The purpose of this study is to develop a geotechnical microzonation model regarding the suitability of the residential areas in Yenisehir (Bursa, Turkey), which is a currently developing settlement area in a seismically active region. For this purpose, soil properties and dynamic soil behaviors of the study area were assessed. Soil classification, soil amplification, natural soil predominant period, resonance phenomena and liquefaction potential of the study area were evaluated using borehole data and microtremor measurements. The raw data obtained from the previous studies carried out at Yenisehir were used for these assessments. The liquefaction potential for the study area was evaluated both in two-dimensional planimetric and three-dimensional volumetric assessments. Two geotechnical microzonation maps were produced for the study area according to the surface damage due to liquefaction (according to two different methods), soil amplification and distance to streams maps / by using Geographical Information Systems (GIS) based Multi-Criteria Decision Analysis. The weight values were assigned to the layers using Analytical Hierarchical Process method by pairwise comparisons. Evaluating geotechnical microzonation maps produced, the safest areas were found on the northern sites of the study area. The most critical areas were found to be in the middle and the southeast parts of the study area.

The construction of dams and hydro-power stations are some of the most common anthropogenic changes of watercourses and rivers. While being important to humans and society by providing electricity, these obstructions of watercourses can have severe consequences for the aquatic ecosystems. One consequence is that dams often hinder the important movement of migrating fish species between habitats. This can lead to decline and even extinction of important fish populations. To prevent these negative effects, a number of different fish passage systems, including nature-like fishways, have been developed. Nature-like fishways mimic natural streams in order to function as a natural corridor for a wide range of species. Planning and construction of a nature-like fishway is a complex task that often involves many different interests. In the present study a combination of multi-criteria decision analysis and least-cost path analysis is used for determining the best location for a nature-like fishway past Strömdalen dam in Gavleån, Sweden. An anisotropic least-cost path algorithm is applied on a friction-layer and a digital elevation model, and the least-cost path for a nature-like fishway is determined. The results show that the method is useful in areas of varying topography and steep slopes. However, because low slope is a very important factor when constructing a nature-like fishway, slope becomes the dominating factor in this analysis at the expense of e.g. distance to roads. Combining the methods with results from biological studies of fish behavior and detailed hydrological modelling would provide a very strong tool for the planning of nature-like fishways.

The purpose of my research is to contribute to the field of forecasting from a methodological perspective as well as to the field of crude oil as an application area to test the performance of my methodological contributions and assess their merits. In sum, two main methodological contributions are presented. The first contribution consists of proposing a mathematical programming based approach, commonly referred to as Data Envelopment Analysis (DEA), as a multidimensional framework for relative performance evaluation of competing forecasting models or methods. As opposed to other performance measurement and evaluation frameworks, DEA allows one to identify the weaknesses of each model, as compared to the best one(s), and suggests ways to improve their overall performance. DEA is a generic framework and as such its implementation for a specific relative performance evaluation exercise requires a number of decisions to be made such as the choice of the units to be assessed, the choice of the relevant inputs and outputs to be used, and the choice of the appropriate models. In order to present and discuss how one might adapt this framework to measure and evaluate the relative performance of competing forecasting models, we first survey and classify the literature on performance criteria and their measures – including statistical tests – commonly used in evaluating and selecting forecasting models or methods. In sum, our classification will serve as a basis for the operationalisation of DEA. Finally, we test DEA performance in evaluating and selecting models to forecast crude oil prices. The second contribution consists of proposing a Multi-Criteria Decision Analysis (MCDA) based approach as a multidimensional framework for relative performance evaluation of the competing forecasting models or methods. In order to present and discuss how one might adapt such framework, we first revisit MCDA methodology, propose a revised methodological framework that consists of a sequential decision making process with feedback adjustment mechanisms, and provide guidelines as to how to operationalise it. Finally, we adapt such a methodological framework to address the problem of performance evaluation of competing forecasting models. For illustration purposes, we have chosen the forecasting of crude oil prices as an application area.

