Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Analyse et détection des trajectoires d'approches atypiques des aéronefs à l'aide de l'analyse de données fonctionnelles et de l'apprentissage automatique

L'amélioration de la sécurité aérienne implique généralement l'identification, la détection et la gestion des événements indésirables qui peuvent conduire à des événements finaux mortels. De précédentes études menées par la DSAC, l'autorité de surveillance française, ont permis d'identifier les approches non-conformes présentant des déviations par rapport aux procédures standards comme des événements indésirables. Cette thèse vise à explorer les techniques de l'analyse de données fonctionnelles et d'apprentissage automatique afin de fournir des algorithmes permettant la détection et l'analyse de trajectoires atypiques en approche à partir de données sol. Quatre axes de recherche sont abordés. Le premier axe vise à développer un algorithme d'analyse post-opérationnel basé sur des techniques d'analyse de données fonctionnelles et d'apprentissage non-supervisé pour la détection de comportements atypiques en approche. Le modèle sera confronté à l'analyse des bureaux de sécurité des vols des compagnies aériennes, et sera appliqué dans le contexte particulier de la période COVID-19 pour illustrer son utilisation potentielle alors que le système global ATM est confronté à une crise. Le deuxième axe de recherche s'intéresse plus particulièrement à la génération et à l'extraction d'informations à partir de données radar à l'aide de nouvelles techniques telles que l'apprentissage automatique. Ces méthodologies permettent d'améliorer la compréhension et l'analyse des trajectoires, par exemple dans le cas de l'estimation des paramètres embarqués à partir des paramètres radar. Le troisième axe, propose de nouvelles techniques de manipulation et de génération de données en utilisant le cadre de l'analyse de données fonctionnelles. Enfin, le quatrième axe se concentre sur l'extension en temps réel de l'algorithme post-opérationnel grâce à l'utilisation de techniques de contrôle optimal, donnant des pistes vers de nouveaux systèmes d'alerte permettant une meilleure conscience de la situation.Improving aviation safety generally involves identifying, detecting and managing undesirable events that can lead to final events with fatalities. Previous studies conducted by the French National Supervisory Authority have led to the identification of non-compliant approaches presenting deviation from standard procedures as undesirable events. This thesis aims to explore functional data analysis and machine learning techniques in order to provide algorithms for the detection and analysis of atypical trajectories in approach from ground side. Four research directions are being investigated. The first axis aims to develop a post-op analysis algorithm based on functional data analysis techniques and unsupervised learning for the detection of atypical behaviours in approach. The model is confronted with the analysis of airline flight safety offices, and is applied in the particular context of the COVID-19 crisis to illustrate its potential use while the global ATM system is facing a standstill. The second axis of research addresses the generation and extraction of information from radar data using new techniques such as Machine Learning. These methodologies allow to \mbox{improve} the understanding and the analysis of trajectories, for example in the case of the estimation of on-board parameters from radar parameters. The third axis proposes novel data manipulation and generation techniques using the functional data analysis framework. Finally, the fourth axis focuses on extending the post-operational algorithm into real time with the use of optimal control techniques, giving directions to new situation awareness alerting systems

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023