In globalizing world, increase in demand for high speed rail travel requires comfortable ride over bridges while maintaining an economical design. These bridges either have composite steel I-girders, prestressed precast I or box girder superstructures. The span lengths can reach up to 40 meters. If frequency of wheel load pass at a point on bridge matches with one the critical frequencies of the structure, excessive vibration can developed both at the train and the bridge even if the structure is structurally safe. Excessive vibration can discomfort the passengers. Focus of this study is given to identify certain thresholds for the rigidity of span to minimize the passenger discomfort at short-span composite steel I-girder high speed railroad bridges. In this context, various span lengths with different girder configurations have been analyzed under various train design speeds and ballast stiffness. Eigenvalue analyses are performed to determine critical frequencies of bridges. Moving force models are used to determine structural vibrations as recommended by high speed railroad bridge design specifications. It is well-known that stiffer structures can have significantly less vibration amplitudes than lighter ones providing a comfortable ride for high speed train passes.

When booking a trip along the railway through several train operators it is not uncommon that information about possible disruptions along the railway (that can change or cancel the booked trip) are not relayed to the passengers. Today, research on rail traffic in Sweden is limited. It is unclear how satisfied customers are with the quality of the information they receive during their trip (if they get it at all), including with respect to disruptions. Our partners have identified what they believe is a need among train operators, which is a service for disruption information to travelers. In addition to confirming that there is a need for such a service, we have an interest to investigate how such a service might look like and what the users want. Our research has shown that passengers are not satisfied with either the amount of information about disturbances or how often they get it. Along with KnowitBorlänge, we have come up with a proposed solution that uses already existing technologies to create a portal for an efficient way to get the interference information to travelers. / Vid bokandet av en resa längs med järnvägen via flera tågbolag så finns (i dagens läge) inte alltid information kring eventuella störningar längs med järnvägen som kan förändra eller ställa in den bokade resan. I dag är forskning kring bantrafiken i Sverige begränsad. Det är oklart hur nöjda resenärer är med kvalitén på den information de får kring sin resa (om de får den överhuvudtaget), bland annat med avseende på störningar. Vår samarbetspartner har identifierat vad de tror är ett behov hos tågbolag, vilket är en tjänst för information om tågtrafiken för resenärer. Utöver att bekräfta att det här behovet finns har vi även ett intresse att undersöka hur en sådan här tjänst skulle kunna se ut och vad användarna vill ha. Vår forskning har visat att resenärerna inte är nöjda med varken mängden information om störningar eller hur ofta de får den. Tillsammans med Knowit Borlänge har vi kommit fram till ett lösningsförslag som använder befintliga tekniker för att skapa en portal som på ett effektivt sätt levererar störningsinformation till resenärer.

In my diploma thesis I deal with the revitalization of the former depot of railway vehicles in Ceska Trebova and possible secondary utilization of railway wagons. From a space that has been inaccessible and neglected to the public for many years, I propose to create an open forest park with a railroad museum.

Gröna Tåget

Simuleringsverktyget Emergo Train System® (ETS) har tidigare använts främst inom sjukvården för träning inom katastrofmedicin, räddningstjänsten däremot saknar motsvarande träningsverktyg. Syftet med denna studie var därför att undersöka hur ETS kan tillämpas i räddningstjänstens utbildnings- och träningsverksamhet. Fyra fokusgrupper bestående av personal från räddningstjänsten fick köra ett brandscenario och utifrån den upplevelsen besvara och diskutera frågor om möjligheten att använda ETS inom räddningstjänsten. Rekommendationer sammanställdes efter varje fokusgrupp och implementerades iterativt i nästkommande fokusgrupp. Studiens resultat visade bland annat att deltagarna vill använda ETS för att träna på ett flertal olyckor och situationer, men att verktyget behöver fler domänspecifika redskap och figurer. En central insikt var att whiteboardtavlornas placering påverkar deltagarnas aktivitet. Det finns även ett behov av att flera olika roller inom räddningstjänstens verksamhet ska kunna använda ETS, exempelvis brandmän och chef i beredskap. Studien fann även att räddningstjänstens “lag om skydd mot olyckor” bör ligga till grund för hur ett utfall ska utformas. Framtida studier bör undersöka detta i djupare detalj samt uppsöka specialister inom olika olycksområden för att ta fram rutinåtgärder, roller och redskapstillämpningar. I sin helhet visar studien på att ETS har stor potential att bli ett tränings- och utbildningsverktyg inom räddningstjänstens verksamhet. / The simulation tool Emergo Train System® (ETS) has in the past been primarily used for training emergency medicine operators, however the fire and rescue services lack a similar training tool for their operators. As a part of the ongoing work of adapting ETS the current study investigated how ETS can be adapted to the fire and rescue service's training activities. Four focus groups of fire and rescue service personnel played a house fire scenario in ETS and, based on that experience, answered and discussed questions regarding the possibilities of using ETS as a training tool in their domain. The recommendations of each focus group were iteratively implemented in the next focus group session. Results showed that the participants want to use ETS to train on different accidents and situations, but additional domain-specific tools and figures must be developed. Importantly, the positioning of the whiteboards affects the participants' activity. ETS should be adapted for use for different personnel within the fire and rescue services, such as firefighters and response managers. The study also found that the fire and rescue services "law for protection against accidents" should form the basis for developing measurable outcomes. Future studies should investigate this in greater detail, and also incorporate various accident specialists from various areas to develop routine measures, roles and implements. Overall, the study shows that ETS has great potential to become a training tool within the fire and rescue services.

