It is a known fact that construction projects do not often complete on time due to several reasons related with the unexpected changes in the project conditions, external factors or performance of project participants. Since construction projects are unique and limited information is available at the beginning of the projects, prediction of delays is a difficult task. However, if the delays can be assessed at the early stages, their impacts might be minimized, some of the delays can even be eliminated. This study introduces a delay assessment methodology which can be used to predict delays both at the activity level and project level. Fuzzy logic and fuzzy network analysis form the basis of this methodology. A software has been developed using the proposed delay assessment methodology and a delay taxonomy developed by Bilgin (2011). Project management teams can use the developed tool to predict delays and also evaluate impacts of delays on a project`s schedule.

Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive Control (MPC) algorithm in designing the controller. By simulating an automotive Cruise Control system and a DC Motor plant in a CAN environment, it goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed method on an 8-bit 16MHz ATmega328P microcontroller and measures the execution time overhead. The results clearly indicate that the method is computationally feasible for online usage. / Master of Science

This thesis conducts a study on a data set from the Swedish and Danish postal service Postnord, comparing an artificial neural network (ANN) and a gradient boosting machine (GBM) for predicting delays in package deliveries. The models are evaluated based on F1-score for the important class which represents the data points that are delayed and needed to be identified. The GBM is already implemented and tuned using grid search by Postnord, the ANN is tuned using sequential model based optimization with the tree Parzen estimator function. Furthermore, it is trained using dynamic resampling to handle the imbalanced data set. Even with several measures implemented to handle the class imbalance, the ANN performs poorly when tested on unseen data, unlike the GBM. The GBM has high precision (84%) and decent recall (24%), which produces a F1-score of 0.38. The ANN has high recall (62%) but extremely low precision (5%) which gives a F1-score of 0.08, indicating that it is biased to predict sample as delayed when it is in time. The GBM has a natural handling of class imbalance unlike the ANN, and even with measures taken to improve the ANN and its handling of class imbalance, GBM performs better.

The timetable is an essential part for the operations of railway traffic, and its quality is considered to have large impact on capacity utilization and reliability of the transport mode. The process of generating a timetable is most often a manual task with limited computer aid, and is known to be a complex planning problem due to inter-train dependencies. These inter-train dependencies makes it hard to manually generate feasible timetables, and also makes it hard to improve a given timetable as new conflicts and surprising effects easily can occur. As the demand for railway traffic is expected to continue grow, higher frequencies and more saturated timetables are required. However, in many European countries there is also an on-going public debate on the punctuality of the railway, which may worsen by increased capacity utilization. It is therefore also a need to increase the robustness of the services. This calls for increased precision of both the planning and the operation, which can be achieved with a higher degree of automation. The research in this thesis is aimed at improving the robustness of railway timetables by combining micro-simulation with mathematical optimization, two methods that today are used frequently by practitioners and researchers but rarely in combination. In this research a sequential approach based on simulating a given timetable and re-optimizing it to reduce the weighted sum of scheduled travel time and predicted average delay is proposed. The approach has generated promising results in simulation studies, in which it has been possible to substantially improve the punctuality and reduce the average delays by only increasing the advertised travel times slightly. Further, the results have also indicated a positive socio-economic benefit. This demonstrates the methods potential usefulness and motivates further research. / För järnvägen har tidtabellen en central roll, och dess kvalité har stor betydelse för kapacitet och tillförlitlighet. Processen att konstruera en tidtabell är ofta en uppgift som utförs manuellt med begränsat datorstöd och på grund av beroenden mellan enskilda tåg är det ofta ett tidskrävande och svårt arbete. Dessa tågberoenden gör det svårt att manuellt konstruera konfliktfria tidtabeller samtidigt som det också är svårt att manuellt förbättra en given tidtabell, vilket beror på att de är svårt att förutsäga vad effekten av en given ändring blir. Eftersom efterfrågan på järnväg fortsatt förväntas öka, finns det ett behov av att kunna köra fler tåg. Samtidigt pågår det redan i många europeiska länder en offentlig debatt om järnvägen punktlighet, vilken riskeras att försämras vid högre kapacitetsanvändning. Därför finns det även ett behov av att förbättra tidtabellernas robusthet, där robusthet syftar till en tidtabells möjlighet att stå emot och återhämta mindre förseningar. För att hantera denna målkonflikt kommer det behövas ökad precision vid både planering och drift, vilket kan uppnås med en högre grad av automation. Forskningen i denna avhandling syftar till att förbättra robustheten för tågtidtabeller genom att kombinera mikro-simulering med matematisk optimering, två metoder som redan används i hög grad av både yrkesverksamma trafikplanerare och forskare men som sällan kombineras. I den här avhandlingen förslås en sekventiell metod baserad på att simulera en given tidtabell och optimera den för att minska den viktade summan av planerad restid och predikterad medelförsening. Metoden har visat på lovande resultat i simuleringsstudier, där det har varit möjligt att uppnå en väsentligt bättre punktlighet och minskad medelförsening, genom att endast förlänga de planerade restiderna marginellt. Även förbättrad samhällsekonomisk nytta har observerats av att tillämpa den föreslagna metoden. Sammantaget visar detta metodens potentiella nytta och motiverar även fortsatt forskning. / <p>QC 20191112</p>

