Spatiotemporal Urban Traffic Modelling

Period: 01.01.2017
- 01.01.2020

Supervisors:

Professor 

Dr. sc. ing., Ek. zin. kand.
Dmitry Pavlyuk
Engineering Faculty
Dmitry Pavlyuk

Academic degree: doctoral degree in Economics (2005) and Engineering (2015).

Current position at TSI: associate professor (since 2019), pro-dean of the Engineering Faculty (since 2021), researcher and head of Data Analytics and Artificial Intelligence research cluster at Transport and Telecommunication Institute, TSI (since 2020).

Experience: academic career from an assistant position at Saratov State University, Russia, in 2002 to an associate professor at TSI. Chair of TSI Mathematical Methods and Modelling Department (2014-2020).

Academic experience: Nineteen years of teaching experience in higher education, development and presenting study courses: Probability Theory and Mathematical Statistics; Econometrics; Operation Research; Optimisation Theory; Discrete Mathematics; Introduction to Stochastic Processes; Data Analysis and Business Forecasting. Member of TSI final attestation commissions on MSc in Computer Science and Management of Information Systems. Dr Dmitry Pavlyuk has supervised 15 successfully promoted MSc and 10 BSc theses. Teaching mobility to Higher School of Transport, Bulgaria (2013), Vilnius Gediminas Technical University, Lithuania (2017).

Research experience: Author of more than 50 publications, including 22 journal articles in Sensors (ISSN: 1424-8220), European Transport Research Review (ISSN: 1867-0717), Algorithms (ISSN: 1999-4893), Transport and Telecommunication (ISSN: 1407-6179), Research in Transportation Economics (ISSN: 0739-8859), Transport (ISSN: 1648-4142) and 12 book chapters in Springer’s Lecture Notes in Networks and Advances in Intelligent Systems and Computing book series. Postdoctoral researcher at TSI (2017-2020) for spatiotemporal urban traffic modelling; presenter at more than 30 international conferences in Latvia, Austria, Cyprus, France, Greece, Germany, Poland, Russia, Spain. The reviewer at many top scientific journals, including Transportation Research, Parts A, C, E, Transport Reviews, IEEE Access, Transportmetrica B. Guest editor at Information (ISSN: 2078-2489); member of programme committees of Reliability and Statistics in Transportation and Communication (RelStat-2017-2020), Latvia, and Computer Modelling in Decision Making (CMDM 2017-2020), Russia. Member of PhD promotion committee, expert of the Latvian Council of Science (since 2017) in Civil Engineering.

Participation in research projects: Dr Pavlyuk has successfully completed the postdoc research project 1.1.1.2/VIAA/1/16/112 “Spatiotemporal urban traffic modelling using big data” (2017-2020); researcher in “Enhancing excellence and innovation capacity in sustainable transport interchanges” (Horizon 2020 Nr. 692426, 2016-2018), “Learning with ICT use” (Erasmus+, 2014-2017) and several commercial research projects.

Research Interests: Research interests include: spatial and spatiotemporal statistical modelling, multivariate time series analysis, econometric models and statistical estimators, stochastic frontier models and machine learning in applied domains of banking, airport industry, urban road traffic, public transport, venture markets, and environmental management.

Project Type: Applied

Main Challenge:

Modern urban traffic forecasting models utilize information about a spatiotemporal structure of traffic flows that includes links between road segments at different time periods and may change over time.

The main challenge of the project is to learn these relationships from real-world data and utilize them for better traffic forecasting and management.

Funding: ERDF, 1.1.1.2/VIAA/1/16/112

DA&AI tools:

  • Multivariate (spatiotemporal) statistical models
  • Time series forecasting
  • Artificial neural networks
  • Ensemble Learning
  • Transfer learning

Publications:

  • Pavlyuk, D., 2020. Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting. Algorithms 13, 39. https://doi.org/10.3390/a13020039
  • Pavlyuk, D., 2020. Towards ensemble learning of traffic flows’ spatiotemporal structure. Transportation Research Procedia 47, 361–368. https://doi.org/10.1016/j.trpro.2020.03.110
  • Pavlyuk, D., 2019. Spatiotemporal Traffic Forecasting as a Video Prediction Problem, in: 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), IEEE, Cracow, Poland, pp. 1–7. https://doi.org/10.1109/MTITS.2019.8883353
  • Pavlyuk, D., 2019. Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review. European Transport Research Review 11. https://doi.org/10.1186/s12544-019-0345-9
  • Pavlyuk, D., 2018. Spatiotemporal Big Data Challenges for Traffic Flow Analysis, in: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (Eds.), Reliability and Statistics in Transportation and Communication. Springer International Publishing, Cham, pp. 232–240. https://doi.org/10.1007/978-3-319-74454-4_22
  • Pavlyuk, D., 2018. On Application of Regime-Switching Models for Short-Term Traffic Flow Forecasting, in: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (Eds.), Proceedings of the Twelfth International Conference on Dependability and Complex Systems DepCoS-RELCOMEX, Brunow, Poland, pp. 340–349. https://doi.org/10.1007/978-3-319-59415-6_33
  • Pavlyuk, D., 2017. Study of a Spatial Structure of Urban Traffic Flows Using a Regime-Switching Vector Autoregressive Model, in: Althonayan, A., Belkina, T.A., Mkhitaryan, V.S., Pavluk, D., Sidorov, S.P. (Eds.), Proceedings of the Second Workshop on Computer Modelling in Decision Making (CMDM), CEUR Workshop Proceedings, Saratov, Russia, pp. 151–160.
  • Pavlyuk, D., 2017. Short-term Traffic Forecasting Using Multivariate Autoregressive Models. Procedia Engineering 178, 57–66. https://doi.org/10.1016/j.proeng.2017.01.062

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