The application of Model Reference Adaptive Control (MRAC) for train dynamic braking is investigated in order to control dynamic braking forces while remaining within the allowable adhesion and coupler forces.  This control method can accurately determine the train braking distance.  One of the critical factors in Positive Train Control (PTC) is accurately estimating train braking distance under different operating conditions.  Accurate estimation of the braking distance will allow trains to be spaced closer together, with reasonable confidence that they will stop without causing a collision.  This study develops a dynamic model of a train consist based on a multibody formulation of railcars, trucks (bogies), and suspensions.   The study includes the derivation of the mathematical model and the results of a numerical study in Matlab.  A three-railcar model is used for performing a parametric study to evaluate how various elements will affect the train stopping distance from an initial speed.  Parameters that can be varied in the model include initial train speed, railcar weight, wheel-rail interface condition, and dynamic braking force.  Other parameters included in the model are aerodynamic drag forces and air brake forces. An MRAC system is developed to control the amount of current through traction motors under various wheel/rail adhesion conditions while braking.  Minimizing the braking distance of a train requires the dynamic braking forces to be maximized within the available wheel/rail adhesion.  Excessively large dynamic braking can cause wheel lockup that can damage the wheels and rail.  Excessive braking forces can also cause large buff loads at the couplers.  For DC traction motors, an MRAC system is used to control the current supplied to the traction motors.  This motor current is directly proportional to the dynamic braking force.  In addition, the MRAC system is also used to control the train speed by controlling the synchronous speed of the AC traction motors.  The goal of both control systems for DC and AC traction motors is to apply maximum available dynamic braking while avoiding wheel lockup and high coupler forces.  The results of the study indicate that the MRAC system significantly improves braking distance while maintaining better wheel/rail adhesion and coupler dynamics during braking.  Furthermore, according to this study, the braking distance can be accurately estimated when MRAC is used.  The robustness of the MRAC system with respect to different parameters is investigated, and the results show an acceptable robust response behavior. / Ph. D.

