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Über dieses Buch

This book gathers the outcomes of the second ECCOMAS CM3 Conference series on transport, which addressed the main challenges and opportunities that computation and big data represent for transport and mobility in the automotive, logistics, aeronautics and marine-maritime fields. Through a series of plenary lectures and mini-forums with lectures followed by question-and-answer sessions, the conference explored potential solutions and innovations to improve transport and mobility in surface and air applications.

The book seeks to answer the question of how computational research in transport can provide innovative solutions to Green Transportation challenges identified in the ambitious Horizon 2020 program. In particular, the respective papers present the state of the art in transport modeling, simulation and optimization in the fields of maritime, aeronautics, automotive and logistics research. In addition, the content includes two white papers on transport challenges and prospects.

Given its scope, the book will be of interest to students, researchers, engineers and practitioners whose work involves the implementation of Intelligent Transport Systems (ITS) software for the optimal use of roads, including safety and security, traffic and travel data, surface and air traffic management, and freight logistics.

Inhaltsverzeichnis

Frontmatter

White Paper

Frontmatter

Chapter 1. Digital Technologies for Transport and Mobility: Challenges, Trends and Perspectives

Abstract
This white paper aims at presenting the ideas emerging from the different fields pertaining to transport and mobility, to describe the capacities of current state-of-the-art digital technologies and the perspectives that are expected to shape the future of transport and mobility.
Pedro Diez, Jacques Periaux, Tero Tuovinen, Jaana Räisänen, Martti Lehto, Adel Abbas, Carlo Poloni, Trond Kvamsdal, Chris Bronk

Reviews and Perspectives

Frontmatter

Chapter 2. Cyber Security in Aviation, Maritime and Automotive

Abstract
Critical information infrastructures support vital services such as energy, transport, telecommunications, financial services, etc., that are so essential that their unavailability may adversely affect the well-being of a nation. Transport is a critical national infrastructure. Disruption to the transport network has significant impacts on everyday life of citizens, national defence, security, and the vital functions of the state. This critical infrastructure is managed and maintained by a complex set of actors, each of whom tackle cyber security differently. The cyber security risk landscape in transport is currently evolving towards the point that risks that were once considered unlikely began occurring with regularity. This ongoing trend can be attributed to higher maturity of attack tools and methods, increased exposure, and increased motivation of attackers. In the past, most of the attacks were conventional and the attackers individuals or small groups of hackers. Now these very-high-impact risks will also force us to become better at protecting our assets and devising creative solutions to mitigate risks. Safety and security are two sides of the same coin. The cyber security threat is increasingly becoming cyber-physical, as vehicles, aircrafts, vessels, infrastructure, and control systems become increasingly connected. Accordingly, physical safety—an established practice across transport sectors—and cyber security will become one and the same. This necessitates a significant shift in the manufacturing and operation culture of the transport. This article introduces cyber security threat and risk environment in aviation, maritime and automotive. In addition, the article presents examples cyber-attacks against those systems. The article also discusses the principles that should be implemented in cyber security in transport.
Martti Lehto

Chapter 3. Operation of Transport and Logistics in a Time of (Cyber)Insecurity

Abstract
All of transport is becoming increasingly automated and computerized. Consider the recent Boeing 737 Max 8 accidents in Indonesia and shortly thereafter. All indications point to a software error in code adopted following a redesign of the aircraft in which larger engines were installed than on previous Boeing 737 models as the likely culprit in both accidents. A computer software error likely overwhelmed both doomed flight crews as they fought the computerized instructions that were being given to the aircraft shortly after takeoff. A total of 346 persons died in the two accidents. Their loss should give stark warning about the safety of computer code emplaced in transportation systems: land, maritime, and air. There is little evidence of liability in commodity software. Since the infancy of modern operating systems, computer bugs, crashes, and hacks have plagued the Information Technology (IT) industry. While countless business plans, academic paper drafts, and personal correspondence have been lost to software bugs and crashes, the idea of filing suit on commodity productivity and operating system software manufacturers for damages has always been and remains a preposterous idea. What should concern us in transport is a new phenomenon—the merging of automation or process control software with network interconnectivity. Whether an aircraft autopilot, a shipboard navigation system, or a self-driving semi-truck, there remains a drive to interconnect these systems with others, often via the same technology and protocols that encompass the Internet. Unfortunately, the Internet remains a rickety ship with regard to security from unauthorized manipulation or subversion. Establishing the ideational footing for a risk and remediation strategy for this problem in the transport sector is the purpose of this paper.
Chris Bronk

