Transport infrastructure projects, including tunneling, suffer from timedelays and cost overrun. A literature review shows that the effect ofuncertainty has been neglected in explaining time and cost overrunmeaning that technical explanations matter. Probabilistic estimations oftime and cost can be employed for dealing with uncertainty in transportinfrastructure projects.In this licentiate thesis, KTH’s probabilistic time and cost estimationmodel for tunneling projects (Isaksson and Stille, 2005, Rock Mech. RockEng., 38, 373-398) was improved. The improvements include breakingdown the production activities into their sub-activities, which form thebasis for assessing times (or costs) for tunnel construction. In addition, theexceptional time and the length of model’s geotechnical zones aredescribed as stochastic variables instead of deterministic values used in theoriginal model. Given its hierarchical structure, the model can be used fortime and cost estimation of all types of tunnels and all constructionmethods in various geological condition.The improved version of the model uses three types of input parametersthat are probabilities of occurrences of different geological condition andidentified undesirable events, production effort of sub-activities (i.e. timespent for performing the sub-activity per unit length of tunnel) andadditive delay time that is imposed as a result of occurrence of undesirableevents. The important issues in modeling the uncertainty in the productionefforts of sub-activities are also explained. / Transportinfrastrukturprojekt, inklusive tunnelbyggnad, lider ofta avförseningar och ökade kostnader. En litteraturgenomgång visar atteffekten av osäkerhet inte har beaktats när man försöker förklara orsakentill förseningar och kostnadsökningar, vilket betyder att osäkerheten omgeotekniska förhållanden mycket väl kan spela en stor roll. För att hanteradenna osäkerhet när man bedömer tid och kostnad för tunnelprojekt kansannolikhetsbaserade metoder användas.I denna licentiatuppsats förbättrades den sannolikhetsbaserade modellför tid och kostnadsskattning i tunnelprojekt som tidigare utvecklats påKTH (Isaksson och Stille, 2005, Rock Mech. Rock Eng., 38, 373-398). Denviktigaste förbättringen var att dela upp produktionsaktiviteterna idelaktiviteter, för vilka man sedan enklare kan bedöma tidsåtgång (ellerkostnader). Dessutom beskrivs exceptionella förseningar och längden påmodellens geotekniska zoner nu med stokastiska variabler istället för meddeterministiska värden. Modellen är flexibel nog att kunna användas förtids- och kostnadsskattning av alla typer av tunnelprojekt ochkonstruktionsmetoder i olika geologiska miljöer.Den förbättrade versionen av modellen använder tre typer avindataparametrar: sannolikheter för förekomster av olika geologiskatillstånd och identifierade oönskade händelser; produktionsinsats fördelaktiviteter (d.v.s. den tid som används för att utföra delaktiviteten permeter tunnel); samt försening som orsakas av oönskade händelser.Uppsatsen diskuterar även de viktigaste aspekterna vid modellering avosäkerheten i produktionsinsatsen för delaktiviteter. / <p>QC 20211102</p>

Travel time estimation can be used in strategical distribution of ambulances and ambulance stations. A more accurate travel time estimation can lead to a better distribution of these ambulance sites. External factors such as weather and traffic conditions can affect the travel time from a starting location to a destination. In this work, we investigate how the SOS Alarm dataset of ambulance trips data and the machine learning model Gradient Boosted Decision Trees can be used to estimate travel time, and how these estimationscan be improved by incorporating aforementioned conditions when predicting travel time. Results showed that reasonable performance can be achieved for a subset of data where the precise origin and destination is known compared to a subset where the precise origin is unknown, and that traffic conditions could improve model performance on a subset of data containing trips only for a single route. Including weather represented as individual weather parameters did not, however, lead to enhanced performance.

