Diez / Neittaanmäki / Periaux Computation and Big Data for Transport

Digital Innovations in Surface and Air Transport Systems
1. Auflage 2020
ISBN: 978-3-030-37752-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 54, 252 Seiten

Reihe: Computational Methods in Applied Sciences

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.

Autoren/Hrsg.

Diez, Pedro

Neittaanmäki, Pekka

Periaux, Jacques

Tuovinen, Tero

Pons-Prats, Jordi

Inhaltsverzeichnis

1;Foreword;6
2;Preface;8
3;Contents;10
4;Contributors;12
5;Part I White Paper;16
6;1 Digital Technologies for Transport and Mobility: Challenges, Trends and Perspectives;17
6.1;1.1 Introduction;18
6.2;1.2 Surface Transport;19
6.2.1;1.2.1 Current Situation and Challenges;19
6.2.2;1.2.2 Trends;20
6.2.3;1.2.3 Perspectives/Recommendations;21
6.3;1.3 Aeronautics and Aviation;22
6.3.1;1.3.1 Current Situation and Challenges;22
6.3.2;1.3.2 Challenges for Improved Aircraft Design;23
6.3.3;1.3.3 Trends;24
6.3.4;1.3.4 Perspectives;26
6.4;1.4 Cyber Security—Transport and Logistics in a Time of (cyber) Insecurity;27
6.4.1;1.4.1 Current Situation and Challenges;27
6.4.2;1.4.2 Trends;28
6.4.3;1.4.3 Perspectives/Recommendations;30
7;Part II Reviews and Perspectives;31
8;2 Cyber Security in Aviation, Maritime and Automotive;32
8.1;2.1 Introduction;33
8.2;2.2 Aviation;33
8.3;2.3 Maritime;36
8.4;2.4 Automotive;39
8.5;2.5 Conclusion;42
8.6;References;44
9;3 Operation of Transport and Logistics in a Time of (Cyber)Insecurity;46
9.1;3.1 The Operational Technology Security Problem;47
9.2;3.2 Cyber Defense;47
9.2.1;3.2.1 Standards and Controls;48
9.2.2;3.2.2 Cyber Security and Institutional Preparedness;50
9.2.3;3.2.3 Authentication and Access Control;51
9.2.4;3.2.4 Cryptography and Encryption;52
9.2.5;3.2.5 Network Security;53
9.2.6;3.2.6 Computer and Software Security;54
9.2.7;3.2.7 Cyber Intelligence;55
9.3;3.3 Managing Transport Cybersecurity;56
9.3.1;3.3.1 Getting from A to B Autonomously—A Case;56
9.3.2;3.3.2 Managed Risk, Infrastructure, and Seeking Remedy;58
9.3.3;3.3.3 Protecting Valued Information and Communications;59
9.3.4;3.3.4 The Geopolitical-Economic Driver;60
9.3.5;3.3.5 Notes on Managing Risk;60
9.4;References;61
10;4 New Data and Methods for Modelling Future Urban Travel Demand: A State of the Art Review;63
10.1;4.1 Introduction;63
10.2;4.2 Understanding Urban Mobility;65
10.2.1;4.2.1 Trends in Relevant Variables for Travel Demand Modelling;65
10.2.2;4.2.2 New Data Sources and Collection Methods;67
10.3;4.3 Urban Travel Demand Modelling Framework;69
10.3.1;4.3.1 The Classical Four-Step Models;69
10.3.2;4.3.2 Activity-Based Models;70
10.3.3;4.3.3 Agent-Based Models;71
10.3.4;4.3.4 Integrated Models and New Transport Services;72
10.4;4.4 Discussion and Possible Lines of Research;73
10.5;References;75
11;5 Maritime Transport and the Threat of Bio Invasion and the Spread of Infectious Disease;80
11.1;5.1 Introduction;81
11.2;5.2 Biological Invasion;81
11.3;5.3 Infectious Disease;82
11.4;5.4 Vector Borne Disease;82
11.5;5.5 Aedes Mosquitos;83
11.6;5.6 Modeling Considerations;83
11.7;References;84
12;Part III Computational Methods and Applications;85
13;6 Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems;86
13.1;6.1 Introduction;86
13.2;6.2 CVRP Feature Extraction;88
13.2.1;6.2.1 Role of Features in VRP;88
13.2.2;6.2.2 Data;89
13.3;6.3 The Analysis Process;92
13.4;6.4 Results;95
13.4.1;6.4.1 Step 0;95
13.4.2;6.4.2 Step 1;95
13.4.3;6.4.3 Step 2;97
13.4.4;6.4.4 Step 3;98
13.4.5;6.4.5 Summary of Results;104
13.5;6.5 Discussion;104
13.6;References;108
14;7 A New Multi-objective Solution Approach Using ModeFRONTIER and OpenTrack for Energy-Efficient Train Timetabling Problem;112