Chapter 4. New Data and Methods for Modelling Future Urban Travel Demand: A State of the Art Review

Abstract
This paper aims is to provide an overview of how new data collection methods and the various advances in urban travel demand modelling are improving the understanding of mobility. These new modelling applications and data allow for a study of both new disruptive transport services and changes in travel behaviours in the “Mobility as a Service” (MaaS) context that needs to be overcome in the future.
Sara A. Puignau Arrigain, Jordi Pons-Prats, Sergi Saurí Marchán

Chapter 5. Maritime Transport and the Threat of Bio Invasion and the Spread of Infectious Disease

Abstract
We overview the threat that shipping poses for the invasive spread of non-indigenous species and spread of infectious disease, focusing in particular on invasions of the aedes aegypti and aedes albopictus mosquitos and their role in the spread of infectious disease. We conclude with a brief discussion of the computational prediction of these processes.
William Fitzgibbon, Jeffrey Morgan, Glenn Webb, Yixiang Wu

Computational Methods and Applications

Frontmatter

Chapter 6. Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems

Abstract
Vehicle Routing Problems (VRP) are computationally challenging, constrained optimization problems, which have central role in logistics management. Usually different solvers are being developed and applied for different kind of problems. However, if descriptive and general features could be extracted to describe such problems and their solution attempts, then one could apply data mining and machine learning methods in order to discover general knowledge on such problems. The aim then would be to improve understanding of the most important characteristics of VRPs from both efficient solution and utilization points of view. The purpose of this article is to address these challenges by proposing a novel feature analysis and knowledge discovery process for Capacitated Vehicle Routing problems (CVRP). Results of knowledge discovery allow us to draw interesting conclusions from relevant characteristics of CVRPs.
Tommi Kärkkäinen, Jussi Rasku

Chapter 7. A New Multi-objective Solution Approach Using ModeFRONTIER and OpenTrack for Energy-Efficient Train Timetabling Problem

Abstract
Trains move along the railway infrastructure according to specific timetables. The timetables are based on the running time calculation and they are usually calculated without considering explicitly energy consumption. Since green transportation is becoming more and more important from environmental perspectives, energy consumption minimization could be considered also in timetable calculation. In particular, the Energy-Efficient Train Timetabling Problem (EETTP) consists in the energy-efficient timetable calculation considering the trade-off between energy efficiency and running times. In this work, a solution approach to solve a multi-objective EETTP is described in which the two objectives are the minimization of both energy consumption and the total travel time. The approach finds the schedules to guarantee that the train speed profiles minimize the objectives. It is based on modeFRONTIER and OpenTrack that are integrated by using the OpenTrack Application Programming Interface in a modeFRONTIER workflow. In particular, the optimization is made by modeFRONTIER, while the calculation of the train speed profiles, energy consumption and total travel time is made by OpenTrack. The approach is used with Multi-objective Genetic Algorithm-II and the Non-dominating Sorting Genetic-II, which are two genetic algorithms available in modeFRONTIER. The solution approach is tested on a case study that represents a real situation of metro line in Turkey. For both algorithms, a Pareto Front of solution which are a good trade-off between the objectives are reported. The results show significant reduction of both energy consumption and total travel time with respect to the existing timetable.
Giovanni Longo, Teresa Montrone, Carlo Poloni

Chapter 8. Development of New Lagrangian Computational Methods for Ice-Ship Interaction Problems: NICESHIP Project

