Predicting Short-Term Traffic Congestion on Urban Motorway Networks

Traffic congestion is a widely occurring phenomenon caused by increased use of vehicles on roads resulting in slower speeds, longer delays, and increased vehicular queueing in traffic. Every year, over a thousand hours are spent in traffic congestion leading to great cost and time losses. In this thesis, we propose a multimodal data fusion framework for predicting traffic congestion on urban motorway networks. It comprises of three main approaches. The first approach predicts traffic congestion on urban motorway networks using data mining techniques. Two categories of models are considered namely neural networks, and random forest classifiers. The neural network models include the back propagation neural network and deep belief network. The second approach predicts traffic congestion using social media data. Twitter traffic delay tweets are analyzed using sentiment analysis and cluster classification for traffic flow prediction. Lastly, we propose a data fusion framework as the third approach. It comprises of two main techniques. The homogeneous data fusion technique fuses data of same types (quantitative or numeric) estimated using machine learning algorithms. The heterogeneous data fusion technique fuses the quantitative data obtained from the homogeneous data fusion model and the qualitative or categorical data (i.e. traffic tweet information) from twitter data source using Mamdani fuzzy rule inferencing systems. The proposed work has strong practical applicability and can be used by traffic planners and decision makers in traffic congestion monitoring, prediction and route generation under disruption

Numerical Computation, Data Analysis and Software in Mathematics and Engineering

The present book contains 14 articles that were accepted for publication in the Special Issue “Numerical Computation, Data Analysis and Software in Mathematics and Engineering” of the MDPI journal Mathematics. The topics of these articles include the aspects of the meshless method, numerical simulation, mathematical models, deep learning and data analysis. Meshless methods, such as the improved element-free Galerkin method, the dimension-splitting, interpolating, moving, least-squares method, the dimension-splitting, generalized, interpolating, element-free Galerkin method and the improved interpolating, complex variable, element-free Galerkin method, are presented. Some complicated problems, such as tge cold roll-forming process, ceramsite compound insulation block, crack propagation and heavy-haul railway tunnel with defects, are numerically analyzed. Mathematical models, such as the lattice hydrodynamic model, extended car-following model and smart helmet-based PLS-BPNN error compensation model, are proposed. The use of the deep learning approach to predict the mechanical properties of single-network hydrogel is presented, and data analysis for land leasing is discussed. This book will be interesting and useful for those working in the meshless method, numerical simulation, mathematical model, deep learning and data analysis fields

Confection de tournées de livraison dans un réseau urbain à l’aide de métaheuristiques et de méthodes de forage de données massives