Demand for transportation continues to increase, for both freight and passenger services. One of the most energy-efficient modes of transportation is rail. One solution to increase the attractiveness of rail transport is to introduce automatic train operation (ATO) with a high grade of automation (GoA). Driverless and unattended train operation could entail positive effects but would also bring challenges when removing the train driver. Thus, there is a need to understand the role of train drivers, especially in unplanned events. The main research objectiveis to understand the train driver roles during unplanned events and the frequency of such events. This thesis includes three papers to fulfill the research objective. This thesis studied delay logs and trackside sensor logs. A qualitative method, thematic analysis, was used to identify themes of the roles performed by train driver from the delay logs. The chi-square test statistical method was used to analyze these trackside sensor logs. Six main categories of tasks for train drivers were identified for unplanned events. Detect, Report, Inspect, Adjust, Manage passengers, and Respond to train orders. Each category was analyzed for each grade of automation by giving the responsibility for each category. The results highlight in a novel way the varied challenges between grade of automation in mainline systems. Detecting abnormalities was the most common task train drivers performed during unplanned events. Train drivers use four human senses to detect abnormalities: sight, hearing, touch, and smell. This indicates the need for onboard sensors. However, the real challenge is in processing all sensor data to gain anaccurate evaluation of any fault. One specific type of unplanned event in which the train driver is needed involves trackside sensor alarms. Freight trains are ten times more likely to trip an alarm than passenger trains. Alarms are more frequent in colder climate zones during winter months. These differences are statistically significant and indicate that not all lines and train types might be suitable for a high grade of automation. If driverless or unattended train operation will become a reality in future, many challenges must be met. This thesis gives deeper understanding of these challenges using a novel way to identify and quantify train driver tasks during unplanned events. / Efterfrågan på transporter fortsätter att öka, både gods- och persontransporter. Ett av de mest energieffektiva transportmedlen är järnväg. En möjlighet att öka järnvägens attraktivitet skulle kunna vara att introducera automatic train operation (ATO) med en hög grad av automatisering. Förarlös och obemannad tågdrift skulle kunna medföra postiva effekter, men det skulle också medföra utmaningar med att ta bort lokföraren. Det finns därför ett behov att förstå lokförarens roll, speciellt i oplanerade situationer. Huvudsyftet är att förstå de olika rollerna lokföraren har vid oplanerade situationer och även frekvensen av dessa situationer. Licentiatuppsatsen är uppbyggd av tre vetenskapliga artiklar för uppnå syftet. Den här licentiatuppsatsen har använt förseningsbeskrivningar och detektorloggar. En kvalitativ metod, tematisk analys, har använts för att identifiera teman för lokförarnas olika roller utifrån förseningsbeskrivningarna. En statistisk metod, chi-square-test, har använts för att analysera detektorloggarna.  Sex huvudkategorier av lokförarens roller vid oplanerade händelser har identifierats: Upptäcka, Rapportera, Kontrollera, Justera, Hantera resenärer och Hantera tågordrar. Varje kategori har analyserats utifrån de olika graderna av automation genom att ge visa hur de skulle kunna genomföras. Resultaten belyser de olika utmaningarna mellan graderna av automation på ett nytt sätt i ett nationellt järnvägssystem. Att upptäcka felaktigheter var den vanligaste uppgiften för lokförare vid oplanerade händelser. Lokförare använder fyra sinnen för att upptäcka felaktigheter, syn, hörsel, känsel och lukt. Det indikerar behovet av ombordsensorer, men den stora utmaningen blir att hantera all sensordata för en korrekt bedömning av verkliga fel. En specifik oplanerad händelse då lokföraren behövs är vid detektorlarm. Godståg har en tio gånger högre risk att utlösa ett detektorlarm än ett persontåg. Detektorlarm förekommer oftare i kallt klimat under vintermånader. Skillanderna är statistiskt säkerställda och ger en indikation på att alla sträckor och tågtyper inte är lämpliga för en hög grad av automatisering.  Om förarlösa eller obemannade tåg ska bli en verklighet i framtiden behöver flera utmaningar hanteras. Den här licentiatuppsatsen ger en djupare förståelse av dessa utmaningar genom att använda ett nytt sätt att identifiera lokförarnas uppgifter vid oplanerade händelser. / <p>QC230830</p>

Рост перевозок с помощью автомобильных поездов существенным образом сдерживается недостаточной их проходимостью и невозможностью широко использовать в районах с малой сетью усовершенствованных дорог. Существует большое количество способов для повышения эффективности использования автопоездов в тяжёлых дорожных условиях. Однако, наиболее эффективным методом является передача крутящего момента тягача колесам прицепа, которые в этом случае становятся ведущими. Автором были рассмотрены вопросы повышения проходимости автопоездов, а также был спроектирован механический привод на полуприцеп. / The growth of transportation by road trains is significantly constrained by their low off-road opportunities and enables to use in areas with a small network of improved roads. There are a lot of ways to improve efficient using road trains in difficult road conditions. However, the most efficient way is transfer torque from truck to trailer wheels which will active in this situation. Author considered the issues of improve off-road opportunities road trains and also constructed a mechanical active drive of the trailer wheels.

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its component motor unit potential trains (MUPTs). The extracted MUPTs can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid trains. Before using decomposition results and the motor unit potential (MUP) shape and motor unit (MU) firing pattern information related to each active MU for either clinical or research purposes the fact that the extracted MUPTs are valid needs to be confirmed. The existing MUPT validation methods are either time consuming or related to operator experience and skill. More importantly, they cannot be executed during automatic decomposition of EMG signals to assist with improving decomposition results. To overcome these issues, in this thesis the possibility of developing automatic MUPT validation algorithms has been explored. Several methods based on a combination of feature extraction techniques, cluster validation methods, supervised classification algorithms, and multiple classifier fusion techniques were developed. The developed methods, in general, use either the MU firing pattern or MUP-shape consistency of a MUPT, or both, to estimate its overall validity. The performance of the developed systems was evaluated using a variety of MUPTs obtained from the decomposition of several simulated and real intramuscular EMG signals. Based on the results achieved, the methods that use only shape or only firing pattern information had higher generalization error than the systems that use both types of information. For the classifiers that use MU firing pattern information of a MUPT to determine its validity, the accuracy for invalid trains decreases as the number of missed-classification errors in trains increases. Likewise, for the methods that use MUP-shape information of a MUPT to determine its validity, the classification accuracy for invalid trains decreases as the within-train similarity of the invalid trains increase. Of the systems that use both shape and firing pattern information, those that separately estimate MU firing pattern validity and MUP-shape validity and then estimate the overall validity of a train by fusing these two indices using trainable fusion methods performed better than the single classifier scheme that estimates MUPT validity using a single classifier, especially for the real data used. Overall, the multi-classifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance with overall accuracy of 99.4% and 98.8% for simulated and real data, respectively. The possibility of formulating an algorithm for automated editing MUPTs contaminated with a high number of false-classification errors (FCEs) during decomposition was also investigated. Ultimately, a robust method was developed for this purpose. Using a supervised classifier and MU firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by a high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and MUP shape information to detect MUPs that were erroneously assigned to the train. Evaluation based on simulated and real MU firing patterns, shows that contaminated MUPTs could be detected with 84% and 81% accuracy for simulated and real data, respectively. For a given contaminated MUPT, the algorithm on average correctly classified around 92.1% of the MUPs of the MUPT. The effectiveness of using the developed MUPT validation systems and the MUPT editing methods during EMG signal decomposition was investigated by integrating these algorithms into a certainty-based EMG signal decomposition algorithm. Overall, the decomposition accuracy for 32 simulated and 30 real EMG signals was improved by 7.5% (from 86.7% to 94.2%) and 3.4% (from 95.7% to 99.1%), respectively. A significant improvement was also achieved in correctly estimating the number of MUPTs represented in a set of detected MUPs. The simulated and real EMG signals used were comprised of 3–11 and 3–15 MUPTs, respectively.

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its component motor unit potential trains (MUPTs). The extracted MUPTs can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid trains. Before using decomposition results and the motor unit potential (MUP) shape and motor unit (MU) firing pattern information related to each active MU for either clinical or research purposes the fact that the extracted MUPTs are valid needs to be confirmed. The existing MUPT validation methods are either time consuming or related to operator experience and skill. More importantly, they cannot be executed during automatic decomposition of EMG signals to assist with improving decomposition results. To overcome these issues, in this thesis the possibility of developing automatic MUPT validation algorithms has been explored. Several methods based on a combination of feature extraction techniques, cluster validation methods, supervised classification algorithms, and multiple classifier fusion techniques were developed. The developed methods, in general, use either the MU firing pattern or MUP-shape consistency of a MUPT, or both, to estimate its overall validity. The performance of the developed systems was evaluated using a variety of MUPTs obtained from the decomposition of several simulated and real intramuscular EMG signals. Based on the results achieved, the methods that use only shape or only firing pattern information had higher generalization error than the systems that use both types of information. For the classifiers that use MU firing pattern information of a MUPT to determine its validity, the accuracy for invalid trains decreases as the number of missed-classification errors in trains increases. Likewise, for the methods that use MUP-shape information of a MUPT to determine its validity, the classification accuracy for invalid trains decreases as the within-train similarity of the invalid trains increase. Of the systems that use both shape and firing pattern information, those that separately estimate MU firing pattern validity and MUP-shape validity and then estimate the overall validity of a train by fusing these two indices using trainable fusion methods performed better than the single classifier scheme that estimates MUPT validity using a single classifier, especially for the real data used. Overall, the multi-classifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance with overall accuracy of 99.4% and 98.8% for simulated and real data, respectively. The possibility of formulating an algorithm for automated editing MUPTs contaminated with a high number of false-classification errors (FCEs) during decomposition was also investigated. Ultimately, a robust method was developed for this purpose. Using a supervised classifier and MU firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by a high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and MUP shape information to detect MUPs that were erroneously assigned to the train. Evaluation based on simulated and real MU firing patterns, shows that contaminated MUPTs could be detected with 84% and 81% accuracy for simulated and real data, respectively. For a given contaminated MUPT, the algorithm on average correctly classified around 92.1% of the MUPs of the MUPT. The effectiveness of using the developed MUPT validation systems and the MUPT editing methods during EMG signal decomposition was investigated by integrating these algorithms into a certainty-based EMG signal decomposition algorithm. Overall, the decomposition accuracy for 32 simulated and 30 real EMG signals was improved by 7.5% (from 86.7% to 94.2%) and 3.4% (from 95.7% to 99.1%), respectively. A significant improvement was also achieved in correctly estimating the number of MUPTs represented in a set of detected MUPs. The simulated and real EMG signals used were comprised of 3–11 and 3–15 MUPTs, respectively.