<p dir="ltr">Most road fatalities are caused by human error. To help mitigate this issue and enhance overall transportation safety, companies are turning to advanced driver assistance systems and autonomous vehicle development. Perception, a key module of these systems, mostly uses light detection and ranging (LiDAR) sensors and enables efficient obstacle detection and environment mapping. Extensive research on the use of LiDAR for autonomous driving has been documented in the literature. Yet still, several researchers and practitioners have advocated continued investigation of LiDAR placement alternatives. To address this research need, this thesis research begins with a comprehensive review of different sensor technologies – camera, radio detection and ranging, global positioning system, and inertial measurement units – and exploring their inherent strengths and limitations. Next, the thesis research developed a methodological multiple criteria framework and implemented it in the context of LiDAR placement optimization. Given the numerous criteria and placement alternatives associated with LiDAR placement, multi-criteria decision analysis (MCDA) was identified as an effective tool for LiDAR placement optimization. MCDA has been applied to some extent in decision-making regarding autonomous vehicle development. However, its application in LiDAR placement optimization remains unexplored. In evaluating the LiDAR placement alternatives, the research first established the placement alternatives and then developed a comprehensive yet diverse set of criteria – point density, blind spot regions, sensor cost, power consumption, sensor redundancy, ease of installation, and aesthetics. The data collection methods included the CARLA simulator, sensor datasheets, and questionnaire surveys. The relative importance among the evaluation criteria was established using weighting techniques such as respondent-assigned weighting, equal weighting, and randomly generated weighting. Then, to standardize the different measurement units, scaling was carried out using value functions developed for each criterion using data from the respondents. Finally, the weighted and scaled criteria measures were amalgamated to obtain the overall evaluation score for each alternative LiDAR placement design. This enabled the ranking of the placement designs and the identification of the best-performing and worst-performing designs. Hence, the optimization method used is the enumeration technique. The findings of this study serve as a reference for future similar efforts that seek to optimize LiDAR placements based on select criteria. Further, it is expected that the thesis’s framework will contribute to an enhanced understanding of the overall impact of LiDAR placement on autonomous vehicles, thus enabling the cost-effective design of their placement and, ultimately, improving AV operational outcomes, including traffic safety.</p>

Our country is a risky position in terms of natural disasters. In the long run, preferentially settlement areas were selected to ensure maximum benefits in terms of both economic and security aspects, other criteria is not taken account when selection of sites. The main purpose of this study is to examine and compare the properties of settlement location based on natural hazard and environmental constraints to be able to understand the interaction between the settlements and natural conditions at the regional scale of YeSilirmak Basin. A MCDA was set up with 10 different data layers in two data domains (environmental and natural hazards domains), are evaluated. The results of the MCDA scores are then transferred to settlement databases in order to evaluate the number of existing settlements in different environmental and natural hazard related suitability classes. It is found that almost 29% of YeSilirmak catchment is environmentally favorable for settlement, and in coherence with that 41% of all existing settlements are located in this zone, indicating a clear preference among the perception of environmentally better places to be settled in. On the other hand with respect to the natural hazards dataset, the locations of the settlements fail to create any preference, as 73,32% of the area is used by 73,50% of existing settlements, which indicates that the perception of natural hazards are low and do not effect settlement criteria, while the acceptable risk of community is high.

Bike share system (BSS) has received increasing attention in research for its potential economic and environmental benefits. However, some research has pointed out the negative sustainability impacts of BSS from rebalancing activity, due to its greenhouse gas (GHG) emissions and additional vehicle travels. Additionally, bike and station manufacturing also bring considerable emissions to the system. Therefore, it is important to analyze the current rebalancing efficiency and sustainability of BSSs, and to assist the BSS operators in optimizing the BSS design. Existing studies lack tools to estimate the real-world rebalancing activities and vehicle usage for system sustainability evaluation and improvements. To address this gap, this research first proposed a framework to estimate rebalancing activities and applied a clustering-based method to estimate the rebalancing vehicle use. Applying the framework to the BSSs in Chicago, Boston, and Los Angeles, this study estimated the rebalancing operation and compared the rebalancing efficiencies among the three systems. The analysis results show that 1) only a small proportion of stations and bikes were involved in the daily rebalancing activities; 2) most rebalancing activities were operated during the daytime, while the overnight rebalancing was limited; 3) the system scale, trip demand, and station types are critical for the rebalancing efficiency; and 4) reducing the rebalancing activities at self-rebalance stations could help to improve the rebalancing efficiency and benefits system sustainability. Additionally, the sustainability performance (e.g., carbon emissions) of BSS is not only decided by the rebalance, but also the manufacturing of bikes and stations. It is important to consider all these factors when optimizing a BSS. The existing literature on system improvement for the BSSs lacks an integrated view, and a well-designed integrated model for current BSS improvement is needed. The second part of this thesis built a simulation-based optimization model and generated 2400 scenarios for evaluation. This model aims to minimize the expansion investment, rebalancing mileage, and maximize the system demand and service rate. A Weight Sum Model is applied to solve the multi-criteria decision analysis. The model results show that the best system improvement is to build a new station with a small capacity and initial bikes. The investment and location impacts are discussed to find the tradeoff among expansion strategies. A sensitivity analysis is conducted to evaluate how different weight combinations (refer to different preferences in decision making) impact the preferred station configuration (docks and bikes) and new station locations.