RÉSUMÉ: La confection de tournées de livraison dans un réseau urbain est un problème qui peut faire appel à la recherche opérationnelle, au forage de données, à l’intelligence artificielle et à la géomatique, entre autres disciplines. Il s’agit de trouver une affectation des clients aux véhicules de livraison et un ordre de visite des clients pour chacun des véhicules afin d’optimiser une certaine fonction objectif. La considération d’une version dynamique du problème, où le processus d’optimisation doit aussi tenir compte de nouvelles données qui affluent en temps réel, rend la résolution encore plus complexe. De tels problèmes se retrouvent, par exemple, dans les entreprises qui offrent des services de livraison à domicile. Dans un contexte d’intégration, de globalisation et de compétitivité, ces entreprises se doivent de prendre les décisions menant aux meilleures tournées de livraison possibles. La recherche d’une solution optimale est souvent impossible pour des instances de taille réaliste. Afin d’obtenir de bonnes solutions dans des temps de calcul raisonnables, des approches heuristiques et métaheuristiques ont été proposées dans la littérature. Cependant, la majorité des travaux se basent sur des instances synthétiques qui, entre autres, négligent la topologie des réseaux routiers rencontrés dans la pratique. Cette thèse vise, dans un premier temps, à contribuer à l’intégration des méthodes de forage de données et d’apprentissage automatique dans la prédiction des vitesses de parcours dans un réseau routier réel. Grâce à la collaboration d’un partenaire industriel, nous disposons d’une base de données de points GPS recueillis dans la région métropolitaine de Montréal à partir de dispositifs embarqués dans des véhicules de livraison, couvrant une période de plus de deux ans. Nous proposons une méthode de prédiction des vitesses sur les arcs de ce réseau en faisant appel au forage de données massives et à l’apprentissage automatique : techniques de réduction de dimensionnalité, méthodes d’imputation et d’apprentissages supervisé et non supervisé. L’ensemble de cette méthodologie a fait l’objet d’un article, actuellement en révision, soumis à la revue scientifique EURO Journal on Transportation and Logistics. Cette thèse vise aussi à contribuer à l’avancement des métaheuristiques pour la résolution de problèmes de tournées de livraisons à domicile définies sur un réseau routier, avec des vitesses et temps de parcours qui dépendent du moment de la journée, des fenêtres de temps pour le service aux clients et une capacité maximale pour les véhicules. Plus précisément, nous proposons une recherche tabou qui remet en question l’ordre de visite des clients ainsi que le chemin utilisé dans le réseau routier pour se rendre d’un client à un autre, en fonction du moment de la journée. Un développement majeur de ce travail est la conception de techniques d’évaluation en temps constant de la réalisabilité ainsi que du coût approximatif des solutions dans le voisinage de la solution courante. Ces techniques permettent de réduire l’effort computationnel et de résoudre des instances avec 200 noeuds et 580 arcs dans des temps de calcul très raisonnables. L’approche de résolution incluant la description des techniques d’évaluation en temps constant de la réalisibilité des solutions dans le voisinage et de leurs coûts approximatifs a fait l’objet d’un article soumis à la revue scientifique Transportation Science. Enfin, la thèse s’attaque à une variante dynamique du problème précédent, dans un contexte où les vitesses sur les arcs du réseau routier varient de façon dynamique due à des incidents imprévus. Un simulateur a été développé pour générer les perturbations dynamiques aux vitesses sur les arcs, tout en respectant les relations spatio-temporelles entre ces derniers. Une procédure permet ensuite de réagir aux perturbations en modifiant la solution courante de façon plus ou moins importante selon l’importance des perturbations. La démarche développée pour résoudre ce problème dynamique de confection de tournées qui inclut l’ajustement à des plans de livraison déjà optimisés dans un contexte statique est présentée dans un article ayant été soumis à la revue scientifique European Journal of Operational Research.----------ABSTRACT: Fleet management for home deliveries in an urban context is at the crossroads between several disciplines such as operations research, data mining, artificial intelligence, geomatic, etc. The objective is to find 1) assignment of customers to vehicles and 2) sequence of customers visited by each vehicle to optimize a certain objective function. Since accurate travel time predictions are of foremost importance in urban environments, it is important to address dynamic variants of vehicle routing problems, because they better model the problems faced by transportation companies like home delivery services. In an era where companies have to integrate their supply chain, face globalization and competitiveness, it is important for them to make decisions leading to the best possible delivery routes. Therefore, in this thesis, we first consider the prediction of travel speeds using as input GPS traces of commercial vehicles collected over a significant period of time. We propose a forecasting framework based on machine learning and data mining techniques: dimensionality reduction techniques, imputation methods, unsupervised and supervised learning. Secondly, we propose a solution approach to solve a time-dependent vehicle routing problem with time windows in which travel speeds are defined on the road network itself. The solution approach involves a tabu search heuristic that considers different shortest paths between pairs of customers at different times of the day. A major contribution of this work is the development of techniques to evaluate the feasibility as well as the approximate cost of a solution in constant time, thus allowing the overall solution approach to handle instances with up to 200 nodes and 580 arcs in very reasonable computing times. Finally, we consider a dynamic vehicle routing problem motivated from home delivery applications that can adapt to changes in speeds by modifying the paths used in the road network to go from one customer to the next and by modifying the sequences of customers in the planned routes. Here, uncertainty comes from a single source, namely, the occurrence of new traffic information that affects speeds. To mimic real-time perturbations in the road network, we developed a simulator that generates traffic events and updates the speeds accordingly

