand, stalls which have a personal means of transportation generate more trips than stalls where none existed. Even though the maximum number of a personal means of transportation available to a stall was two, there was a substantial difference between the number of trips produced by stalls with the maximum number of vehicles and stalls where vehicles were unavailable. Finally, while provision goods stalls generate a mean of 3.3 trips, foodstuffs stalls generate about 2.1 trips with intermediate results for other commodities. For policymaking purposes, there is empirical evidence to show that traders, together with consumers, contribute to off-peak trips which directly impact the urban transportation system. There is the need to evolve policies to minimize the frequency of trips by traders, especially by discouraging multiple tripmaking because, it will contribute to a reduction in the amount of resources allocated to the upkeep of the urban transportation infrastructure.

This Master Thesis examines the experiences and motivations of solo travelers prior to, during as well as after the COVID-19 pandemic. A total of 250 solo travelers completed a designed survey, and in addition eight solo travelers were in-depth interviewed regarding their solo travel experiences in connection to COVID-19. Having used a mixed method approach, content analysis has been chosen to be the most suitable analysis for this thesis, identifying patterns throughout the survey as well as the interviews answering the research question: Which overall impact did the COVID-19 pandemic have on solo travel?  Prior to the pandemic, the main motivations for solo travel were identified as independence, flexibility, personal growth, self-actualization, and the desire for freedom. The primary constraints were financial limitations, safety concerns about certain destinations, fear of being alone, and anxiety about not having assistance in challenging situations. Despite these constraints, participants reported positive experiences and deemed solo travel as worthwhile, as it allowed them to overcome internal fears and limitations. During the COVID-19 pandemic, travel destinations were limited due to restrictions, and destination choices were influenced by the accessibility and perceived handling of the pandemic by governments. Solo travelers sought destinations with fewer COVID-19 restrictions than their place of residence. Additional stress was associated with solo travel, including the need for proper documentation, COVID-19 tests, and vaccines. However, participants found it easier to travel solo during the pandemic compared to traveling with companions due to logistical challenges and restrictions on group activities. Respondents acknowledged the need for a higher budget and mental preparedness for unexpected changes in travel plans and potential COVID-19 exposure. The motivation to travel during the pandemic stemmed from the desire to escape the lockdown situation in the respondents' home countries and regain a sense of normality. The German concept of "Fernweh," the longing to be somewhere else, was mentioned as a driving force. The pandemic heightened awareness of the unpredictability and uncertainty of future travel, leading to a greater appreciation for the freedom and independence of solo travel. Post-pandemic, respondents expressed a heightened interest in solo travel, with a focus on regaining independence and making travel decisions based on personal preferences rather than travel restrictions. The constraints of traveling solo, such as not having a companion or fear of being alone, became less significant after the pandemic. Participants anticipated increased interactions and a desire to meet fellow solo travelers, indicating a shift towards more social engagement in the post-COVID-19 era.

This research is concerned with the development of algorithmic approaches for solving problems that arise in the design and analysis of telecommunication networks, location-allocation distribution contexts, and intelligent transportation networks. Specifically, the corresponding problems addressed in these areas are a local access and transport area (LATA) network design problem, the discrete equal-capacity p-median problem (PMED), and the estimation of dynamic origin-destination path ows or trip tables in a general network. For the LATA network problem, we develop a model and apply the Reformulation-Linearization Technique (RLT) to construct various enhanced tightened versions of the proposed model. We also design efficient Lagrangian dual schemes for solving the linear programming relaxation of the various enhanced models, and construct an effective heuristic procedure for deriving good quality solutions in this process. Extensive computational results are provided to demonstrate the progressive tightness resulting from the enhanced formulations and their effect on providing good quality feasible solutions. The results indicate that the proposed procedures typically yield solutions having an optimality gap of less than 2% with respect to the derived lower bound, within a reasonable effort that involves the solution of a single linear program. For the discrete equal-capacity p-median problem, we develop various valid inequalities, a separation routine for generating cutting planes via specific members of such inequalities, as well as an enhanced reformulation that constructs a partial convex hull representation that subsumes an entire class of valid inequalities via its linear programming relaxation. We also propose suitable heuristic schemes for solving this problem, based on sequentially rounding the continuous relaxation solutions obtained for the various equivalent formulations of the problem. Extensive computational results are provided to demonstrate the effectiveness of the proposed valid inequalities, enhanced formulations, and heuristic schemes. The results indicate that the proposed schemes for tightening the underlying relaxations play a significant role in enhancing the performance of both exact and heuristic solution methods for solving this class of problems. For the estimation of dynamic path ows in a general network, we propose a parametric optimization approach to estimate time-dependent path ows, or origin-destination trip tables, using available data on link traffic volumes for a general road network. Our model assumes knowledge of certain time-dependent link ow contribution factors that are a dynamic generalization of the path-link incidence matrix for the static case. We propose a column generation approach that uses a sequence of dynamic shortest path subproblems in order to solve this problem. Computational results are presented on several variants of two sample test networks from the literature. These results indicate the viability of the proposed approach for use in an on-line mode in practice. Finally, we present a summary of our developments and results, and offer several related recommendations for future research. / Ph. D.

