Dr. Valerio De Martinis
Position at the ZHAW
Expertise and research interests
Public transport design and operation.
Energy saving train operation.
Freight train operation.
Travel demand modeling.
Simulation and optimization of transport systems.
Data driven models in railway operation (Determination of resistance parameters,
Energy efficient driving strategies, Train localization with filtering techniques).
Dipl. eng. in civil engineering with specialization in transportation.
Ph.D. in transport systems and road infrastructures.
2009-2012. Simulation & Modeling Engineer at the regional competence center for transport systems (Campania Region, Naples, ITALY).
2012-14. Post-doc in Rail Simulation, Signalling Systems and Energy Efficiency (Ansaldo STS - University of Napoli Federico II).
2014-17. Post Doc in Freight Trains Operation, Rail Traffic Management, and Energy Efficiency (Institute for Transport planning and systems - ETH Zurich).
2017-21. Research Associate and Project Leader Big Data in Optimal Railway Operation and Infrastructure Use (SCCER Mobility, Institute for Transport planning and systems - ETH Zurich).
Membership of networks
- IAROR - International Association of Railway Operations Research.
- TRB Transportation Research Board (Railroad Transportation committee)
- IEEE ITSS Intelligent Transportation Systems Society
- Untersuchung der Lokführerproduktivität und -arbeitszufriedenheit / Project leader / Project ongoing
de Doncker, Rik W.; Nießen, Nils; Friesen, Nadine; Schindler, Christian, eds.,
IRSA 2021 : Tagungsband.
3rd International Railway Symposium Aachen (IRSA), Aachen, Germany, 21-23 November 2021.
RWTH Aachen University.
Available from: https://doi.org/10.18154/RWTH-2022-01700
Publications before appointment at the 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.