Towards Learning Feasible Hierarchical Decision-Making Policies in Urban Autonomous Driving

Modern learning-based algorithms, powered by advanced deep structured neural nets, have multifacetedly facilitated automated driving platforms, spanning from scene characterization and perception to low-level control and state estimation schemes. Nonetheless, urban autonomous driving is regarded as a challenging application for machine learning (ML) and artificial intelligence (AI) since the learnt driving policies must handle complex multi-agent driving scenarios with indeterministic intentions of road participants. In the case of unsignalized intersections, automating the decision-making process at these safety-critical environments entails comprehending numerous layers of abstractions associated with learning robust driving behaviors to allow the vehicle to drive safely and efficiently. Based on our in-depth investigation, we discern that an efficient, yet safe, decision-making scheme for navigating real-world unsignalized intersections does not exist yet. The state-of-the-art schemes lacked practicality to handle real-life complex scenarios as they utilize Low-fidelity vehicle dynamic models which makes them incapable of simulating the real dynamic motion in real-life driving applications. In addition, the conservative behavior of autonomous vehicles, which often overreact to threats which have low likelihood, degrades the overall driving quality and jeopardizes safety. Hence, enhancing driving behavior is essential to attain agile, yet safe, traversing maneuvers in such multi-agent environments. Therefore, the main goal of conducting this PhD research is to develop high-fidelity learning-based frameworks to enhance the autonomous decision-making process at these safety-critical environments. We focus this PhD dissertation on three correlated and complementary research challenges. In our first research challenge, we conduct an in-depth and comprehensive survey on the state-of-the-art learning-based decision-making schemes with the objective of identifying the main shortcomings and potential research avenues. Based on the research directions concluded, we propose, in Problem II and Problem III, novel learning-based frameworks with the objective of enhancing safety and efficiency at different decision-making levels. In Problem II, we develop a novel sensor-independent state estimation for a safety-critical system in urban driving using deep learning techniques. A neural inference model is developed and trained via deep-learning training techniques to obtain accurate state estimates using indirect measurements of vehicle dynamic states and powertrain states. In Problem III, we propose a novel hierarchical reinforcement learning-based decision-making architecture for learning left-turn policies at four-way unsignalized intersections with feasibility guarantees. The proposed technique involves an integration of two main decision-making layers; a high-level learning-based behavioral planning layer which adopts soft actor-critic principles to learn high-level, non-conservative yet safe, driving behaviors, and a motion planning layer that uses low-level Model Predictive Control (MPC) principles to ensure feasibility of the two-dimensional left-turn maneuver. The high-level layer generates reference signals of velocity and yaw angle for the ego vehicle taking into account safety and collision avoidance with the intersection vehicles, whereas the low-level planning layer solves an optimization problem to track these reference commands considering several vehicle dynamic constraints and ride comfort

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics

Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at CMES-Computer Modeling in Engineering & Science

