Whilst considerable research has found that people tend to underestimate their task completion times (e.g., Buehler et al., 1994), factors that might influence the accuracy of temporal predictions have received little empirical treatment. The research presented in this thesis identified two distinct factors that mediated time estimation accuracy and bias. One factor was task duration, whereas the other factor was the person’s prior experience of the task. There was evidence that having prior experience of performing all or a substantial part of the same task enabled participants to more accurately estimate its duration. Additionally, predictions were more accurate when participants viewed tasks before making time estimates. Contrary to the theory of the planning fallacy (Kahneman & Tversky, 1979), these findings suggest that people do take account of their previous task performance, and use such distributional information to good effect. However, there was evidence of time prediction bias when unrelated tasks were completed beforehand, suggesting that erroneous information about previous task performance was used when making a subsequent estimate. The directional nature of time estimation bias was also highlighted in the present research. In general, there was some evidence of temporal overestimation on tasks with a duration of up to four or five minutes, whereas participants tended to underestimate their completion times on tasks that took between eight and 16 minutes to complete. These findings indicate that task duration influences the direction in which time estimates are biased (i.e., under or overestimation), with the temporal underestimation indicative of the planning fallacy occurring on tasks of at least eight minutes' duration. The present research has potential implications for task duration estimation in everyday life, and outlines conditions under which prediction bias can be reduced. The present findings are discussed in relation to the theory of the planning fallacy and the potential role of cognitive judgemental heuristics in determining temporal misestimation.

Knowledge of phylogenetic relationships among organisms is essential for anchoring evolutionary studies. Phylogenomic studies use large amounts of genetic data in analyses, which is particularly important for highly phenotypically variable taxa that are difficult to distinguish from one another without the use of genetic data, due to the abundance of homoplasy in morphological characters typically used in morphological classification. Use of genome-scale molecular data has thus become the gold standard for identifying these phylogenetic relationships, specifically in comparison to past studies based on fewer genes. Greater quantities of genetic data, in addition to finer taxon sampling, may lead to different conclusions about phylogenetic relationships among organisms compared to previous studies, necessitating new analyses on organisms when new discoveries of populations and new sources of genetic data arise. Ranitomeya poison frogs (Amphibia: Dendrobatidae) are an Amazonian lineage of dendrobatid frogs consisting of 16 species possessing remarkable diversity in color pattern, range size, and parental care behavior. I present the first phylogeny based on genomic data for all species in Ranitomeya, using maximum likelihood and multi-species coalescent methods. I used ultraconserved elements (UCEs), a genome-scale nuclear marker, as my source of molecular data to construct the tree. I also present divergence time estimations using the MCMCTree program. My results indicate several differences from previous analyses in terms of interspecific relationships. Notably, I find R. toraro and R. defleri constitute different species groups, and recover R. uakarii as paraphyletic. I also designate former populations of R. fantastica from Isla Pongo, Peru and Tarapoto as R. summersi, and transfer the French Guianan R. amazonica populations to R. variabilis. My study clarifies both interspecific and intraspecific relationships within Ranitomeya, and provides key insights into phylogeny that pave the way for future studies testing hypotheses on color pattern evolution and historical biogeography.

<p>Time estimation after wake-up from sleep has in earlier studies been shown to correlate with relative amount of slow-wave sleep. The aim of this study was to investigate if this effect could be found with subjects sleeping until spontaneous wake-up. Twenty-six women who slept alone at home, equipped with an actigraph and a questionnaire, participated in the study. The result showed that there was a positive correlation between time in bed, which was assumed to reflect relative amount of slow-wave sleep, and subjective time in relation to objective time. However, there also was a positive correlation between predicted wake-up time before going to sleep and subjective time in relation to objective time. This suggest that the former correlation might have been a result of pure intellectual guesses. Further studies, using more participants or different research designs, are needed to investigate or reject this eventual relationship.</p>

Time estimation after wake-up from sleep has in earlier studies been shown to correlate with relative amount of slow-wave sleep. The aim of this study was to investigate if this effect could be found with subjects sleeping until spontaneous wake-up. Twenty-six women who slept alone at home, equipped with an actigraph and a questionnaire, participated in the study. The result showed that there was a positive correlation between time in bed, which was assumed to reflect relative amount of slow-wave sleep, and subjective time in relation to objective time. However, there also was a positive correlation between predicted wake-up time before going to sleep and subjective time in relation to objective time. This suggest that the former correlation might have been a result of pure intellectual guesses. Further studies, using more participants or different research designs, are needed to investigate or reject this eventual relationship.