Abstract
This document presents the activities carried out to date (04/2019) in the project ‘Development of new Lagrangian computational methods for ice-ship interaction problems’ (NICE-SHIP). The NICE-SHIP project aims at developing a new generation of computational methods, based on the integration of innovative Lagrangian particle-based and finite element procedures for the analysis of the operation of a vessel in an iced sea, taking into account the different possible conditions of the ice. It is expected that the computational analysis techniques to be developed in NICE-SHIP will allow ice-class vessel designers to accurately evaluate the loads acting on the structure of a ship navigating in iced-seas and, in particular, to determine the ice resistance of the ship in different ice conditions.
Julio García-Espinosa, Eugenio Oñate, Borja Serván Camas, Miguel Angel Celigueta, Salva Latorre, Jonathan Colom-Cobb

Chapter 9. Delivery Service in Congested Urban Areas

Abstract
Nowadays logistical costs are significant in many developing countries, for instance, basing upon the last researches, in Russian Federation they make up 20 %. No doubts that heavy traffic congestions in modern urban areas impact directly on vehicle routing costs in road networks. Moreover, logistics companies are faced with lost profits since actually they serve less number of customers then they could planned, because of traffic congestions. Thus, contemporary approaches for planning delivery routes should necessarily take into account traffic information. Herewith, accuracy of such information is crucial since all systems for traffic congestions prediction are highly sensitive to input data. Wide spread of traffic counters, plate-scanning sensors, in-vehicle guide systems can certainly provide accurate data collection. However, emphasize that data collection only is fruitless without intellectual data processing. The present paper is devoted to development of optimization approach which incorporates modern data collection systems and contemporary mathematical tools to cope with comprehensive delivery planning under traffic congestions in road networks. Implementation of the approach to Saint Petersburg city demonstrates reduction of actual travel time of delivery vehicles in the congested road network by 8–16%.
Victor Zakharov, Alexander Krylatov, Alexander Mugayskikh

Translational Research

Frontmatter

Chapter 10. Current CAE Trends in the Automotive Industry

Abstract
This paper provides a compact review of the current Computer Aided Engineering (CAE) trends in the automotive industry from the perspective of Volkswagen Group research. CAE has established itself as an integral part of the vehicle engineering design process. Moreover, it provides the foundation for success in a very competitive market. In order to reduce costs and time to market, the number of physical prototypes needs to be reduced. This can only be accomplished through the systematic advancement of virtual methodologies that support the brands to evolve towards a 100% simulation-based prototype-free product development environment. Several relevant areas of vehicle development are considered. Starting with the issue of safety, the topics of finite-element human body modelling for occupant and pedestrian safety evaluation as well as structural vehicle crash simulations are discussed. Other topics such as the usage of computational fluid dynamics methods in the areas of vehicle aerodynamics and aeroacoustics are also considered. Finally, new areas of methods development are briefly discussed by showing novel applications of reduced order modelling and artificial intelligence methods including Big Data analysis. These methodologies will provide the basis for greatly accelerating solver speed and ensure the extraction of more information from simulation results.
Vasileios Tsiolakis, Henry P. Bensler

Chapter 11. Establishment of MoS in Chile: Pertinence Assessment Through an Analysis of Previous Scenarios

Abstract
With the opening of the Panama Canal, Chile is adapting its transport logistics to the expected arrival of larger container vessels by assuming the establishment of a hub port in its central region. This paper tackles the feasibility of the intermodal chains through MoS to feed the North and the South regions from this central hub port. Due to the features of Chile, the intermodal distances are similar to the unimodal distances. This fact along with the remarkable imbalance of the cargo flows between the North and the South are an additional challenge for the success of the intermodality. In order to support the opportunities of success of the intermodality this study defines, through the optimization of a mathematical model, the most adequate fleets for MoS in the North and South of Chile. Likewise, assuming identical conditions for all Chilean ports (previous scenarios), the resolution of the model identifies the most suitable peripheral ports to articulate MoS from a large-scale hub port in the central region of Chile. The results show that, the intermodality is a competitive solution in the north, but it is not in the south when optimized fleets are used.
Alba Martínez-López, Trujillo Lourdes, Chica González Manuel