Time estimation is an important aspect in project management. Failure to make accurateestimates can lead to large consequences. Despite this, humans tend to make fairly inaccurateestimates when tasked to, often underestimating the time something will take substantially. Thisthesis explores using artificial intelligence and machine learning to produce time estimates forthe life science company Biotage. A predictive model can be trained using previous projects assamples, including time reporting data for employees as the output variable.A total of 12 completed projects were found that had both sufficient time reporting data andsome project information. Previous projects took on average 55.1% longer to complete thanestimated at the start of the project. Every project had one or more of the following: projectdescription, work breakdown structure and/or Gantt chart. However, the level of detail in almostall of the projects was very low, making it difficult to extract useful features. A constant-timemodel (predicting that every project takes the same amount of time), had a Root Mean SquaredError (RMSE) of 5058 hours and a Mean Absolute Percentage Error (MAPE) of 282%. Anothermodel that took into account whether the project was a software only, hardware only or both hada RMSE of 4269 hours and MAPE of 320%. Due to the scarcity of data, no furtherimprovements were made. It was determined that in order to develop a predictive model thatcan match human estimates, at least one of the following had to be true: Better level of detail inthe data, bigger sample size of previous projects, or projects being more similar so that theyshare common features more often.

Forms are used in many situations. For example, they tend to be ubiquitous in communications between individuals and government agencies. Something which could potentially boost transparency and efficiency is accurate estimates of how long it will take for the receiver to process a given completed form. Unfortunately, such estimates are often not available. This thesis examines the problem of using machine learning to predict form processing times, applied to the context of Swedish pay-as-you-earn tax returns. More specifically, it compares a naive baseline model to several random forest models, some based on the more common batch learning principle, and others on online learning which is typically seen as more suitable for working with data streams and changing conditions. Despite the theoretical advantages of online learning, none of the models using that approach were able to consistently outperform the naive baseline model. Conversely, the two primarily evaluated batch learning models were successful in doing so, although the improvement over the baseline was small. / Formulär används i många sammanhang. De är exempelvis mycket vanligt förekommande i kommunikation mellan privatpersoner och myndigheter. Något som potentiellt skulle kunna innebära ökad transparens och effektivitet är träffsäkra uppskattningar av hur lång tid det tar för mottagaren att handlägga ett givet formulär. Dessvärre är sådana uppskattningar ofta inte tillgängliga. Detta examensarbete undersöker hur maskininlärning kan användas för att förutspå formulärhandläggningstider, tillämpat i kontexten svenska arbetsgivardeklarationer. Mer specifikt jämförs en enkel naiv modell mot flera random forest-modeller, vissa baserade på den vanligare batchinlärningsprincipen, och andra på onlineinlärning som brukar ses som mer passande för dataströmmar och föränderliga förhållanden. Trots de teoretiska fördelarna med onlineinlärning lyckades inte någon av modellerna som använde sig av den tekniken konsekvent ge bättre resultat än den naiva grundmodellen. Däremot visade sig de två primärt utvärderade batchinlärningsmodellerna vara framgångsrika i det avseendet, även om skillnaden mot den naiva modellen var liten.

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The optimal estimation theory of the Wiener-Kalman filter is extended to cover the situation in which the number of memory elements in the estimator is restricted. A method, based on the simultaneous diagonalisation of two symmetric positive definite matrices, is given which allows the weighted least square estimation error to be minimised. A control system design method is developed utilising this estimator, and this allows the dynamic controller in the feedback path to have a low order. A 12-order once-through boiler model is constructed and the performance of controllers of various orders generated by the design method is investigated. Little cost penalty is found even for the one-order controller when compared with the optimal Kalman filter system. Whereas in the Kalman filter all information from past observations is stored, the given method results in an estimate of the state variables which is a weighted sum of the selected information held in the storage elements. For the once-through boiler these weighting coefficients are found to be smooth functions of position, their form illustrating the implicit model reduction properties of the design method. Minimal-order estimators of the Luenberger type also generate low order controllers and the relation between the two design methods is examined. It is concluded that the design method developed in this thesis gives better plant estimates than the Luenberger system and, more fundamentally, allows a lower order control system to be constructed. Finally some possible extensions of the theory are indicated. An immediate application is to multivariable control systems, while the existence of a plant state estimate even in control systems of very low order allows a certain adaptive structure to be considered for systems with time-varying parameters.

