C2 Toimitettu kirja, kokoomateos, konferenssijulkaisu tai lehden erikoisnumero
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-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Diez, Pedro; Neittaanmäki, Pekka; Periaux, Jacques; Tuovinen, Tero; Pons-Prats, Jordi
ISBN: 978-3-030-37751-9
eISBN: 978-3-030-37752-6
Lehti tai sarja: Computational Methods in Applied Sciences
ISSN: 1871-3033
Julkaisuvuosi: 2020
Sarjan numero: 54
Kirjan kokonaissivumäärä: 250
Kustantaja: Springer
Kustannuspaikka: Cham
Julkaisumaa: Sveitsi
Julkaisun kieli: englanti
DOI: https://doi.org/10.1007/978-3-030-37752-6
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Tiivistelmä
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.
YSO-asiasanat: big data; mallintaminen; simulointi; optimointi; hallintajärjestelmät; logistiikka; kuljetus; kuljetustekniikka
Vapaat asiasanat: 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
Liittyvät organisaatiot
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2020
JUFO-taso: 0
Tähän julkaisuun sisältyvät artikkelit, joissa JYU:n tekijöitä:
- 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