This master thesis is made in 2001 at Blekinge Institute of Technology and Symbian, which is a software company in Ronneby, Sweden. The purpose of the thesis is to find a suitable prediction and estimation model for the test effort. To do this, we have studied the State of the Art in cost/effort estimation and fault prediction. The conclusion of this thesis is that it is hard to make a general proposal, which is applicable for all organisations. For Symbian we have proposed a model based on use and test cases to predict the test effort.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第24079号 / 理博第4846号 / 新制||理||1693(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 田村 実, 教授 工藤 洋, 教授 松下 智直 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

As one of the kernel components of ITS technology, Travel Time Estimation (TTE) has been a high-interest topic in highway operation and management for years. Out of numerous vehicle detection technologies being applied in this project, intrusive loop detector, as the representative of spot measurement devices, is the most common. The ultimate goal of this dissertation is to seek a TTE approach based primarily on spot speed measurement and capable of successfully performing in a certain accuracy range under various traffic conditions. The provision of real-time traffic information could offer significant benefits for commuters looking to make optimum travel decisions. The proposed research effort attempts to characterize typical variability in traffic conditions using traffic volume data obtained from loop detectors on I-66 Virginia during a 3-month period. The detectors logged time-mean speed, volume, and occupancy measurements for each station and lane combination. Using these data, the study examines the spatiotemporal link and path flow variability of weekdays and weekends. The generation of path flows is made through the use of a synthetic maximum likelihood approach. Statistical Analysis of Variance (ANOVA) tests are performed on the data. The results demonstrate that in terms of link flows and total traffic demand, Mondays and Fridays are similar to core weekdays (Tuesdays, Wednesdays, and Thursdays). In terms of path flows, Fridays appear to be different from core weekdays. A common procedure for estimating roadway travel times is to use either queuing theory or shockwave analysis procedures. However, a number of studies have claimed that deterministic queuing theory and shock-wave analysis are fundamentally different, producing different delay estimates for solving bottleneck problems. Chapter 5 demonstrates the consistency in the delay estimates that are derived from both queuing theory and shock-wave analysis and highlights the common errors that are made in the literature with regards to shock-wave analysis delay estimation. Furthermore, Chapter 5 demonstrates that the area between the demand and capacity curves can represent the total delay or the total vehicle-hours of travel if the two curves are spatially offset and queuing theory has its advantages on this because of its simplicity. As the established relationship between time-mean and space-mean speed is suitable for estimating time-mean speeds from space-mean speeds in most cases, it is also desired to estimate the space-mean speeds from time-mean speeds. Consequently, Chapter 6 develops a new formulation that utilizes the variance of the time-mean speed as opposed to the variance of the space-mean speed for the estimation of space-mean speeds. This demonstrates that the space-mean speeds are estimated within a margin of error of 0 to 1 percent. Furthermore, it develops a relationship between the space- and time-mean speed variance and between the space-mean speed and the spatial travel-time variance. In addition, the paper demonstrates that both the Hall and Persaud and the Dailey formulations for estimating traffic stream speed from single loop detectors are valid. However, the differences in the derivations are attributed to the fact that the Hall and Persaud formulation computes the space-mean speed (harmonic mean) while the Dailey formulation computes the time-mean speed (arithmetic mean). Chapter 7 focuses on freeway Travel Time Estimation (TTE) algorithms that are based on spot speed measurements. Several TTE approaches are introduced including a traffic dynamics TTE algorithm that is documented in literature. This traffic dynamics algorithm is analyzed, highlighting some of its drawbacks, followed by some proposed corrections to the traffic dynamics formulation. The proposed approach estimates traffic stream density from occupancy measurements, as opposed to flow measurements, at the onset of congestion. Next, the study validates the proposed model using field data from I-880 and simulated data. Comparison of five different TTE algorithms is conducted. The comparison demonstrates that the proposed approach is superior to the TTE traffic dynamics approach. Particularly, a multi-link simulation network is built to test spot-speed-measurement TTE performance on multi links, as well as the data smoothing technique's effect on TTE accuracy. Findings further prove advantages of utilizing space-mean speed in TTE rather than time-mean speed. In summary, a feasible TTE procedure that is adaptive to various traffic conditions has been established. Since each approach would under-/over-estimate travel time depending on the concrete traffic condition, different models will be selected to ensure TTE's accuracy window. This approach has broad applications because it is based on popular loop detectors. / Ph. D.