Les systèmes informatiques critiques - tels que les commandes de vol électroniques et le contrôle des centrales nucléaires - doivent répondre à des exigences strictes en termes de sûreté de fonctionnement. Nous nous intéressons ici à l'application de méthodes formelles - ancrées sur de solides bases mathématiques - pour la vérification du comportement des logiciels critiques. Plus particulièrement, nous spécifions formellement nos algorithmes et nous les prouvons corrects, à l'aide de l'assistant à la preuve Coq - un logiciel qui vérifie mécaniquement la correction des preuves effectuées et qui apporte un degré de confiance très élevé. Nous appliquons ici des méthodes formelles à l'estimation du Temps d'Exécution au Pire Cas (plus connu par son abréviation en anglais, WCET) de programmes C. Le WCET est une propriété importante pour la sûreté de fonctionnement des systèmes critiques, mais son estimation exige des analyses sophistiquées. Pour garantir l'absence d'erreurs lors de ces analyses, nous avons formellement vérifié une méthode d'estimation du WCET fondée sur la combinaison de deux techniques principales: une estimation de bornes de boucles et une estimation du WCET via la méthode IPET (Implicit Path Enumeration Technique). L'estimation de bornes de boucles est elle-même décomposée en trois étapes : un découpage de programmes, une analyse de valeurs opérant par interprétation abstraite, et une méthode de calcul de bornes. Chacune de ces étapes est formellement vérifiée dans un chapitre qui lui est dédiée. Le développement a été intégré au compilateur C formellement vérifié CompCert. Nous prouvons que le résultat de l'estimation est correct et nous évaluons ses performances dans des ensembles de benchmarks de référence dans le domaine. Les contributions de cette thèse incluent la formalisation des techniques utilisées pour estimer le WCET, l'outil d'estimation lui-même (obtenu à partir de la formalisation), et l'évaluation expérimentale des résultats. Nous concluons que le développement fondé sur les méthodes formelles permet d'obtenir des résultats intéressants en termes de précision, mais il exige des précautions particulières pour s'assurer que l'effort de preuve reste maîtrisable. Le développement en parallèle des spécifications et des preuves est essentiel à cette fin. Les travaux futurs incluent la formalisation de modèles de coût matériel, ainsi que le développement d'analyses plus sophistiquées pour augmenter la précision du WCET estimé. / Safety-critical systems - such as electronic flight control systems and nuclear reactor controls - must satisfy strict safety requirements. We are interested here in the application of formal methods - built upon solid mathematical bases - to verify the behavior of safety-critical systems. More specifically, we formally specify our algorithms and then prove them correct using the Coq proof assistant - a program capable of mechanically checking the correctness of our proofs, providing a very high degree of confidence. In this thesis, we apply formal methods to obtain safe Worst-Case Execution Time (WCET) estimations for C programs. The WCET is an important property related to the safety of critical systems, but its estimation requires sophisticated techniques. To guarantee the absence of errors during WCET estimation, we have formally verified a WCET estimation technique based on the combination of two main methods: a loop bound estimation and the WCET estimation via the Implicit Path Enumeration Technique (IPET). The loop bound estimation itself is decomposed in three steps: a program slicing, a value analysis based on abstract interpretation, and a loop bound calculation stage. Each stage has a chapter dedicated to its formal verification. The entire development has been integrated into the formally verified C compiler CompCert. We prove that the final estimation is correct and we evaluate its performances on a set of reference benchmarks. The contributions of this thesis include (a) the formalization of the techniques used to estimate the WCET, (b) the estimation tool itself (obtained from the formalization), and (c) the experimental evaluation. We conclude that our formally verified development obtains interesting results in terms of precision, but it requires special precautions to ensure the proof effort remains manageable. The parallel development of specifications and proofs is essential to this end. Future works include the formalization of hardware cost models, as well as the development of more sophisticated analyses to improve the precision of the estimated WCET.

