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Dr. Valerio De Martinis

Dr. Valerio De Martinis

Dr. Valerio De Martinis

ZHAW School of Engineering
Forschungsschwerpunkt Transport and Traffic Engineering

+41 (0) 58 934 46 28
valerio.demartinis@zhaw.ch

Persönliches Profil

Tätigkeit an der ZHAW als

Wissenschaftlicher Mitarbeiter

Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse

Design und Betrieb von öffentlichen Verkehrsmitteln.

Energiesparender Zugbetrieb.

Güterzugbetrieb.

Modellierung der Reisenachfrage.

Simulation und Optimierung von Transportsystemen.

Fahrplan-Design.

Data-Driven Modelle im Bahnbetrieb (Bestimmung von Widerstandsparametern, Energieeffiziente Fahrstrategien, Zuglokalisierung mit Filtertechniken).



Aus- und Fortbildung

Dipl.-Ing. in Bauingenieurwesen mit Schwerpunkt Verkehrswesen.
Ph.D. in Transportsystemen und Strasseninfrastrukturen.

Beruflicher Werdegang

2009-2012. Simulations- und Modellierungsingenieur im regionalen Kompetenzzentrum für Verkehrssysteme (Region Kampanien, Neapel, Italien).

2012-14. Postdoc im Bereich Eisenbahnsimulation, Signalisierung und Energieeffizienz (Ansaldo STS - Universität von Neapel Federico II. Italien).

2014-17. Post-Doc im Bereich Güterzugbetrieb, Schienenverkehr und Energieeffizienz. (Institut für Verkehrsplanung und Transportsysteme - ETH Zürich).

2017-21. Wissenschaftlicher Mitarbeiter und Projektleiter Big Data für optimalen Bahnbetrieb und Infrastrukturnutzung (SCCER Mobility, Institut für Verkehrsplanung und Transportsysteme - ETH Zürich).

Mitglied in Netzwerken

Projekte

Publikationen

Publikationen vor Tätigkeit an der ZHAW

Relevante Publikationen in den letzten 5 Jahren (vollständige Liste auf Anfrage):