Travel time information is important for Advanced Traveler Information Systems (ATIS) applications. People traveling on urban freeways are interested in knowing how long it will take them to reach their destinations, particularly under congested conditions. Though many advances have been made in the field of traffic engineering and ITS applications, there is a lack of practical travel time estimation procedures for ATIS applications. Automatic Vehicle Identification (AVI) and Geographic Information System (GPS) technologies can be used to directly estimate travel times, but they are not yet economically viable and not widely deployed in urban areas. Hence, data from loop detectors or other point estimators of traffic flow variables are predominantly used for travel time estimation. Most point detectors can provide this data efficiently. Some attempts have been made in the past to estimate travel times from point estimates of traffic variables, but they are not comprehensive and are valid for only particular cases of freeway conditions. Moreover, most of these methods are statistical and thus limited to the type of situations for which they were developed and are not of much general use. The purpose of current research is to develop a comprehensive system for travel time estimation on urban freeways for ATIS applications. The system is based on point estimates of traffic variables obtained from detectors. The output required from the detectors is flow and occupancy aggregated for a short time interval of 5 minutes. The system for travel time estimation is based on the traffic flow theory rather than statistical methods. The travel times calculated using this system are compared with the results of FHWA simulation package TSIS 5.0 and the estimation system is found to give reasonable and comparable results when compared with TSIS results. / Master of Science

Additive Manufacturing (AM) refers to the use of a group of technologies capable of combining material layer-by-layer to manufacture geometrically complex products in a single digitally controlled process step, entirely without moulds, dies or other tooling. AM is a parallel manufacturing approach, allowing the contemporaneous production of multiple, potentially unrelated, components or products. This thesis contributes to the understanding of the economic aspects of additive technology usage through an analysis of the effect of AM s parallel nature on economic and environmental performance measurement. Further, this work assesses AM s ability to efficiently create complex components or products. To do so, this thesis applies a methodology for the quantitative analysis of the shape complexity of AM output. Moreover, this thesis develops and applies a methodology for the combined estimation of build time, process energy flows and financial costs. A key challenge met by this estimation technique is that results are derived on the basis of technically efficient AM operation. Results indicate that, at least for the technology variant Electron Beam Melting, shape complexity may be realised at zero marginal energy consumption and cost. Further, the combined estimator of build time, energy consumption and cost suggests t AM process efficiency is independent of production volume. Rather, this thesis argues that the key to efficient AM operation lies in the user s ability to exhaust the available build space.

Emergency services has a vital function in society, and except saving lifes a functioning emergency service system provides the inhabitants of any give society with a sence of feeling secure. Because of the delicate nature of the services provided there is always an interest in improvement with regards to the performance of the system. In order to have a good system there are a variety of models that can be used as decision making support. An important component in many of these models are the travel time of an emergency vehicle. In In this study the focus lies in travel time estimation for the emergency services and how it could be estimated by using a neural network, called a deep learning process in this report. The data used in the report is map matched GPS points that have been collected by the emergency services in two counties in Sweden, Östergötland and Västergötland. The map matched data has then been matched with NVDB, which is the the national road database, adding an extra layer of information, such as roadlink geometry, number of roundabouts etc. To find the most important features to use as input in the developed model a Pearson and Spearman correlation test was performed. Even if these two tests do not capture all possible relations between features they still give an indication of what features that can be included. The deep learning process developed within this study uses route length, average weighted speed limit, resource category, and road width. It is trained with 75% of the data leaving the remaining 25% for testing of the model. The DLP gives a mean absolute error of 51.39 when trained and 59.21 seconds when presented with new data. This in comparison a simpler model which calculates the travel time by dividing the route length with the weighted averag speed limt, which gives a mean absolute error of 227.48 seconds. According to the error metrics used in order to evaluate the models the DLP performs better than the current model. However there is a dimension of complexity with the DLP which makes it sort of a black box where something goes in and out comes an estimated travel time. If the aim is to have a more comprehensive model, then the current model has its benefits over a DLP. However the potential that lies in using a DLP is entruiging, and with a more in depth analysis of features and how to classify these in combination with more data there may be room for developing more complex DLPs.