A large part of people's lives are spent in indoor spaces such as office and university buildings, shopping malls, subway stations, airports, museums, community centers, etc. Such kind of spaces can be very large and paths inside the locations can be constrained and complex. Deployment of indoor tracking technologies like RFID, Bluetooth, and Wi-Fi can track people and object movements from one symbolic location to another within the indoor spaces. The resulting tracking data can be massive in volume. Analyzing these large volumes of tracking data can reveal interesting patterns that can provide opportunities for different types of location-based services, security, indoor navigation, identifying problems in the system, and finally service improvements. In addition to the huge volume, the structure of the unprocessed raw tracking data is complex in nature and not directly suitable for further efficient analysis. It is essential to develop efficient data management techniques and perform different kinds of analysis to make the data beneficial to the end user. The Ph.D. study is sponsored by the BagTrack Project (http://daisy.aau.dk/bagtrack). The main technological objective of this project is to build a global IT solution to significantly improve the worldwide aviation baggage handling quality. The Ph.D. study focuses on developing data management techniques for efficient and effective analysis of RFID-based symbolic indoor tracking data, especially for the baggage tracking scenario. First, the thesis describes a carefully designed a data warehouse solution with a relational schema sitting underneath a multidimensional data cube, that can handle the many complexities in the massive non-traditional RFID baggage tracking data. The thesis presents the ETL flow that loads the data warehouse with the appropriate tracking data from the data sources. Second, the thesis presents a methodology for mining risk factors in RFID baggage tracking data. The aim is to find the factors and interesting patterns that are responsible for baggage mishandling. Third, the thesis presents an online risk prediction technique for indoor moving objects. The target is to develop a risk prediction system that can predict the risk of an object in real-time during its operation so that the object can be saved from being mishandled. Fourth, the thesis presents two graph-based models for constrained and semi-constrained indoor movements, respectively. These models are used for mapping the tracking records into mapping records that represent the entry and exit times of an object at a symbolic location. The mapping records are then used for finding dense locations. Fifth, the thesis presents an efficient indexing technique, called the $DLT$-Index, for efficiently processing dense location queries as well as point and interval queries. The outcome of the thesis can contribute to the aviation industry for efficiently processing different analytical queries, finding problems in baggage management systems, and improving baggage handling quality. The developed data management techniques also contribute to the spatio-temporal data management and data mining field. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

