Traffic problems caused by congestion are increasing in cities all over the world. As a traffic management tool traffic predictions can be used in order to make prevention actions against traffic congestion. There is one software for traffic state estimations called Mobile Millennium Stockholm (MMS) that are a part of a project for estimate real-time traffic information.In this thesis a framework for running traffic predictions in the MMS software have been implemented and tested on a stretch north of Stockholm. The thesis is focusing on the implementation and evaluation of traffic prediction by running a cell transmission model (CTM) forward in time.This method gives reliable predictions for a prediction horizon of up to 5 minutes. In order to improve the results for traffic predictions, a framework for dynamic inputs of demand and sink capacity has been implemented in the MMS system. The third part of the master thesis presents a model which adjusts the split ratios in a macroscopic traffic model based on driver behavior during congestion.

Environmental Impact Assessment (EIA) is recognised worldwide as a tool for identifying the potential adverse effects of a proposed development on the environment. Very little attention has been given to determining the actual environmental effects resulting from a development. The need for EIA follow–up (i.e. monitoring, auditing, evaluation, management and communication) was identified and would form the building blocks within the EIA process. Follow–up provides information about the consequences of an activity and presents opportunities to implement adequate mitigation measures. EIA follow–up is not developed to its full potential even though the need for it is acknowledged and supported in legislation, scientific journals and scientific books. EIA follow–up necessitates feedback in the EIA process to ensure lessons learnt and outcomes from past experiences can be applied in future actions. Follow–up is only a legal requirement if conditions are specified in the environmental authorisation (EA). Of particular concern to follow–up is the accuracy of prediction and secondly, the level of compliance to conditions set out in the authorization and management plans. This study will focus primarily on critically analysing predictions and compliance from the construction phase of a high profile mega shopping mall project, namely the Mooi River Mall (MRM), with an analysis to gauge the actual effect and contribution of the EIA process to decision making and implementation practices. Multiple data sources were used to determine the accuracy of predictions and legal compliance level of the Mooi River Mall. The Mooi River Mall's accuracy of predictions (66%) and legal compliance (83%) suggest that some of the impacts were unavoidable; that mitigation measures were either not implemented or identified or that EIA follow–up served its purpose in the form of implementing effective auditing programmes to monitor legal compliance. / Thesis (M.Sc (Environmental Science))--North-West University, Potchefstroom Campus, 2011.

Environmental Impact Assessment (EIA) is recognised worldwide as a tool for identifying the potential adverse effects of a proposed development on the environment. Very little attention has been given to determining the actual environmental effects resulting from a development. The need for EIA follow–up (i.e. monitoring, auditing, evaluation, management and communication) was identified and would form the building blocks within the EIA process. Follow–up provides information about the consequences of an activity and presents opportunities to implement adequate mitigation measures. EIA follow–up is not developed to its full potential even though the need for it is acknowledged and supported in legislation, scientific journals and scientific books. EIA follow–up necessitates feedback in the EIA process to ensure lessons learnt and outcomes from past experiences can be applied in future actions. Follow–up is only a legal requirement if conditions are specified in the environmental authorisation (EA). Of particular concern to follow–up is the accuracy of prediction and secondly, the level of compliance to conditions set out in the authorization and management plans. This study will focus primarily on critically analysing predictions and compliance from the construction phase of a high profile mega shopping mall project, namely the Mooi River Mall (MRM), with an analysis to gauge the actual effect and contribution of the EIA process to decision making and implementation practices. Multiple data sources were used to determine the accuracy of predictions and legal compliance level of the Mooi River Mall. The Mooi River Mall's accuracy of predictions (66%) and legal compliance (83%) suggest that some of the impacts were unavoidable; that mitigation measures were either not implemented or identified or that EIA follow–up served its purpose in the form of implementing effective auditing programmes to monitor legal compliance. / Thesis (M.Sc (Environmental Science))--North-West University, Potchefstroom Campus, 2011.