Chapter 12. Radars in Transport Applications

Abstract
In the recent years, automotive car industry is evolving towards a new generation of autonomous vehicles, where decision making is not fully perform by the driver but it partially relies on the technology of the car itself. Indeed, a CPU inside the car will process all information coming from the sensors, distinguishing different scenarios appearing in the real life and ultimately allowing decision making. Since the CPU will be confronted with plenty of information, tools like machine learning or big-data analysis will be a useful ally to separate data from information. These existing machine learning techniques, such as kernel Principal Component Analysis (k-PCA), Locally Linear Embedding (LLE) among many other techniques, are useful to unveil the latent parameters defining a given scenario. Indeed, these algorithms have been already used to perform real-time classification of signals appearing throughout the road. Selecting the modeling of the electromagnetic response of the radar plays an important role to achieve real time constraints. Even though Helmholtz equation represents accurately the physics, the computational cost of such simulation is not affordable for real-time applications due to high radar operating frequencies, resulting into a very fine finite element mesh. On the other hand, far field approaches are not so accurate when the objects are very close due to plane wave assumption. In the first part of this work, the Geometrical Optics method is investigated in this work as a possible route to fulfill both real-time and accuracy constraints. The main hypothesis under such model is that waves are treated as straight lines constrained to optical reflection laws. Therefore, there is no need to mesh the interior of the domain. However, the accuracy of such approach is compromised when the size of the objects inside the domain are comparable to the wave lengths or in the vicinity of angular points. The second part is mainly focused on of the application of manifold learning and big data analysis into a data set of precomputed scenarios. Indeed, the identification of an unknown scenario from electromagnetic signals is purchased. Nevertheless, current research lines are devoted to give an answer to questions such as how many receptors do we need to identify unequivocally the scenario, where to locate the receptors, or which parts of the scenario have a negligible impact in the electromagnetic response.
Rubén Ibáñez Pinillo, Francisco Chinesta, Emmanuelle Abisset-Chavanne, Erik Abenius, Antonio Huerta

Chapter 13. Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks

Abstract
In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. While these planes are used for transportation they could similarly be used for remote sensing applications by adding sensors to the planes. Hyperspectral imagers are one this kind of sensor types. There is need for the efficient methods to interpret hyperspectral data to the wanted water quality parameters. In this work we survey the performance of neural networks in the prediction of water quality parameters from remotely sensed hyperspectral data in freshwater basins. The hyperspectral data consists of 36 bands in the wavelength range of 508–878 nm and the water quality parameters to be predicted are temperature, conductivity, turbidity, Secchi depth, blue-green algae, chlorophyll-a, total phosphorus, acidity and dissolved oxygen. The objective of this investigation was to study the behaviour of different types of neural networks with this kind of data. Study is a survey of the operation of neural networks on this problem, which can be used as a basis for the design of a more comprehensive study. The neural network types examined were multilayer perceptron and 1-, 2- and 3-dimensional convolutional neural networks with the effect of scaling the hyperspectral data with standard or min-max -scaler recorded. We also investigated investigated how the prediction of individual water quality parameter depends on whether the neural network model is done solely with respect to this one parameter or with several parameters predicted simultaneously with the same model. The results of the correspondence between the predicted and measured water quality parameters were presented with normalized root mean square error, Pearson correlation coefficient and coefficient of determination. The best models were obtained the 2-dimensional convolutional neural networks with standard scaling made separately for each parameter. The parameters showing good predictability were conductivity, turbidity, Secchi-depth, blue-green algae, chlorophyll-a and total phosphorus, for which the coefficient of determination was at least 0.96 (apart from Secchi-depth even 0.98).
Taina Hakala, Ilkka Pölönen, Eija Honkavaara, Roope Näsi, Teemu Hakala, Antti Lindfors

Chapter 14. Distortions in Large Aluminum Forgings: Current Situation and Simulation Challenges

Abstract
Distortions after machining of large aluminum forgings are a recurrent problem for the aeronautical industry. These deviations from design geometry are caused by the presence of residual stresses, which are developed along the manufacturing chain. To solve this problem, a series of post-machining operations called reshaping are required. Despite reshaping manages to restore the correct geometry, it is highly manual and time-consuming, therefore, there is a need at an industrial level to use numerical simulation to study reshaping. The present document describes the problem of distortion, the operations required to mitigate these geometrical defects and the challenges associated to simulate reshaping.
Ramiro Mena, Stéphane Guinard, José V. Aguado, Antonio Huerta
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