1. G. Sessa, V. De Martinis, A. Bomhauer-Beins, U.A. Weidmann, F. Corman, 2020. A hybrid stochastic approach for offline train trajectory reconstruction. Public Transport, Springer Verlag, DOI: 10.1007/s12469-020-00230-4.
2. P.G. Sessa, V. De Martinis, F. Corman, 2019. Filtering approaches for online train motion estimation with onboard power measurements. Computer-Aided Civil and Infrastructure Engineering, Wiley & Co, pp. 1-15, DOI: 10.1111/mice.12514, Impact Factor: 6.208.
3. V. De Martinis, A. Toletti, F. Corman, U. Weidmann, A. Nash, 2019. Feedforward Tactical Optimization For energy efficient operation of Freight Trains: The Swiss case. Transportation Research Record (TRR), Journal of the Transportation Research Board, pp. 1-11, DOI:10.1177/0361198118776508, Impact Factor 0.522.
4. V. De Martinis, F. Corman, 2019. Online microscopic calibration of train motion models: towards the era of customized control solutions. Proceedings of Rail Norrkoping 2019 International Conference on Railway Operations Modelling and Analysis (ICROMA)
5. V. De Martinis, F. Corman, 2018. Data-driven perspectives for energy efficient railway systems: current practices and future opportunities. Transportation Research part C, Emerging Technologies, volume 95, pp. 679-697, DOI: 10.1016/j.trc.2018.08.008, Impact Factor 4.334.
6. P.G. Sessa, V. De Martinis, F. Corman, 2018. A Hybrid Dynamic-Kinematic EKF for Train Trajectory Estimation. 21st IEEE International Conference on Intelligent Transportation Systems, Maui, US. Selected for Best Paper Award
7. N. Leng, V. De Martinis, F. Corman, 2018. Agent-based simulation approach for disruption management in rail schedule. Conference on Advanced Systems in Public Transport (CASPT) 2018, Brisbane, Australia
8. P.G. Sessa, V. De Martinis, A. Bomhauer-Beins, F. Corman, U.A. Weidmann, 2018. Hybrid stochastic approaches for train trajectory reconstruction. TransitData 2018, Brisbane, Australia.
9. F. Corman, X. Luan, V. De Martinis, 2018. Model-based and data-driven approaches for railway traffic management and energy efficient operations. AIRO International Conference on Optimization and Decision Science
10. V. De Martinis, F. Corman, A. Toletti, U. Weidmann, 2017. How to make freight trains more energy-efficient during operation? Swiss Transport Research Conference STRC 2017 (Monte Verità, Ascona – Switzerland).
11. V. De Martinis, A. Toletti, U. Weidmann, A. Nash, 2017. Modeling real time CBTC operation in mixed traffic networks: a simulation-based approach. 96th Annual Meeting of the Transportation Research Board, Washington, D.C.
12. A. Toletti, V. De Martinis, F. Corman, U. Weidmann, 2017. Boosting the Resource Conflict Graph approach to train rescheduling with column-and-row generation. Proceedings of AIRO/ Optimization and Decision Sciences Conference, Sorrento, Italy
13. A. Toletti, V. De Martinis, U. Weidmann, 2017. Enhancing energy efficiency in railway operation through RCG-based rescheduling. Proceedings of 17th International Conference EEEIC-IEEE 2017, Milan, Italy
14. A. Toletti, V. De Martinis, U. Weidmann, 2017. Multicriteria train rescheduling using efficient adaptive epsilon-constraint method. Proceedings of Rail Lille 2017 - 7th International Conference on Railway Operations Modelling and Analysis, Lille, France.
15. A. Toletti, V. De Martinis, U. Weidmann, 2016. Multi objective solutions with RCG models for the rescheduling of mixed rail traffic. Proceedings of IEEE 19th International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brasil.
16. V. De Martinis, U. Weidmann, 2016. Energy efficiency in railway operation: the case of freight trains. 9th Triennial Symposium on Transportation Analysis (TRISTAN), Aruba
17. V. De Martinis, U. Weidmann, 2016. Improving energy efficiency for freight trains during operation: the use of simulation. Proceedings of 16th International Conference EEEIC-IEEE 2016, Florence, Italy.
18. V. De Martinis, U. Weidmann, 2016. The use of on-board monitoring data for the evaluation of potential energy savings: an application to freight trains. Swiss Transport Research Conference STRC 2016, Ascona – Switzerland.
19. V. De Martinis, U. Weidmann, A. Nash, 2016. An evaluation of freight train energy saving potential using onboard monitoring data. 95th Annual Meeting of the Transportation Research Board, Washington, D.C., January 2016
20. A. Toletti, V. De Martinis, U. Weidmann, A. Nash, 2016. An enhanced RCG rescheduling model for freight rail traffic: introducing the energy saving. 95th Annual Meeting of the Transportation Research Board, Washington, D.C.
21. V. De Martinis, U. Weidmann, 2015.Definition of energy-efficient speed profiles within rail traffic by means of supply design models. Research in Transportation Economics, volume 54, pp. 41-50, DOI: 10.1016/j.retrec.2015.10.024, Impact Factor 1.798.
22. V. De Martinis, F. Pagliara, A. Wilson, 2014. The evolution and planning of hierarchical transport networks. Environment and Planning B: Planning and Design, volume 41, Issue 2, pp.192-210, DOI: 10.1068/b39102, Impact Factor 2.807.
23. A. Toletti, V. De Martinis, U. Weidmann, 2015. What about Train Length and Energy Efficiency of Freight Trains in Rescheduling Models? Transportation Research Procedia, volume 10, DOI: 10.1016/j.trpro.2015.09.012.
24. M. Gallo, F. Simonelli, G. De Luca, V. De Martinis, 2015. Estimating the effects of energy-efficient driving profiles on railway consumption. Proceedings of IEEE EEEIC 2015 ¯¯ 15th International Conference on Environment and Electrical Engineering, pp. 813-818, ISBN: 978-1-4799-7992-9.