Many workers are daily exposed to chemical risks in their work place that has to be assessed and controlled. Due to exposure variability, repeated and random measurements should be conducted for valid estimates of the average exposure. Traditionally, experts such as safety engineers, work environment inspectors, and occupational hygienists, have performed the measurements. In self assessment of exposure (SAE), the workers perform unsupervised exposure measurements of chemical agents. This thesis studies a prerequisite for SAE, i.e. the workers’ mental models of chemical exposure. Further, the workers’ mental models are contrasted with experts’ reasons and decision criteria for measurement. Both qualitative and quantitative data generated from three studies (Paper I, II, and III) were used to describe the workers’ mental model of chemical exposure. SAE was introduced to workers in three different industries; transports (benzene), sawmill industry (monoterpenes), and reinforced plastic industry (styrene). By interviews, qualitative data were collected on the workers’ interpretation of measurement results and preventive actions. To evaluate the validity of worker measurement, the measurements were compared with expert measurements. The association between each worker’s number of performed measurement and mean level and variability in exposure concentrations was calculated. Mean absolute percent/forecast error (MAPE) was used to assess whether the workers’ decision models were in accordance with a coherence or correspondence model. In Paper IV, experts (safety engineers, work environment inspectors, and occupational hygienists) were interviewed to elucidate their mental models about the triggers and decision criteria for exposure measurements. The results indicate that the workers’ measurement results were in agreement with experts’. However, the measurement results were not a strong enough signal to induce workers to take preventive actions and sustained exposure measurements even if the measurement result were close to the occupational exposure limit. The fit was best for the median model, indicating that the workers’ mental models for interpretation of measurement data can best be described by the coherence theory rather than by the correspondence theory. The workers seemed to mentally reduce the variation in the exposure to a measure of central tendency (the median), and underestimated the average exposure level. The experts were found to directly take preventive actions instead of performing exposure measurements. When they performed exposure measurements, a worst case sampling strategy was most common. An important trigger for measurement for the experts was “request from the employer” (safety engineers), “legal demands” (work environment inspectors), and “symptoms among workers” (occupational hygienists). When there was a trigger, all experts mentioned expectations of high exposure level as a decision criterion for measurements. In conclusion, the studies suggest that workers’ mental interpretation model is best described in terms of a coherence model rather than a model of correspondence. The workers reduced the variation mentally in favor of an estimate of average exposure (median), which may imply that they underestimate short-term, high exposure health risks. A consequence is that interpretation of measurements such as SAE cannot be given to the individual worker without some support, e.g. from an expert. However, experts often chose to directly take preventive actions, without measuring the exposure. The results indicate that also the experts need support e.g. from the legal system if exposure measurements are to be done.

To manage an uncertain future relevant societal groups, such as government and corporate sectors, utilise economic forecasts to help plan future strategies. Many vital decisions are based on economic forecasts. Economists have traditionally been the professionals employed as economic forecasting experts. The dominant paradigm for present day forecasting is the "rational expectations theory", which assumes that a forecaster is capable of making optimal use all of the available information. The field of psychology offers a different, yet complementary, approach to the topic of economic forecasting. The aim of the current study was to research mental processes and behaviours of individuals participating in a forecasting task. The role of the following psychological variables within economic prediction behaviour was assessed: (1) task complexity, (2) decision making style, (3) the anchoring and adjustment heuristic, (4) the framing effect, and (5) personal feelings about the task content. All of these variables were hypothesised to have a direct influence on prediction behaviour. In addition, task complexity and decision making style were assumed to moderate the influence of the other psychological variables. A conceptual framework was designed to depict the assumed relationships. (For complete abstract open document)

abstract: The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning techniques today are not yet fully equipped to be trusted with this critical task. This work seeks to address this fundamental knowledge gap. Existing approaches that provide a measure of confidence on a prediction such as learning algorithms based on the Bayesian theory or the Probably Approximately Correct theory require strong assumptions or often produce results that are not practical or reliable. The recently developed Conformal Predictions (CP) framework - which is based on the principles of hypothesis testing, transductive inference and algorithmic randomness - provides a game-theoretic approach to the estimation of confidence with several desirable properties such as online calibration and generalizability to all classification and regression methods. This dissertation builds on the CP theory to compute reliable confidence measures that aid decision-making in real-world problems through: (i) Development of a methodology for learning a kernel function (or distance metric) for optimal and accurate conformal predictors; (ii) Validation of the calibration properties of the CP framework when applied to multi-classifier (or multi-regressor) fusion; and (iii) Development of a methodology to extend the CP framework to continuous learning, by using the framework for online active learning. These contributions are validated on four real-world problems from the domains of healthcare and assistive technologies: two classification-based applications (risk prediction in cardiac decision support and multimodal person recognition), and two regression-based applications (head pose estimation and saliency prediction in images). The results obtained show that: (i) multiple kernel learning can effectively increase efficiency in the CP framework; (ii) quantile p-value combination methods provide a viable solution for fusion in the CP framework; and (iii) eigendecomposition of p-value difference matrices can serve as effective measures for online active learning; demonstrating promise and potential in using these contributions in multimedia pattern recognition problems in real-world settings. / Dissertation/Thesis / Ph.D. Computer Science 2010