Traflic safety and accessibility have been two important subjects in transportation research. On the one hand traffic crashes bring about high societal costs and serious health risks for urban road users. The cost oftraffic crashes is estimated to be 17 billion euros per year only in the Netherlands while over 600 people were killed in traffic, of whom 229 were cyclists and 195 were car users [l, 2]. Accessibility, on the other band, is regarded as one of the indicators of the quality of the transport system serving the public. There is comprehensive literature investigating the relationship between traffic crashes and factors associated with traffic, roadway design, built environment, and human factors. Similarly, several studies assessed and evaluated accessibility levels of individuals, communities, and regions by utilizing the aforementioned. factors. Nevertheless, there is a scarcity ofliterature investigating the relationships between accessibility and traffic safety. This is especially surprising considering that both subjects are associated with a similar set of factors, including land use and transport systems, as weil as individual and temporal factors [3-7]. The relationships between accessibility and traffic safety can be an adverse one; for example, improved accessibility by increasing the travel speeds (i.e., declining travel time) intensifies the crash risks which also deteriorates equity. Furthermore, levels ofboth accessibility and traffic safety are not homogeneous throughout urban areas and among different population groups. Based on the literature, it is obvious that accessibility is associated with economic equity [8]. lt is revealed that accessibility of lower-income groups is substantially worse than the higher-income groups as these groups have less mobility [9]. Previous studies also showed. that lower-income groups usually suffer from traffic safety problems more than other socio-economic groups [10-12]. Therefore, this research aims to address the aforementioned gap in the literature in understanding the relationships between accessibility levels and traffic safety with a focus on social equity perspecti.ves. For this purpose, a Gravity model and risk exposure evaluation approaches are utilix.ed to analyze traffic safety and accessibility to jobs by bicycle via extending the traditional definition of accessibility based on only travel time or proximity to a location.

Trajectory data is playing an increasingly important role in our daily lives, as well as in commercial applications and scientific research. With the rapid development andpopularity of GPS, people can locate themselves in real time. Therefore, the users’behavior information can be collected by analyzing their GPS trajectory data, so as topredict their new trajectories’ destinations, ways of travelling and even thetransportation mode they use, which forms a complete personal travel diary. The taskin this thesis is to implement travel diary semantics enrichment of user’s trajectoriesbased on the historical labeled data of the user and trajectory similarity measures.Specially, this dissertation studies the following tasks: Firstly, trip segmentationconcerns detecting the trips from trajectory which is an unbounded sequence oftimestamp locations of the user. This means that it is important to detect the stops,moves and trips of the user between two consecutive stops. In this thesis, a heuristicrule is used to identify the stops. Secondly, tripleg segmentation concerns identifyingthe location / time instances between two triplegs where / when a user changesbetween transport modes in the user's trajectory, also called makes transport modetransitions. Finally, mode inference concerns identifying travel mode for each tripleg.Specially, steps 2 and 3 are both based on the same trajectory similarity measure andproject the information from the matched similar trip trajectory onto the unlabeled triptrajectory. The empirical evaluation of these three tasks is based on real word data set(contains 4240 trips and 5451 triplegs with 14 travel modes for 206 users using oneweek study period) and the experiment performance (including trends, coverage andaccuracy) are evaluated and accuracy is around 25% for trip segmentation; accuracyvaries between 50% and 55% for tripleg segmentation; for mode inference, it isbetween 55% and 60%. Moreover, accuracy is higher for longer trips than shortertrips, probably because people have more mode choices in short distance trips (likemoped, bus and car), which makes the measure more confused and the accuracy canbe increased by nearly 10% with the help of reverse trip identifiable, because it makesa trip have more similar historical trips and increases the probability that a newunlabeled trip can be matched based on its historical trips.