<p dir="ltr">Highway safety continues to pose a serious challenge to the social sustainability of transportation systems, and initiatives are being pursued at all levels of government to reduce the high fatality count of 42,000. At the same time, it is sought to ensure higher travel efficiency in order to increase economic productivity. The emergence of automated transportation provides great promise to mitigate these ills of the transportation sector that have persisted for so many decades. With regards to safety, such promise is rooted in the capability of autonomous vehicles to self-drive some or all of the time, thus reducing the impact of inherently errant human driving to which 95% of all crashes have been attributed. With regards to mobility, such promise is guided by the capability of the autonomous vehicle to carry out path planning, navigation, and vehicle controls in ways that are far more efficient than the human brain, thereby facilitating mobility and reducing congestion-related issues such as delay, emissions, driver frustration, and so on.</p><p dir="ltr">Unfortunately, the two key outcomes (safety and mobility) are reciprocal in the sense that navigation solutions that enhance safety generally tend to reduce mobility, and vice versa. As such, there is a need to assign values explicit to these performance criteria in order to develop balanced solutions for AV decisions. Most existing machine-learning-based path planning algorithms derive these weights using a learning approach. Unfortunately, the stability of these weights across time, individuals, and trip types, is not guaranteed. It is necessary to develop weights and processes that are trip situation-specific. Secondly, user trust in automation remains a key issue, given the relatively recent emergence of this technology and a few highly-publicized crashes, which has led to reservations among potential users.</p><p dir="ltr">To address these research questions, this thesis identifies various situational contexts of the problem, identifies the alternatives (the viable trajectories by fitting curves between the vehicle maneuver’s initial and final positions), develops the decision criteria (safety, mobility, comfort), carries out weighting of the criteria to reflect their relative significance, and scales the criteria to develop dimensionless equivalents of their raw values. Finally, a process for amalgamating the overall impacts of each driving decision alternative is developed based on the weighted and scaled criteria, to identify the best decision (optimal trajectory path). This multi-criteria decision making (MCDM) problem involves the collection of data through questionnaire surveys.</p><p dir="ltr">The weights obtained early in the MCDM process could be integrated into any one of two types of planning algorithms. First, they could be incorporated into interpolating curve-based planning algorithms, to identify the optimal trajectory based on human preferences. Additionally, they can be integrated into optimization-based planning algorithms to allocate weights to the various functions used.</p><p dir="ltr">Overall, this research aims to align the behavior of autonomous vehicles closely with human-driven vehicles, serving two primary purposes: first, facilitating their seamless coexistence on mixed-traffic roads and second, enhancing public acceptance of autonomous vehicles.</p>

The understanding that humans are bounded in their rationality has been proven to manifest in complex decision making as a result of a limit in the amount of information available, the cognitive limitations of the mind and the amount of time available in which to make a decision. Because of this, humans have been known to appeal to heuristics and the rules of thumb (termed 'satisficing‘) when making decisions, resulting in biased probability judgments and not maximizing expected utility. Corporate application of bounded rationality is still very limited. This study builds on and advances the study and application of bounded rationality in corporate environments, using climate change response as a real-life situation, and in a circular fashion help explain some of the debates and paradoxes that agitate researchers from the climate change community. Using a mixed methods comparative case study of two organisations‘ responses to climate change, the study theorises that competitive market forces and the ability of organisations to learn from other organisations limits the levels of 'satisficing‘ in strategic decision making. Instead, the limited amount of information and the fear of the unknown cause organizations to approach the subject cautiously. A tactical interpretive climate change response framework emerges. / Business Management / D.B.L.

This research presents an overview of different sustainable energy development scenarios in Central Europe and East Asia, and is aimed to evaluate the efficiency and availability for introducing a specific sustainable energy source. Accordingly: wind, hydropower, solar, bioenergy, geothermal, nuclear energy. By conducting analysis though multi criteria decision analysis (MCDA) and analytic hierarchy process (AHP) models, divergences among energy options in Central Europe and East Asia are emphasised due to its preferences in hierarchy. A short introduction, related to the present energy outlook with a series of relative regressions and a case study based on corresponding statistics, is presented firstly. This gives insights to assess the evaluation of sustainable energy development options. Evaluation results indicating Central Europe and East Asia should introduce different sustainable energy technologies on account of their own strengths and drawbacks in energy judgements and criterions. Keywords Sustainable energy, energy development, Central Europe, East Asia, energy scenario, energy option, evaluation, multi criteria decision analysis (MCDA), analytic hierarchy process (AHP)