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.

Patients admitted to intermediate care units require frequent monitoring by hospital personnel. An automatisation of this monitoring would save a considerable amount of resources and could also improve the quality of the treatment. In this thesis, a deep learning-based video action recognition model is through different transfer learning approaches trained to distinguish between behaviours of patients in TV-series and a prediction system which collects, processes and predicts on images in real-time is proposed. The results from the model-training suggest that it is possible to detect behaviours that need human intervention but training on a large-scale, real-life dataset is required to form a solid conclusion. The performance results of the prediction system show that live-streamed predictions are possible at frame rates sufficient for capturing sought events, without GPU acceleration. / Patienter inlagda på intermediärvårdsavdelningar behöver frekvent övervakning av sjukhuspersonal. En automatisering av denna övervakning skulle spara en betydande mängd resurser och även kunna förbättra kvaliteten av behandlingen. I detta examensarbete tränas en djupinlärningsbaserad modell för videohandlingsigenkänning att, genom olika överföringsinlärningsmetoder, skilja på beteenden mellan olika patienter i TV-serier och ett prediktionssystem som insamlar, processerar och predikterar på bilder i realtid presenteras. Resultaten från modellträningen tyder på att det är möjligt att detektera beteenden som kräver mänsklig interaktion men träning på ett storskaligt, realistiskt dataset krävs för att kunna dra en säker slutsats. Prestandaresultaten från prediktionssystemet visar att live-strömmade prediktioner är möjliga vid bilduppdateringsfrekvenser tillräckliga för att fånga de sökta händelserna, utan GPU-acceleration.

Industrial Control Systems (ICSes), networked through conventional IT infrastructures, are vulnerable to attacks originating from network channels. Perimeter security techniques such as access control and firewalls have had limited success in mitigating such attacks due to the frequent updates required by standard computing platforms, third-party hardware and embedded process controllers. The high level of human-machine interaction also aids in circumventing perimeter defenses, making an ICS susceptible to attacks such as reprogramming of embedded controllers. The Stuxnet and Aurora attacks have demonstrated the vulnerabilities of ICS security and proved that these systems can be stealthily compromised. We present several run-time methods for preemptive intrusion detection in industrial control systems to enhance ICS security against reconfiguration and network attacks. A run-time prediction using a linear model of the physical plant and a neural-network based classifier trigger mechanism are proposed for preemptive detection of an attack. A standalone, safety preserving, optimal backup controller is implemented to ensure plant safety in case of an attack. The intrusion detection mechanism and the backup controller are instantiated in configurable hardware, making them invisible to operating software and ensuring their integrity in the presence of malicious software. Hardware implementation of our approach on an inverted pendulum system illustrates the performance of both techniques in the presence of reconfiguration and network attacks. / Master of Science

With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is one of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and prediction of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of these stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. ?? Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. ?? A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. ?? Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector data is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results.

Over the past decade, the popularity of regional bus services has grown in large North American cities owing to more people living in suburban areas and commuting to the Central Business District to work every day. Estimating journey time for regional buses is challenging because of the low frequencies and long commuting distances that typically characterize such services. This research project developed a mathematical model to estimate regional bus travel time using artificial neural networks (ANN). ANN outperformed other forecasting methods, namely historical average and linear regression, by an average of 35 and 26 seconds respectively. The ANN results showed, however, overestimation by 40% to 60%, which can lead to travellers missing the bus. An operational strategy is integrated into the model to minimize stakeholders’ costs when the model’s forecast time is later than the scheduled bus departure time. This operational strategy should be varied as the commuting distance decreases.

Over the past decade, the popularity of regional bus services has grown in large North American cities owing to more people living in suburban areas and commuting to the Central Business District to work every day. Estimating journey time for regional buses is challenging because of the low frequencies and long commuting distances that typically characterize such services. This research project developed a mathematical model to estimate regional bus travel time using artificial neural networks (ANN). ANN outperformed other forecasting methods, namely historical average and linear regression, by an average of 35 and 26 seconds respectively. The ANN results showed, however, overestimation by 40% to 60%, which can lead to travellers missing the bus. An operational strategy is integrated into the model to minimize stakeholders’ costs when the model’s forecast time is later than the scheduled bus departure time. This operational strategy should be varied as the commuting distance decreases.