The European Commission has foreseen a modal share of 30% by 2030 for rail freight transport. To achieve this increase in the modal share, enhanced reliability of rail freight services is required. Optimal functioning of shunting yards is one of the areas that can improve this reliability. Shunting yards are large areas allocated to reassemble freight trains for dispatching to new destinations. Their productivity has a direct impact on the overall performance of a rail freight network. Therefore, analysing and modelling of departure deviations from shunting yards are required to enhance the interactions between shunting yards and the network; this thesis contributes to this gap. Paper I investigates the probability and temporal distribution of departure deviations using a large data set comprising 250,000 departures over seven years from two main shunting yards (Malmö and Hallsberg) in Sweden. The probability distribution of departure deviations is found comparing four main distributions including the exponential, the log-normal, the gamma, and the Weibull according to the maximum likelihood estimates and the results of the Anderson-Darling goodness of fit test.  The log-normal and the gamma are shown the best fits for departure deviations: the former on delays, and the latter on early departures. In the temporal delay distribution, the weekly and monthly, but not yearly delayed departures are positively correlated with the network usage. However, for hourly delayed departures, a shunting yard involved with international traffic does not show any correlation between delayed departures and the network usage, whereas a domestic shunting yard shows a significant negative correlation between these two parameters.  The findings obtained from this thesis contribute to a better understanding of departure deviations from shunting yards, and can be applied in enhancing the operations and capacity utilization of shunting yards in future models. Papers II and III analyse the relationship between congestion in the arrival yard and departure delays using the same data set as paper I.  According to previous research, congestion plays an important role in shunting yard delays. With defining congestion as the number of arriving trains before departure time, paper II analyses this relationship limiting the arrivals and departures between the two shunting yards considering varying time periods before departure,whereas Paper III elaborates the analysis by defining congestion level in a fixed period of time before departure time including all arrivals and departures. Considering the data set used in the analysis, the results show that there is no significant relationship between the congestion in the arrival yard and departure delays of trains. It is possible that congestion may not impact the departure delays of trains, but it may impact the departure delays of wagons due to missed wagon connection or increasing wagon idle time, which can be explored with the availability of wagon connection data.  Additionally, future elaboration of congestion definition, covering congestion at the shunting yard level, may lead to further improved analyses. / Europeiska kommissionen har förutspått en markansandel på 30% framtill 2030 för järnvägstransporter av gods. För att uppnå denna ökning krävsökad tillförlitlighet hos järnvägstransporttjänster. Rangergodsbangårdars optimalafunktion är ett av de områden som kan förbättra denna tillförlitlighet.Rangergodsbangårdar stora områden som är avsedda för att koppla ihopgodståg för sändning till nya destinationer. Deras produktivitet har en direktinverkan på järnvägsnätets totala prestanda. Därför krävs analys och modelleringav avvikelser från dessa noder för att förbättra interaktionen mellanrangergodsbangårdar och järnvägsnätet. I papper I undersöks sannolikheten och den tidsmässiga fördelningen avavvikelser med hjälp av en stor datamängd som omfattar 250 000 avgångaröver sju år från två rangergodsbangårdar (Malmö och Hallsberg) i Sverige.Sannolikhetsdistributioner av avvikelser jämförs med fyra huvuddistributioner,exponentiell, log-normal, gamma och Weibull enligt de maximalasannolikhetsuppskattningarna och resultaten av Anderson-Darling godhetav passningstest. Log-normal och gamma visar sig passa bäst för avvikelser:den förstnämnda vid förseningar och den senare vid tidiga avgångar. I dentidsmässiga fördröjningsfördelningen är de veckovisa och månatliga men inteårliga försenade avgångarna positivt korrelerade med järnvägsnätets nyttjandegrad.För försenade avgångar per timme visar dock en rangergodsbangårdsom är inblandad i internationell trafik ingen korrelation mellan försenadeavgångar och järnvägsnätets nyttjandegrad, medan en inhemsk rangergodsbangårdvisaren signifikant negativ korrelation mellan dessa två parametrar.Resultaten från denna avhandling bidrar till en bättre förståelse av avvikelserfrån rangergodsbangårdar och kan användas för att förbättra drift och kapacitetsutnyttjandeav rangergodsbangårdar växelplatser i framtida modeller. Papper II och III analyserar förhållandet mellan trängsel i ankomstgårdenoch avgångsförseningar med hjälp av samma datamängd som i papperI. Enligt tidigare analyser spelar trängsel en viktig roll vid förseningar förrangergodsbangårdar. Trängsel definieras som antalet ankommande tåg föreavgångstid och papper II analyserar detta förhållande som begränsar ankomsteroch avgångar mellan de två rangergodsbangårdar med beaktande av olikatidsperioder före avgång, medan papper III utvecklar analysen genom attdefiniera trängselnivån under en fast tidsperiod före avgångstid inklusive allaankomster och avgångar. Med tanke på datamängden som användes i analysenvisar resultaten att det inte finns något signifikant samband mellan trängselni ankomstgården och tågens förseningar. Det är möjligt att trängsel kanskeinte påverkar tågens avgångsfördröjningar, men det kan påverka vagnarnasavgångsfördröjningar på grund av missad vagnanslutning eller öka vagnenstomgångstid, vilket kan undersökas med vid tillgång av vagnanslutningsdata.Dessutom kan framtida vidareutveckling av definitionen av trängsel som påen detaljerad nivå täcker rangergodsbangårdars alla delar, leda till ytterligareförbättrade analyser. / <p>QC 20201105</p> / Shift2Rail / FR8HUB

This thesis deals with delay prediction issue between nodes on the Internet. Accurate delay prediction helps with choosing of the nearest internet neighbor and contributes to effective usage of network sources. Unnecessary network load is decreased due to algorithms of delay prediction (no need for many latency measuring). The thesis focuses theoretically on the three main algorithms using coordinate systems - GNP, Vivaldi, Lighthouses. Last one is at the same time the main subject of the thesis too. Algorithm Lighthouses is explored in detail theoretically and in practise too. In order to verify the accurate of delay prediction of Lighthouses algorithm the simulation application was developed. The application is able to compute node coordinates of synthetic network using Lighthouses algorithm. Description of simulation application and evaluation of simalution results are part of practice part of this thesis.