OIL SPILL ALONG THE TURKISH STRAITS SEA AREA; ACCIDENTS, ENVIRONMENTAL POLLUTION, SOCIO-ECONOMIC IMPACTS AND PROTECTION

The Turkish Straits Sea Area (TSSA) is a long water passage that is consisted of the Sea of Marmara, an inland sea within Turkey's borders, and two narrow straits connected to neighboring seas. With a strategic location between the Balkans and Anatolia, the Black Sea and the Mediterranean, and dominated by the continental climate, the region hosted many civilizations throughout the centuries. This makes the region among the busiest routes in the world, with sea traffic three times higher than that in the Suez Canal. The straits are the most difficult waterways to navigate and witnessed many hazardous and important collisions and accidents throughout history. In addition, this area has vital roles as a biological corridor and barrier among three distinctive marine realms. Therefore, the region is rather sensitive to damages of national and international maritime activities, which may cause severe environmental problems. This book addresses several key questions on a chapter basis, including historical accidents, background information on main dynamic restrictions, oil pollution, oil spill detection, and clean-up recoveries, its impacts on biological communities, socioeconomic aspects, and subjects with international agreements. This book will help readers, public, local and governmental authorities gain a deeper understanding of the status of the oil spill, mostly due to shipping accidents, and their related impacts along the TSSA, which needs precautionary measures to be protected.CONTENTS INTRODUCTION CHAPTER I - HISTORY OF ACCIDENTS AND REGULATIONS Remarkable Accidents at the Istanbul Strait Hasan Bora USLUER and Saim OĞUZÜLGEN …………………………………...... 3 History of Regulations before Republican Era along the Turkish Straits Sea Area Ali Umut ÜNAL …………………………………………………………………….. 16 Transition Regime in the Turkish Straits during the Republican Era Osman ARSLAN ……….……………………………………………………….……26 26 The Montreux Convention and Effects at Turkish Straits Oktay ÇETİN ………………………………………………………………….…….. 33 Evaluation of the Montreux Convention in the Light of Recent Problems Ayşenur TÜTÜNCÜ ………………………………………………………………… 44 A Historical View on Technical Developments on Ships and Effects of Turkish Straits Murat YAPICI ………………………………………………………………………. 55 CHAPTER II - GEOGRAPHY, BATHYMETRY AND HYDRO-METEOROLOGICAL CONDITIONS Geographic and Bathymetric Restrictions along the Turkish Straits Sea Area Bedri ALPAR, Hasan Bora USLUER and Şenol AYDIN ……………………..…… 61 Hydrodynamics and Modeling of Turkish Straits Serdar BEJİ and Tarkan ERDİK ………………………………………………….… 79 Wave Climate in the Turkish Sea of Marmara Tarkan ERDİK and Serdar BEJİ …………………………………………………..… 91 CHAPTER III - OIL POLLUTION, DETECTION AND RECOVERY Oil Pollution at Sea and Coast Following Major Accidents Selma ÜNLÜ ……………………………………………………………………….101 Forensic Fingerprinting in Oil-spill Source Identification at the Turkish Straits Sea Area Özlem ATEŞ DURU ……………………………………………………………… 121 xi Oil Spill Detection Using Remote Sensing Technologies-Synthetic Aperture Radar (SAR) İbrahim PAPİLA, Elif SERTEL, Şinasi KAYA and Cem GAZİOĞLU ……..……. 140 The Role of SAR Remote Sensing to Detect Oil Pollution and Emergency Intervention Saygın ABDIKAN, Çağlar BAYIK and Füsun BALIK ŞANLI ……….….……….. 157 Oil Spill Recovery and Clean-Up Techniques Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………… 176 Turkish Strait Sea Area, Contingency Planning, Regulations and Case Studies Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………... 188 Dispersant Response Method to Incidental Oil Pollution Dilek EDİGER, Leyla TOLUN and Fatma TELLİ KARAKOÇ ………………….... 205 CHAPTER IV - THE EFFECTS / IMPACTS OF OIL SPILL ON BIOLOGICAL COMMUNITIES – INCLUDING SAMPLING AND MONITORING Marine Microorganisms and Oil Spill Sibel ZEKİ and Pelin S. ÇİFTÇİ TÜRETKEN …………...………………………… 219 Estimated Effects of Oil Spill on the Phytoplankton Following “Volgoneft-248” Accident (Sea of Marmara) Seyfettin TAŞ ………………………………..…………………………………….... 