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In dieser Arbeit wird ein Verfahren zur Prognose von Schaltzeiten verkehrsadaptiver Lichtsignalanlagen vorgestellt. Der vorgestellte Algorithmus ist dabei sowohl hersteller- als auch schnittstellenunabhängig und somit universell einsetzbar. Der Algorithmus besteht aus einer Langzeit- und einer Kurzfristprognose und setzt das Verfahren zur Berechnung von Entscheidungsbäumen effizient um. Die Verifikation der vorgestellten Schaltzeitprognose wird an über 200 Lichtsignalanlagen in Dresden durchgeführt. Es wird gezeigt das eine gute Prognose von Signalabläufen für das automatisierte Fahren grundsätzlich möglich ist, wobei die Variabilität des Steuerverfahrens und der betrachtete Prognosehorizont maßgeblich die erzielbare Prognosequalität beeinflussen.:1 Einleitung 1.1 Motivation 1.2 Stand der Technik 1.3 Zielsetzung 1.4 Aufbau der Arbeit 2 Grundlagen 2.1 Aufbau von Lichtsignalanlagen 2.2 Steuerungsarten von Lichtsignalanlagen 2.3 Betrieb von Lichtsignalanlagen 2.4 Fahrerassistenzsysteme an signalisierten Knotenpunkten 2.5 Fazit 3 Analyse verkehrsadaptiver Lichtsignalanlagen 3.1 Möglichkeiten der Erhebung von Prozessdaten 3.2 Methodik der Datenauswertung 3.3 Statistische Kenngrößen 3.4 Anforderung an die Prädiktion 3.5 Annäherungsstrategie an signalisierten Knotenpunkten 3.6 Fazit 4 Neue Verfahren zur Prädiktion von Signalzeiten 4.1 Systemanforderungen 4.2 Arten der Wissensverarbeitung 4.3 Anwendung von Entscheidungsbäumen 4.4 Algorithmus der Kurzfristprognose 4.5 Algorithmus der Langzeitprognose 4.6 Fazit 5 Umsetzung und Anwendung der Verfahren 5.1 Vorstellung der Referenzstrecken 5.2 Datenübertragung und Informationsaustausch 5.3 Programmtechnische Umsetzung 5.4 Darstellung der Prognose im Fahrzeug 5.5 Fazit 6 Auswertung, Evaluation und Bewertung 6.1 Methodik 6.2 Prozessdaten und Datenübertragung 6.3 Prognose im gesamten Untersuchungsgebiet 6.4 Prognose auf den Pilotstrecken 6.5 Auswertung von Messfahrten 6.6 Verbesserungsmöglichkeiten der Prognose 7 Zusammenfassung und Ausblick

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In this dissertation, new algorithms that address three traffic problems of major importance are developed. First automatic identification and prediction algorithms are developed to identify and predict the occurrence of traffic congestion. The identification algorithms concoct a model to identify speed thresholds by exploiting historical spatiotemporal speed matrices. We employ the speed model to define a cutoff speed separating free-flow from congested traffic. We further enhance our algorithm by utilizing weather and visibility data. To our knowledge, we are the first to include weather and visibility variables in formulating an automatic congestion identification model. We also approach the congestion prediction problem by adopting an algorithm which employs Adaptive Boosting machine learning classifiers again something novel that has not been done previously. The algorithm is promising where it resulted in a true positive rate slightly higher than 0.99 and false positive rate less than 0.001. We next address the issue of travel time modeling. We propose algorithms to model travel time using various machine learning and statistical learning techniques. We obtain travel time models by employing the historical spatiotemporal speed matrices in conjunction with our algorithms. The algorithms yield pertinent information regarding travel time reliability and prediction of travel times. Our proposed algorithms give better predictions compared to the state of practice algorithms. Finally we consider driver safety at signalized intersections and uncontrolled intersections in a connected vehicles environment. For signalized intersections, we exploit datasets collected from four controlled experiments to model the stop-run behavior of the driver at the onset of the yellow indicator for various roadway surface conditions and multiple vehicle types. We further propose a new variable (predictor) related to driver aggressiveness which we estimate by monitoring how drivers respond to yellow indications. The performance of the stop-run models shows improvements after adding the new aggressiveness predictor. The proposed models are practical and easy to implement in advanced driver assistance systems. For uncontrolled intersections, we present a game theory based algorithm that models the intersection as a chicken game to solve the conflicts between vehicles crossing the intersection. The simulation results show a 49% saving in travel time on average relative to a stop control when the vehicles obey the Nash equilibrium of the game. / Ph. D.