Advances in crowd analysis for urban applications through urban event detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined

Predicting Bus End-Trip Delays Using Different Machine Learning Algorithms to Model Planning Effectiveness

RÉSUMÉ : Le transport public existe presque partout dans le monde. Cela permet à toutes les personnes le désirant de se déplacer d’un endroit à un autre d’une ville de façon économique et écologique. De plus, de plus en plus de données sont disponibles de nos jours grâce aux systèmes embarqués à l’intérieur des véhicules. Ces données pourraient être utilisées dans une optique de prévision des retards, qui permettraient par la suite de les anticiper. Ainsi la fiabilité des horaires serait améliorée et plus de gens seraient susceptibles d’employer ce mode de transport. Des travaux ont été réalisés afin de prédire les retards en utilisant différentes données, cependant aucune d’elle ne l’a fait dans l’idée d’intégrer ces prévisions dans les procédures de création de planification de trajet. Au cours de ce mémoire, divers modèles de prédiction de retard pour les fins de trajet sont essayés. Il ne s’agit pas de prédire le retard exact, mais de classifier les retards des fins de trajet. Afin d’être utile aux planificateurs d’horaires, ces modèles n’utilisent que des données qui peuvent se trouver en amont de la planification. Les données exploitées pour les modèles sont des observations historiques de la ville de Montréal. Deux problèmes de classification sont abordés au cours de ce mémoire. Le premier est un modèle de classification binaire qui prédit si un bus va finir son trajet en retard ou à l’heure. Le second est un modèle qui prévoit dans quel créneau de retard le bus va finir son trajet. Pour chacun des problèmes, trois algorithmes de machine learning pour l’estimation des retards sont testés : réseau de neurones, forêt aléatoire et arbre stimulé par gradient. De plus, une régression logistique est également testée afin de comparer les résultats par rapport à une méthode plus standard. Les modèles sont optimisés selon différentes méthodes et sont comparés en terme de précision et de temps d’entraînement. Les modèles sont par la suite entraînés sur une période et testés sur d’autres afin d’étudier la possibilité d’intégrer ces modèles dans le processus de création de lignes. Par la suite, les prédictions sont utilisées afin de créer des distributions de probabilité pour les différents crénaux de retard pour les fins de trajet des bus. Les différents algorithmes sont testés afin de distinguer ceux qui reproduisent au mieux la réalité. Le projet conclut sur la possibilité d’utiliser les données de planning pour prédire le retard des fins de trajet des bus. Une classification sur plusieurs classes peut être améliorée en intégrant de l’apprentissage non supervisée afin de déterminer les classes de retard. Il est également possible d’entraîner un modèle sur des périodes passées afin de prédire sur de futures périodes, mais cette méthode doit être encore améliorée.----------ABSTRACT : Public transportation services are provided in almost all the cities of the world. They allow people to move through the cities in an economical and eco-friendly way. The buses are one of the possible solutions for public transportation. Moreover buses are interesting to study because more data are available from onboard systems and can be used to optimize service quality. Indeed, preventing delays could improve service reliability and thus make people more likely to use public transport instead of their cars, which are currently more comfortable and more reliable. The first step in this process would be to forecast the delays. A lot of factors are linked to delays: peak-hour traÿc, weather or accidents, etc. Some studies were conducted to predict end trips delay using real-time input which does not allow improvement to schedule reliability because these data are not available during planning. This research focuses on modeling end-trip arrival time for each bus trip based only on o˜ine input available to public transport planner. The models do not intend to predict the exact delays, but rather to classify them. The delays used to train and test the models are historical observations from the city of Montreal in autumn 2017. Two di˙erent classification problems were treated. The first one estimates the probability for a trip to end on-time or late. The second one estimates the slot of delay. For each problem, three di˙erent machine learning models were built and optimized: random forest, gradient boosted tree and artificial neural network. Also, logistic regression was tested in order to compare the results. Several optimization methods were tried. The models are compared in term of accuracy, recall, f1 score and training time. The data from another period (autumn 2016) were then added to the database, and the model tested on the aggregated database. The model accuracy remained constant after the addition of the new period. The models were then fit on a single period (autumn 2016) and tested on the other one (autumn 2017) in order to check the possibility to use the model to forecast future schedules. The prediction is then used to generate a probability distribution for the di˙erent trips to end late to assess service reliability. The probability distributions are then compared with reality by comparing the distance between them and the frequencies of delays for the di˙erent trips. Normal distribution was also tested and obtained better results than the machine learning models. The project concluded that it is possible to model end trip delays using o˜ine data. Multi-label classification can be improved by using unsupervised learning to determine classes

Mutational mechanisms shaping the coding and noncoding genome of germinal center derived B-cell lymphomas

B cells have the unique property to somatically alter their immunoglobulin (IG) genes by V(D)J recombination, somatic hypermutation (SHM) and class-switch recombination (CSR). Aberrant targeting of these mechanisms is implicated in lymphomagenesis, but the mutational processes are poorly understood. By performing whole genome and transcriptome sequencing of 181 germinal center derived B-cell lymphomas (gcBCL) we identified distinct mutational signatures linked to SHM and CSR. We show that not only SHM, but presumably also CSR causes off-target mutations in non-IG genes. Kataegis clusters with high mutational density mainly affected early replicating regions and were enriched for SHM- and CSR-mediated off-target mutations. Moreover, they often co-occurred in loci physically interacting in the nucleus, suggesting that mutation hotspots promote increased mutation targeting of spatially co-localized loci (termed hypermutation by proxy). Only around 1% of somatic small variants were in protein coding sequences, but in about half of the driver genes, a contribution of B-cell specific mutational processes to their mutations was found. The B-cell-specific mutational processes contribute to both lymphoma initiation and intratumoral heterogeneity. Overall, we demonstrate that mutational processes involved in the development of gcBCL are more complex than previously appreciated, and that B cell-specific mutational processes contribute via diverse mechanisms to lymphomagenesis

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

Real-time peak power prediction for zinc nickel single flow batteries

The Zinc Nickel single flow batteries (ZNBs) have gained increasing attention recently. Due to the high variability of the intermittent renewable energy sources, load demands, and the operating conditions, the state of charge (SoC) is not an ideal indicator to gauge the potential cycling abilities. Alternatively, the peak power is more closely related to the instantaneous power acceptance and deliverance, and its real-time estimation plays a key role in grid-based energy storage systems. However, little has been done to comprehensively examine the peak power delivery capability of Zinc Nickel single flow batteries (ZNBs). To fill this gap, the recursive least square (RLS) method is first employed to achieve online battery model identification and represent the impact of varying working conditions. The state of charge (SoC) is then estimated by the extended Kalman filter (EKF). With these preliminaries, a novel peak power prediction method is developed based on the rolling prediction horizon. Four indices are proposed to capture the characteristics of the peak power capability over length-varying prediction windows. Finally, the consequent impacts of the electrode material and applied flow rate on peak power deliverability are analysed qualitatively

Platooning-based control techniques in transportation and logistic

This thesis explores the integration of autonomous vehicle technology with smart manufacturing systems. At first, essential control methods for autonomous vehicles, including Linear Matrix Inequalities (LMIs), Linear Quadratic Regulation (LQR)/Linear Quadratic Tracking (LQT), PID controllers, and dynamic control logic via flowcharts, are examined. These techniques are adapted for platooning to enhance coordination, safety, and efficiency within vehicle fleets, and various scenarios are analyzed to confirm their effectiveness in achieving predetermined performance goals such as inter-vehicle distance and fuel consumption. A first approach on simplified hardware, yet realistic to model the vehicle's behavior, is treated to further prove the theoretical results. Subsequently, performance improvement in smart manufacturing systems (SMS) is treated. The focus is placed on offline and online scheduling techniques exploiting Mixed Integer Linear Programming (MILP) to model the shop floor and Model Predictive Control (MPC) to adapt scheduling to unforeseen events, in order to understand how optimization algorithms and decision-making frameworks can transform resource allocation and production processes, ultimately improving manufacturing efficiency. In the final part of the work, platooning techniques are employed within SMS. Autonomous Guided Vehicles (AGVs) are reimagined as autonomous vehicles, grouping them within platoon formations according to different criteria, and controlled to avoid collisions while carrying out production orders. This strategic integration applies platooning principles to transform AGV logistics within the SMS. The impact of AGV platooning on key performance metrics, such as makespan, is devised, providing insights into optimizing manufacturing processes. Throughout this work, various research fields are examined, with intersecting future technologies from precise control in autonomous vehicles to the coordination of manufacturing resources. This thesis provides a comprehensive view of how optimization and automation can reshape efficiency and productivity not only in the domain of autonomous vehicles but also in manufacturing