14.1;7.1 Introduction;113
14.2;7.2 Literature Review;114
14.3;7.3 ModeFRONTIER and OpenTrack;117
14.4;7.4 Case Study;119
14.5;7.5 Solution Approach;121
14.6;7.6 Application;123
14.6.1;7.6.1 ModeFRONTIER Workflow;123
14.6.2;7.6.2 Results;125
14.7;7.7 Conclusion;127
14.8;References;127
15;8 Development of New Lagrangian Computational Methods for Ice-Ship Interaction Problems: NICESHIP Project;129
15.1;8.1 Introduction;130
15.1.1;8.1.1 Navigation in the Arctic Area;130
15.1.2;8.1.2 The Design of Ships for Navigation in Ice;131
15.1.3;8.1.3 History and State of the Art in Simulation and Analysis of Ship Navigation in Ice;132
15.2;8.2 NICE-SHIP Project Scenarios;133
15.2.1;8.2.1 Scenario 1: Analysis of the Icebreaking Performance of Ships in Level Ice;134
15.2.2;8.2.2 Scenario 2: Analysis of the Performance of Ships in Brash Ice;147
15.3;8.3 Conclusions;160
15.4;References;160
16;9 Delivery Service in Congested Urban Areas;162
16.1;9.1 Introduction;163
16.2;9.2 Congested Urban Traffic Area;163
16.3;9.3 Approaches, Models, Methods;164
16.4;9.4 Estimation of Traffic Flow Assignment;165
16.5;9.5 Travel Demands in an Urban Area;168
16.6;9.6 Delivery Route Planning Under Conditions of Urban Congestions;170
16.7;9.7 Conclusion;171
16.8;References;172
17;Part IV Translational Research;173
18;10 Current CAE Trends in the Automotive Industry;174
18.1;10.1 Introduction;175
18.2;10.2 The Need for a Fully Digital Development Process;175
18.2.1;10.2.1 Safety;176
18.2.2;10.2.2 Environmental Impact;178
18.2.3;10.2.3 Comfort;179
18.2.4;10.2.4 Method Validation;180
18.3;10.3 Digital Methods in the Development Process: State-of-the-Art;180
18.4;10.4 Novel Methodologies in Computational Mechanics, Big Data and Artificial Intelligence;183
18.4.1;10.4.1 Reduced Order Modelling;183
18.4.2;10.4.2 Artificial Intelligence and Big Data;183
18.5;10.5 Towards a Clever Digital Development Process;184
18.6;References;184
19;11 Establishment of MoS in Chile: Pertinence Assessment Through an Analysis of Previous Scenarios;185
19.1;11.1 Introduction;186
19.2;11.2 Framework;186
19.3;11.3 The Model;188
19.3.1;11.3.1 Objective Functions, Restrictions and the Resolution;189
19.4;11.4 The Resolution of the Model;190
19.5;11.5 The Results for Preliminary Scenarios;191
19.6;11.6 Conclusions;193
19.7;References;196
20;12 Radars in Transport Applications;198
20.1;12.1 Introduction;199
20.2;12.2 State of Art Manifold Learning;200
20.2.1;12.2.1 Principal Component Analysis;200
20.2.2;12.2.2 Locally Linear Embedding;203
20.3;12.3 Geometrical Optics;204
20.3.1;12.3.1 Convergence Results;206
20.3.2;12.3.2 Black-Boxing the Scenario;209
20.4;12.4 The Scenario Manifold;211
20.5;12.5 Conclusions;213
20.6;References;214
21;13 Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks;215
21.1;13.1 Introduction;216
21.2;13.2 Materials and Methods;218
21.2.1;13.2.1 Research Data;218
21.2.2;13.2.2 Neural Networks;220
21.2.3;13.2.3 Network Types;223
21.2.4;13.2.4 Scaling Methods;224
21.2.5;13.2.5 Configuration of the Modelled Water Quality Parameters;225
21.2.6;13.2.6 Metrics;227
21.2.7;13.2.7 Learning and Adjusting the Models;227
21.3;13.3 Results;230
21.4;13.4 Discussion;236
21.5;13.5 Conclusion;238
21.6;References;239
22;14 Distortions in Large Aluminum Forgings: Current Situation and Simulation Challenges;241
22.1;14.1 Introduction;241
22.2;14.2 Distortion in the Aeronautical Industry: An Open Problem;243
22.3;14.3 Residual Stresses, Distortion and Reshaping;244
22.4;14.4 Post-machining Distortion Mitigation Techniques;246
22.5;14.5 Reshaping Simulation: Challenges and Perspectives;247
22.6;14.6 Conclusions;250
22.7;References;251