C2 Edited work
Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems (2020)
Diez, P., Neittaanmäki, P., Periaux, J., Tuovinen, T., & Pons-Prats, J. (Eds.). (2020). Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems. Springer. Computational Methods in Applied Sciences, 54. https://doi.org/10.1007/978-3-030-37752-6
JYU authors or editors
Publication details
All authors or editors: Diez, Pedro; Neittaanmäki, Pekka; Periaux, Jacques; Tuovinen, Tero; Pons-Prats, Jordi
ISBN: 978-3-030-37751-9
eISBN: 978-3-030-37752-6
Journal or series: Computational Methods in Applied Sciences
ISSN: 1871-3033
Publication year: 2020
Number in series: 54
Number of pages in the book: 250
Publisher: Springer
Place of Publication: Cham
Publication country: Switzerland
Publication language: English
DOI: https://doi.org/10.1007/978-3-030-37752-6
Publication open access: Not open
Publication channel open access:
Abstract
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.
Keywords: big data; modelling (representation); simulation; optimisation; control systems; logistics; transport; transportation engineering
Free keywords: Big Data in aeronautics; Big Data in automotive; Big Data for logistics; modeling and simulation; optimization and control; AI assisted optimization; ECCOMAS; ship design and navigation; Big Data challenges; transport challenges
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 0
This publication includes articles with JYU authors:
- Kärkkäinen, T., & Rasku, J. (2020). Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems. In P. Diez, P. Neittaanmäki, J. Periaux, T. Tuovinen, & J. Pons-Prats (Eds.), Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems (pp. 77-102). Springer. Computational Methods in Applied Sciences, 54. https://doi.org/10.1007/978-3-030-37752-6_6
- Lehto, M. (2020). Cyber Security in Aviation, Maritime and Automotive. In P. Diez, P. Neittaanmäki, J. Periaux, T. Tuovinen, & J. Pons-Prats (Eds.), Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems (pp. 19-32). Springer. Computational Methods in Applied Sciences, 54. https://doi.org/10.1007/978-3-030-37752-6_2
- Diez, P., Periaux, J., Tuovinen, T., Räisänen, J., Lehto, M., Abbas, A., Poloni, C., Kvamsdal, T., & Bronk, C. (2020). Digital Technologies for Transport and Mobility : Challenges, Trends and Perspectives. In P. Diez, P. Neittaanmäki, J. Periaux, T. Tuovinen, & J. Pons-Prats (Eds.), Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems (pp. 3-16). Springer. Computational Methods in Applied Sciences, 54. https://doi.org/10.1007/978-3-030-37752-6_1
- Hakala, T., Pölönen, I., Honkavaara, E., Näsi, R., Hakala, T., & Lindfors, A. (2020). Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks. In P. Diez, P. Neittaanmäki, J. Periaux, T. Tuovinen, & J. Pons-Prats (Eds.), Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems (pp. 213-238). Springer. Computational Methods in Applied Sciences, 54. https://doi.org/10.1007/978-3-030-37752-6_13