Orientador: Prof. Dr. André Mascioli Cravo / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, 2016. / We constantly learn and update our predictions about when events we cause will occur. This flexibility is important to program motor actions and to estimate when errors have been made. However, the mechanisms that govern learning and updating in temporal domain are largely unknown. In order to clarify these mechanisms we had three mains objectives: 1. To describe how we learn a new temporal relation between two events and how expectation is updated based on new information; 2. To describe the neural correlates underlying temporal learning and temporal updating; 3. To investigate temporal learning in two different sensory modalities: vision and audition, in order to verify whether such processes occur independently of sensory modality. In order to achieve the objectives, we developed two different experiments with electroencephalography recordings. In the first experiment, we aimed to answer the first two objectives by developing a behavioral task in which participants had to monitor whether a temporal error had been made. Results evidenced a rapid temporal adjustment by the participants to a new temporal relation. Temporal errors evoked electrophysiological markers classically related to error coding as frontal theta oscillations and feedback-related negativity. Delta phase was modulated by behavioral adjustments, suggesting its importance in temporal prediction updating. In conclusion, low frequency oscillations appear to be modulated in error coding and temporal learning. The second experiment investigated temporal learning in two different sensory modalities. Results indicated that time perception is biased differently depending on temporal marker sensory modality. Besides, we found that intertrial phase coherence of theta oscillations was modulated by expectation on both sensory conditions. However, such result occurs on central electrodes analysis, but not on sensory electrodes analysis, indicating a supramodal mechanism of temporal prediction.

In this paper, we provide a parsimonious means of estimating panel VARs with stochastic volatility. We assume that coefficients associated with domestic lagged endogenous variables arise from a finite mixture of Gaussian distribution. Shrinkage on the cluster size is introduced through suitable priors on the component weights and cluster-relevant quantities are identified through novel normal-gamma shrinkage priors. To assess whether dynamic interdependencies between units are needed, we moreover impose shrinkage priors on the coefficients related to other countries' endogenous variables. Finally, our model controls for static interdependencies by assuming that the reduced form shocks of the model feature a factor stochastic volatility structure. We assess the merits of the proposed approach by using synthetic data as well as a real data application. In the empirical application, we forecast Eurozone unemployment rates and show that our proposed approach works well in terms of predictions. / Series: Department of Economics Working Paper Series

As a result of a growing population, the city of Stockholm is facing many challenges. Getting more people to travel by public transportation is a key factor in coping with this increased urbanization. In the strive for increased ridership, it is the Stockholm Public Transport Administration’s (SL) job to make sure that the services provided are of high quality. One of these services is the real time bus departure predictions provided to the travellers through digital signs or by web and mobile applications. Due to a lack of proper tools, SL has unfortunately not yet been able to establish a systematic assessment of the quality of these bus prediction. The goal of this study was to help SL find such tools and solutions for assessing the quality of bus predictions. More specifically, the purpose of the study was to investigate the concept of prediction quality and identify suitable statistical tools for measuring quality. In order to do this a comprehensive literature study has been conducted. The findings of the literature study were then tested in practice in order to answer how such quality measurements should be made in the context of SL’s ITinfrastructure. This was answered by carrying out a pilot study in which the prediction quality was assessed on data from one week for a specific bus line. From the initial literature study, it was concluded that there are many dimensions that potentially affect the traveller’s perception of bus prediction quality. However, it was also concluded that a quality assessment plausibly should start with an evaluation of the precision. In order to assess the precision, several types of descriptive measures and analytical perspectives were proposed. As of how these findings should be made in the context of SL’s IT-systems, a method for creating observations from the available prediction data was presented. It was also concluded that in order to mirror the travellers experience, the prediction data should be collected “late” in the process of bus prediction generation.

