Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems / / edited by Pedro Diez, Pekka Neittaanmäki, Jacques Periaux, Tero Tuovinen, Jordi Pons-Prats

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (XIV, 249 p. 87 illus., 70 illus. in color.)
• Disciplina: 385.0724
• Soggetto topico: Transportation; Transportation engineering; Traffic engineering; Computer mathematics; Computer simulation; Aerospace engineering; Astronautics; Big data; Transportation Technology and Traffic Engineering; Computational Science and Engineering; Simulation and Modeling; Aerospace Technology and Astronautics; Big Data
• Persona (resp. second.): DiezPedro; NeittaanmäkiPekka; PeriauxJacques; TuovinenTero; Pons-PratsJordi
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Preliminary Table of Contents: Reviews and perspectives -- White paper: Computational models for the model shift: a State of the art review, by Sara Ahetze Puignau, Jordi Pons-Prats, Sergi Sauri -- Part 1. Maritime -- Shipping 4.0 – Digitalization of maritime industry, by Trond Kvamsdal -- Challenges on computational models for ship design and navigation: Ongoing projects at CIMNE, by Julio Garcia -- Fast Simulation-Assisted Shape Correction after Machining, by Ramiro Mena -- Part 2. Automotive -- Design optimization challenges and opportunities for mobility applications, by Carlo Poloni -- PGD for interactive aerodynamic optimization, by Vasilis Tsiolakis and Henry Bensler -- Parametric wave progation for radar simulation, by Rubén Ibañez -- Application of Big Data Analytics for Understanding the Complexity of Vehicle Routing Problems, by Jussi Rasku and Tommi Karkkainen -- Part 3. Logistics -- Transport management with LogiApps: Maximizing efficiency with optimization and simplicity, by Jyri Leskinen, Panu Silvasti, and Jukka Toivanen -- Optimization of fleets and their Propulsive alternatives by operating under Motorways of the Sea conditions to Improve their Environmental Results and the Feasibility of the intermodal chains, by Alba Martínez-López and Lourdes Trujillo -- Part 4. Aeronautics -- Public Health Concerns Regarding Modern Transportation, by William E. Fitzgibbon -- Aircraft Multidisciplinary Design chain Opportunities and Transport Challenges, by Adel Abbas -- Part 5. White paper: Computation and Big Data Challenges and perspectives for Transport.
• Sommario/riassunto: 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.
• Titolo autorizzato: Computation and Big Data for Transport
• ISBN: 3-030-37752-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910377819903321
• Lo trovi qui: Univ. Federico II
• Serie: Computational Methods in Applied Sciences, . 1871-3033 ; ; 54