L’objectif majeur de cette thèse de doctorat est d’étudier la pression temporelle afin de mieux appréhender cette notion si familière et pourtant si peu étudiée. A partir de l’observation de l’omniprésence de cette pression temporelle dans notre société et de l’importance de la perception du temps dans nos activités quotidiennes, nous avons choisi de l’examiner à travers son influence sur l’estimation de durées. Pour ce faire, nous avons mis en place une série d’expérimentations : d’abord dans le cadre de la littérature sur l’estimation du temps, utilisant des tâches temporelle d’estimation verbale et de production de durées ; puis dans le cadre de la littérature sur la mémoire prospective, et plus précisément avec des tâches de mémoire prospective basée sur le temps. Notre hypothèse est que la pression temporelle provoque une modification du temps perçu. Les résultats de nos premières expérimentations réalisées avec des tâches temporelles d’estimation verbale et de production de durées révèlent que soumettre des participants à une condition de pression temporelle provoque une distorsion temporelle comparée à une condition sans pression temporelle. Cette distorsion temporelle va dans le sens d’une surestimation des durées. En revanche, aucun effet de la pression temporelle n’a été relevé dans les expérimentations réalisées avec les tâches de mémoire prospective basée sur le temps, si ce n’est sur les performances à la tâche non temporelle, aussi appelée tâche en cours. / This doctoral thesis aims at investigating time pressure to have a better understanding of this so familiar concept and yet so little studied. On the one hand, time pressure is ubiquitous in our occidental society; on the other hand, time perception is essential in our daily activities. Then, we have consciously opted to study time pressure through its effects on time estimation. To this end, we elaborate series of experiments within two different frameworks; time estimation literature with two different tasks (verbal estimation and time production) and prospective memory literature, more precisely with time-based prospective memory tasks. Our assumption is that time pressure leads to a subjective time distortion. The results of our experiments reveal that time pressure causes a temporal distortion when participants have to estimate or produce a duration: in the condition with time pressure they overestimate durations, compared to a condition without time pressure. In contrast, no time pressure effect is revealed on the temporal component of our time-based prospective memory task; but performance on the ongoing task is affected by time pressure.

Traveler information systems play a significant role in most travelers’ daily trips. These systems assist travelers in choosing the best routes to reach their destinations and possibly select suitable departure times and modes for their trips. Connected Vehicle (CV) technologies are now in the pilot program stage. Vehicle-to-Infrastructure (V2I) communications will be an important source of data for traffic agencies. If this data is processed properly, then agencies will be able to better determine traffic conditions, allowing them to take proper countermeasures to remedy transportation system problems under different conditions. This research focuses on developing methods to assess the potential of utilizing CV data to support the traveler information system data collection process. The results from the assessment can be used to establish a timeline indicating when an agency can stop investing, at least partially, in traditional technologies, and instead rely on CV technologies for traveler information system support. This research utilizes real-world vehicle trajectory data collected under the Next Generation Simulation (NGSIM) program and simulation modeling to emulate the use of connected vehicle data to support the traveler information system. NGSIM datasets collected from an arterial segment and a freeway segment are used in this research. Microscopic simulation modeling is also used to generate required trajectory data, allowing further analysis, which is not possible using NGSIM data. The first step is to predict the market penetration of connected vehicles in future years. This estimated market penetration is then used for the evaluation of the effectiveness of CV-based data for travel time and volume estimation, which are two important inputs for the traveler information system. The travel times are estimated at different market penetrations of CV. The quality of the estimation is assessed by investigating the accuracy and reliability with different CV deployment scenarios. The quality of volume estimates is also assessed using the same data with different future scenarios of CV deployment and partial or no detector data. Such assessment supports the identification of a timeline indicating when CV data can be used to support the traveler information system.