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Formable Advanced High-Strength Steels (AHSS) have a unique combination of strength and ductility, making them ideal in the effort to lightweight vehicles. The AHSS in this study, Quenched and Partitioned 1180, rely on the Transformation Induced Plasticity (TRIP) effect, in which retained austenite (RA) grains transform to martensite during plastic deformation, providing extra ductility via the transformation event. Understanding the factors involved in RA transformation, such as local strain and grain attributes, is therefore key to optimizing the microstructure of these steels. This research seeks to increase understanding of those attributes and the correlations between microstructure and RA transformation in TRIP steels. To measure local strain, the viability of using forescatter detector (FSD) images as the basis for DIC study is investigated. Standard FSD techniques, along with an integrated EBSD / FSD approach (Pattern Region of Interest Analysis System), are both analyzed. Simultaneous strain and microstructure maps are obtained for tensile deformation up to around 6% strain. The method does not give sub-grain resolution, and surface feature evolution prevents DIC analysis across large strain steps; however, the data is easy to obtain and provides a natural set of complementary information for the EBSD analysis. In-situ tensile tests combined with EBSD allow RA grain and neighboring attributes to be characterized and corresponding transformation data to be obtained. However, pseudo-symmetry of the ferrite (BCC) and martensite (BCT) phases prevents EBSD from accurately identifying all phases. Measuring the relative distortion of the crystal lattice, tetragonality, is one approach to identifying the phases. Unfortunately, small errors in the pattern center can cause significant errors in tetragonality measurement. Therefore, this research utilizes a new approach for accurate pattern center determination using a strain minimization routine and applies it to tetragonality maps for phase identification. Tetragonality maps based on dynamically simulated patterns result in the most accurate maps and can also be used to predict approximate local carbon content. Machine learning is then used on the collected data to isolate key attributes of RA grains and provide a decision tree model to predict transformation based on those attributes. Among the most relevant attributes found, RA grain area, RA grain shape aspect ratio, a “hardness” factor, and major axis orientation are included. Possible correlations between these factors and transformation improve understanding of relevant attributes and show the advantage that machine learning can have in unravelling complex material behavior.

Predicting the passenger flow inside a city is a vital component of the intelligent transportation management system. The proposal for a new residential area, an office space, post­pandemic policy implications for work from home, behavioral changes for revised traffic patterns, infrastructural improvements, require a visual and analytical backing which can be provided through a macro simulation model. This research explores the performance of the Machine learning (ML) based transport model against the predictions provided by the traditional Spatial Interaction Models (SIM) for the city of Oslo. The transport models and their parameters are analyzed for sensitivity analysis and scenario analysis to derive city character. Furthermore, the derived model is deployed over an interactive dashboard for analytical and their practical visualizations through infographics. The results show that the ML model outperforms the SIM. Although the traditional SIM has a clear advantage of being interpreted by design and requiring a few parameters, it suffers from its inability to accurately capture the structure of real flows and greater variability as compared to the ML model. Extensive statistical analyses are conducted to obtain significant results and realize the pros and cons of both the models which question the validity of results for the ML model over SIM. With this thesis, we discuss the potential of ML model detected trends of passenger flows, andtheir capacity to simulate city development­related scenarios for the traffic flows within the city.