229 Interactions between Zooplankton and Oil Spills: Lessons Learned from Global Accidents and a Proposal for Zooplankton Monitoring İ. Noyan YILMAZ and Melek İŞİNİBİLİR ……………………………………..….. 238 The Effects of Oil Spill on the Macrophytobenthic Communities Ergün TAŞKIN and Barış AKÇALI …………………………….…………….……. 244 Potential Impacts of Oil Spills on Macrozoobenthos in the Turkish Straits System Güley KURT-ŞAHİN …………………………………………………………….… 253 The Anticipated Effects of Oil Spill on Fish Populations in Case of an Accident along the Turkish Straits System – A review of Studies after Several Incidents from the World M. İdil ÖZ and Nazlı DEMİREL …………………………………………………….261 Estimated Impacts of an Oil Spill on Bird Populations along the Turkish Straits System Itri Levent ERKOL …………………………………………………………….…… 272 The Effect of Oil Spills on Cetaceans in the Turkish Straits System (TSS) Ayaka Amaha ÖZTÜRK ………………………………………………………….. 277 Changes in the Ichthyoplankton and Benthos Assemblages following Volgoneft-248 Oil Spill: Case Study Ahsen YÜKSEK and Yaprak GÜRKAN …………………………………….……. 280 Assessing the Initial and Temporal Effects of a Heavy Fuel Oil Spill on Benthic Fauna Yaprak GÜRKAN, Ahsen YÜKSEK ………………………………………..…….. 287 CHAPTER V - SOCIO-ECONOMIC ASPECTS Socio-economic Aspects of Oil Spill Özlem ATEŞ DURU and Serap İNCAZ ……………………………………….…… 301 Effects of Oil Spill on Human Health Türkan YURDUN ………………………………………………………………..…. 313 Crisis Management of Oil Spill, A Case Study: BP Gulf Mexico Oil Disaster Serap İNCAZ and Özlem ATEŞ DURU …………………………….………….……324 CHAPTER VI - CONVENTIONS RELATING TO PREVENTION OF OIL SPILL International Convention for the Prevention of Pollution of the Sea by Oil (OILPOL), 1954 and its Situation Related with Turkey Emre AKYÜZ, Metin ÇELİK and Ömer SÖNER …………………………...……... 334 International Convention for the Prevention of Pollution from Ships, 1973, as Modified by the Protocol of 1978 Relating Thereto and by the Protocol of 1997 (MARPOL) Özcan ARSLAN, Esma UFLAZ and Serap İNCAZ ………………………….……. 342 Applications of MARPOL Related with Oil Spill in Turkey Emre AKYÜZ, Özcan ASLAN and Serap İNCAZ ………………………………… 356 Ship Born Oil Pollution at the Turkish Straits Sea Area and MARPOL 73/78 Duygu ÜLKER and Sencer BALTAOĞLU………………………….…………….. 363 International Convention Relating to Intervention on the High Seas in Cases of Oil Pollution Casualties (INTERVENTION 1969) and its Applications Related with Oil Spill in Turkey Şebnem ERKEBAY ……………………………….……………………………….. 371 International Convention on Oil Pollution Preparedness, Response and Co-operation (OPRC) 1990 and its Applications Related with Oil Spill in Turkey Kadir ÇİÇEK ………………………………………………………………………. 381 Protocol on Preparedness, Response and Co-operation to Pollution Incidents by Hazardous and Noxious Substances, 2000 (OPRC-HNS Protocol) and its Effects in Turkey Aydın ŞIHMANTEPE and Cihat AŞAN ……………….…………………………. 392 The International Convention on Salvage (SALVAGE) 1989 Related with Oil Spill in Turkey İrşad BAYIRHAN ……………………………………….………………..……….. 408 CHAPTER VII - CONVENTIONS COVERING LIABILITY AND COMPENSATION RELATED WITH OIL SPILL International Convention on Civil Liability for Oil Pollution Damage (CLC), 1969 and its Applications Serap İNCAZ and Pınar ÖZDEMİR ……………………………………..………… 416 1992 Protocol to the International Convention on the Establishment of an International Fund for Compensation for Oil Pollution Damage (FUND 1992) and its Applications Related with Oil Spill in Turkey Ali Umut ÜNAL and Hasan Bora USLUER …………………………….………… 424 International Convention on Liability and Compensation for Damage in Connection with the Carriage of Hazardous and Noxious Substances by Sea (HNS), 1996 (and its 2010 Protocol) and its Applications Related with Oil Spill in Turkey Bilun ELMACIOĞLU ……………………………………………………………… 437 Bunkering Incidents and Safety Practices in Turkey Fırat BOLAT, Pelin BOLAT and Serap İNCAZ …………………………………... 447 "Nairobi International Convention on the Removal of Wrecks 2007" and its Effects on Turkey Şafak Ümit DENİZ and Serap İNCAZ ……………………….……………………. 457

Recent Trends in Computational Research on Diseases

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin