The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data

Multiple Imputation (MI) methods have been widely applied in economic applications as a robust statistical way to incorporate data where some observations have missing values for some variables. However in Stochastic Frontier Analysis (SFA), application of these techniques has been sparse and the case for such models has not received attention in the appropriate academic literature. This paper fills this gap and explores the robust properties of MI within the stochastic frontier context. From a methodological perspective, we depart from the standard MI literature by demonstrating, conceptually and through simulation, that it is not appropriate to use imputations of the dependent variable within the SFA modelling, although they can be useful to predict the values of missing explanatory variables. Fundamentally, this is because efficiency analysis involves decomposing a residual into noise and inefficiency and as a result any imputation of a dependent variable would be imputing efficiency based on some concept of average inefficiency in the sample. A further contribution that we discuss and illustrate for the first time in the SFA literature, is that using auxiliary variables (outside of those contained in the SFA model) can enhance the imputations of missing values. Our empirical example neatly articulates that often the source of missing data is only a sub-set of components comprising a part of a composite (or complex) measure and that the other parts that are observed are very useful in predicting the value

Strategies of anomalies detection in bridges and tunnels as a tool for structural health management. New approaches and AI support.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Proceedings of the ECMLPKDD 2015 Doctoral Consortium

ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium

Proceedings of the 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022

The 4th Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS) was held in Dresden, Germany, from November 30th to December 2nd, 2022. Organized by the Chair of Traffic Process Automation (VPA) at the “Friedrich List” Faculty of Transport and Traffic Sciences of the TU Dresden, the proceedings of this conference are published as volume 9 in the Chair’s publication series “Verkehrstelematik” and contain a large part of the presented conference extended abstracts. The focus of the MFTS conference 2022 was cooperative management of multimodal transport and reflected the vision of the professorship to be an internationally recognized group in ITS research and education with the goal of optimizing the operation of multimodal transport systems. In 14 MFTS sessions, current topics in demand and traffic management, traffic control in conventional, connected and automated transport, connected and autonomous vehicles, traffic flow modeling and simulation, new and shared mobility systems, digitization, and user behavior and safety were discussed. In addition, special sessions were organized, for example on “Human aspects in traffic modeling and simulation” and “Lesson learned from Covid19 pandemic”, whose descriptions and analyses are also included in these proceedings.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the FutureDas 4. Symposium zum Management zukünftiger Autobahn- und Stadtverkehrssysteme (MFTS) fand vom 30. November bis 2. Dezember 2022 in Dresden statt und wurde vom Lehrstuhl für Verkehrsprozessautomatisierung (VPA) an der Fakultät Verkehrswissenschaften„Friedrich List“ der TU Dresden organisiert. Der Tagungsband erscheint als Band 9 in der Schriftenreihe „Verkehrstelematik“ des Lehrstuhls und enthält einen Großteil der vorgestellten Extended-Abstracts des Symposiums. Der Schwerpunkt des MFTS-Symposiums 2022 lag auf dem kooperativen Management multimodalen Verkehrs und spiegelte die Vision der Professur wider, eine international anerkannte Gruppe in der ITS-Forschung und -Ausbildung mit dem Ziel der Optimierung des Betriebs multimodaler Transportsysteme zu sein. In 14 MFTS-Sitzungen wurden aktuelle Themen aus den Bereichen Nachfrage- und Verkehrsmanagement, Verkehrssteuerung im konventionellen, vernetzten und automatisierten Verkehr, vernetzte und autonome Fahrzeuge, Verkehrsflussmodellierung und -simulation, neue und geteilte Mobilitätssysteme, Digitalisierung sowie Nutzerverhalten und Sicherheit diskutiert. Darüber hinaus wurden Sondersitzungen organisiert, beispielsweise zu „Menschlichen Aspekten bei der Verkehrsmodellierung und -simulation“ und „Lektionen aus der Covid-19-Pandemie“, deren Beschreibungen und Analysen ebenfalls in diesen Tagungsband einfließen.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the Futur

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

An Ecological Analysis of the Impact of Weather, Land Cover and Politics on Childhood Pneumonia in Tanzania

Pneumonia is the main killer of under-five children worldwide. The developing nations suffer the most. But within such countries, the spatial and temporal distribution of pneumonia cases is not uniform; yet little is known of the spatial and temporal distribution of pneumonia or the factors that might affect spatial and temporal variability. This dissertation explores the causes of spatial and temporal variation in under-five pneumonia morbidity in Tanzania. This study uses an ecological analysis to explore weather, land cover and politics as potential drivers of the observed differences in the distribution of pneumonia. A study is at an ecological level when it examines the population-level health aspects. That is, ecological analyses in health studies evaluate groups of people rather than individuals. The current study found that weather variables such as temperature and atmospheric pressure partially explained pneumonia variance. The strength of weather-pneumonia association varies over space and time in both seasonal elements (temporal factors) and broadly-defined climate zones (spatial factors). For example, the prevalence rate was higher in the regions with bimodal rainfall compared with the regions with unimodal rainfall, with a statistically difference 117.3 (95% confidence interval: 36.6 to 198.0) cases per 100,000. In addition, within the regions (mikoa) with unimodal rainfall regime, however, the rainy season (msimu) had lower rates of pneumonia compared to the dry season (kiangazi). Land use and land cover also were partial drivers of pneumonia. Some land cover types—particularly urban areas and croplands—were associated with high rates of childhood pneumonia. In addition, districts (wilaya) categorized as urban land cover had high rates of pneumonia compared to those categorized as only rural. To determine the associations between politics and pneumonia, this study compared the pneumonia cases in the administrative locations that received less central government funding with those locations that were financially rewarded for voting for the ruling party. The locations with lower funding generally had higher rates of childhood pneumonia. However, it is unclear whether these locations had higher rates of childhood pneumonia because of, or in addition, to their funding gaps. In sum, this dissertation evaluated population-level factors affecting distribution of childhood pneumonia. Like other similarly population-level studies, this dissertation provides an understanding of the coarse-scale dynamics related to childhood pneumonia. By so doing, it contributes to the pneumonia etiology scientific literature. That is, this dissertation contributes to the understanding of within-nation pneumonia distribution in developing nations. It is the first in Tanzania to evaluate the impact of weather, land cover and politics on childhood pneumonia. By evaluating the impact of weather and land cover, this dissertation also provides an example of non socio-economic factors affecting health inequalities. By analyzing a large landmass of two main climatic types, this dissertation also contributes appreciation of non-stationarity of temporal variations of childhood pneumonia, in addition to the commonly-evaluated spatial variations

Aplicaciones en Economía del Aprendizaje Automático

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 06-05-2022This Thesis examines problems in economics from a Machine Learning perspective. Emphasisis given on the interpretability of Machine Learning algorithms as opposed to blackbox predictions models. Chapter 1 provides an overview of the terminology and Machine Learning methods used throughout this Thesis. This chapter aims to build a roadmap from simple decision tree models to more advanced ensemble boosted algorithms. Other Machine Learning models are also explained. A discussion of the advances in Machine Learning in economics is also provided along with some of the pitfalls that Machine Learning faces. Moreover, an example of how Shapley values from coalition game theory are used to help infer inference from the Machine Learning models' predictions. Chapter 2 analyses the problem of bankruptcy prediction in the Spanish economy and how Machine Learning, not only provides more predictive accuracy, but can also provide adierent interpretation of the results that traditional econometric models cannot. Several financial ratios are constructed and passed to a series of Machine Learning algorithms. Case studies are provided which may aid in better decision-making from financial institutions. A section containing supplementary material based on further analysis is also provided...Este Tesis examina problemas en economía desde la perspectiva de Aprendizaje Mecánico. Se hace hincapié en la interpretabilidad de los algoritmos de Aprendizaje Mecánico en lugar de modelos de predicción de black-box. Capítulo 1 Proporciona el resumen de la terminología y los métodos de Aprendizaje Mecánico utilizados a lo largo de esta tesis. El objetivo de este capítulo es construir la trayectoria desde un simple árbol de decisión hasta algoritmos impulsados por conjuntos más avanzados. También se explican otros modelos de Machine Learning. Asimismo, se proporciona una discusión de los avances en el Aprendizaje Mecánico en economía junto con algunos de los escollos que enfrenta el aprendizaje automático. Además, un ejemplo sobre cómo se utilizan los valores de Shapley de coalición de teoría de juegos y muestran cómo se puede tomar inferencia de los modelos de predicción. Capítulo 2 Analiza el problema de la predicción de quiebra en la economía española y cómo Aprendizaje Mecánico, no sólo proporciona una mayor precisión predictiva, sino que también puede proporcionar una interpretación diferente de los resultados en la que los modelos econométricos tradicionales no pueden. Se construyen una serie de ratios financieros y se pasan a una serie de algoritmos de Aprendizaje Mecánico. Se proporcionan estudios de casos que pueden ayudar a mejorar la toma de decisiones por parte de las instituciones financieras. También se proporciona una sección que contiene material complementario basado en un análisis más detallado...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu