Dr. D.T. (Dimitrios) Tselentis

Profile

Dr. Dimitrios Tselentis is a Post-doctoral Researcher at the Safety and Security Science Section of the Faculty of Technology, Policy, and Management. He holds a Diploma (2012) and a Doctor of Philosophy (2018) in Civil Engineering from the National Technical University of Athens, majored in Transportation Engineering. His Ph.D. research was on driving behavior assessment using data science techniques.

Research

Dimitrios primary research fields are Transport Safety, Driving Behavior, Transportation data analysis, Machine Learning and Artificial Intelligence applications, Statistics and Econometrics, Traffic Simulation and Forecasting and Energy Efficient Vehicles. He has more than nine years of experience, working with research and industry projects related to data and analytics and has published several scientific papers in peer-reviewed journals and international conference proceedings. He serves as a reviewer in nine scientific journals.

Tselentis, D. I., Yannis, G., & Vlahogianni, E. I. (2017). “Innovative motor insurance schemes: a review of current practices and emerging challenges.” Accident Analysis & Prevention, 98, 139-148.

Tselentis, D. I., Vlahogianni, E. I., & Karlaftis, M. G. (2014). “Improving short-term traffic forecasts: to combine models or not to combine?”. IET Intelligent Transport Systems, 9(2), 193-201.

Papadimitriou, E., Argyropoulou, A., Tselentis, D. I., & Yannis, G. (2019). “Analysis of driver behaviour through smartphone data: The case of mobile phone use while driving.” Safety Science.

Tselentis I. D., Theofilatos A., George Y., Konstantinopoulos E. (2018). “Public opinion on usage-based motor insurance schemes: A stated preference approach”, Travel Behaviour and Society, Volume 11, April 2018, Pages 111–118.

Ziakopoulos A., Tselentis I. D., Kontaxi A., George Y. (2020) “A critical overview of driver recording tools”. Journal of Safety Research, Volume 72, Pages 203-212.

Tselentis, D. I., Vlahogianni, E. I., & Yannis, G. (2019). “Driving safety efficiency benchmarking using smartphone data”. Transportation Research Part C: Emerging Technologies, 109C, 343-357.

Anna-Maria Stavrakaki, Tselentis I. D., Barmpounakis Ε. Ν., Vlahogianni I. E., George Y. (2020) “Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior”. Sensors; 20(9):2600

Tselentis, D. I., Vlahogianni, E. I., & Yannis, G. (2021). “Temporal analysis of driving efficiency using smartphone data.” Accident Analysis & Prevention, 154, 106081. 

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RHAPSODY - Recognition of HumAn PatternS of Optimal Driving for safetY of conventional and autonomous vehicles

Objective

Driving behaviour analytics is an emerging field with new potential for addressing the human factors that are persistently causing a huge burden of traffic injuries. However, there is need for new insights regarding driving profiles and patterns identification and a robust relevant methodology is lacking. The objective of RHAPSODY is to provide evidence for a shift of focus in driving behaviour models, targeting to identify not only the unsafe but also the optimal driving, through the analysis of the dynamic evolution of driving behaviour on both macro- and microscopic levels. Machine learning (ML) and artificial intelligence (AI) techniques will be applied on existing European naturalistic driving data to identify different driver profiles and driving patterns, their rapid changes under different conditions and their variability over individual drivers and populations. Ultimately, RHAPSODY will recognize the benchmarks of optimal driving and investigate the conditions under which drivers may demonstrate best performance. These can be applied for the improvement of safety of both conventional drivers and human-mimic autonomous vehicles (AVs).

Dimitrios Tselentis

Post-doctoral Researcher

Department:
Values, Technology and Innovation

Section:
Safety and Security Science

Research interests:
Responsible risk management
Transport Safety
Driver’s Behavior and Road Risk
Transportation Statistics and Econometrics
Machine Learning and Artificial Intelligence applications
Autonomous Vehicles

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