De acordo com Gray (1982) o sistema nervoso monitora o ambiente e o comportamento continuamente, sendo capaz de inibir o comportamento em curso quando se depara com novidades ou com discrepâncias entre expectativas geradas com base em memórias de regularidades passadas e a informação sensorial presente, de modo a explorar a fonte de novidade ou discrepância e, assim, obter informações que possibilitem gerar previsões melhores no futuro. O sistema septo-hipocampal compararia estímulos presentes com informações antecipadas (ou previstas). Tal sistema envolve um comparador, o subículo, que receberia informações do presente através de aferências neocorticais, via córtex entorrinal, e informações \"previstas\" geradas em um \"circuito gerador de previsões\". Gray (1982) propôs que esse circuito gerador de previsões inclui o subículo, os corpos mamilares, o tálamo anteroventral, o córtex cingulado e, novamente, o subículo. Destas estruturas, o tálamo anteroventral encontra-se em posição privilegiada, do ponto de vista hodológico e experimental, para investigar este postulado circuito gerador de previsões. O objetivo do presente trabalho foi investigar o efeito da lesão seletiva no tálamo anteroventral, pela aplicação tópica de ácido N-metil-D-aspártico (NMDA), sobre a habilidade de ratos extrapolarem a partir de padrões seriais de estímulos. Tampão fosfato foi aplicado em sujeitos controle. Ratos da linhagem Wistar, machos, foram treinados a correr em uma pista reta para receberem reforço ao seu final. Em cada sessão (uma sessão por dia), os animais correram 4 tentativas sucessivas, recebendo quantidades diferentes de sementes de girassol em cada tentativa. No padrão monotônico decrescente os sujeitos receberam 14, 7, 3 e 1 sementes de girassol, enquanto os sujeitos expostos ao padrão não-monotônico receberam 14, 3, 7 e 1 sementes de girassol. Os animais foram treinados ao longo de 31 sessões. No 32° dia do experimento, uma quinta tentativa, nunca antes experienciada pelos animais, foi adicionada à sessão. Como esperado, os tempos de corrida na quinta tentativa dos animais controle expostos ao padrão monotônico decrescente foram substancialmente maiores se comparados aos animais controle expostos ao padrão não-monotônico, indicando a ocorrência de extrapolação. Em contraste, os sujeitos lesados expostos ao padrão monotônico não exibiram esse aumento de latência na quinta corrida, indicando que esses animais não extrapolaram. Em conclusão, os resultados indicam que extrapolação a partir de padrões seriais de estímulos é prejudicada pela lesão seletiva do tálamo anteroventral / According to Gray (1982) the brain continuously monitors environment and behavior, being capable of inhibiting ongoing behaviors when facing novelty or detecting discrepancies involving predictions generated from memories of past regularities and the actual sensorial information, in order to explore the source of novelty and/or discrepancy, and thus to gather information for generating better predictions in the future. The septo-hippocampal system compares anticipated and present information. The comparator would be the subiculum. This brain structure would receive present information from neocortical afferents, via the entorhinal cortex, and expected information from a \"generator of predictions system\" including the subiculum, mammillary bodies, anteroventral thalamus, cingulate cortex and, again, the subiculum. The anteroventral thalamus is in a privileged position, both hodologically and experimentally, to allow investigation of this postulated generator of predictions system. This study investigated the effect of selective damage to the anteroventral thalamus, by topical application of N-Methyl-D-Aspartic acid (NMDA), on the ability of rats to extrapolate relying on serial stimulus patterns. Control subjects were injected with phosphate buffer. Male Wistar rats were trained to run through a straight alleyway to get rewarded. In each session (one session per day) the animal run four successive trials, one immediately after the other, receiving different amounts of sunflower seeds in each trial. While subjects exposed to the monotonic decremental schedule received 14, 7, 3, 1 sunflower seeds along trials, subjects exposed to the non-monotonic schedule received 14, 3, 7, 1 sunflower seeds. Subjects were trained along 31 sessions. Then, on the 32nd testing session, a fifth trial never experienced before by all subjects was included immediately after the fourth trial. As expected, running times on the fifth trial for Control subjects exposed to the monotonic schedule were significantly longer as compared to the corresponding scores of Control subjects exposed to the non-monotonic schedule, thus indicating the occurrence of extrapolation. In contrast, lesioned subjects exposed to the monotonic schedule did not exhibit this increase in running times on the fifth trial thus indicating that these subjects did not extrapolate. In conclusion, results indicate that extrapolation relying on serial stimulus patterns is disrupted following selective damage to the anteroventral thalamus