Rafal Kucharski Lab


We study how people travel. We want to understand their travel demands and behaviour better. We want to see how they use novel mobility services (like ride-pooling or mobility platforms) and how we can design them to better fit the needs of: users, suppliers and sustainable cities. We want to forecast the future of urban mobility with connected autonomous vehicles. We use big empirical datasets, real-world networks, old and new algorithms and models. We simulate, model, analyze, solve problems, propose algorithms to better understand how cities of the future will work. See our works here. The group is led by Rafał Kucharski, associate professor at Group of Machine Learning Research, Faculty of Mathematics and Computer Science at Jagiellonian University in Kraków, Poland. Former PostDoc at TU Delft (prof. Oded Cats), PhD of prof. Guido Gentile (La Sapienza) and alumni of prof. Andrzej Szarata (Cracow University of Technology). In our research we do stuff which can be classified as:

  • model estimation, optimization, system control, network design;
  • agent-based simulation, game-theory, network science, stochastic simulation, epidemic modelling;
  • machine learning, spatial analysis, big data analysis, pattern recognition, unsupervised learning;
  • behavioural modelling, economic discrete choice models, policy, sustainability.

Currently there is ten of us, working in three different projects, at the modern campus of one of the oldest universities in Europe (est. 1364), Jagiellonian University in Kraków, Poland. We have three major projects:

  • ERC Starting Grant COeXISTENCE, where we simulate future of cities shared by humans and autonomous vehicles. We use reinforcement learning to optimize joint actions of collaborative machines (cars) and see how it affects the well studied complex social system of urban traffic - will it remain in the Nash Equilibrium? We do not think so, but that’s what we want to demonstrate - stay tuned.
  • Horizon Europe SUM project - where we apply our in-house ride-pooling algorithms to see the potential of on-demand transit in urban areas of Jerusalem and Kraków.
  • NCN Opus - where we look at the future of ride-pooling and platform services in post-pandemic world.

Feel free to reach to us for a joint seminar, collaboration or vacancies.

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selected publications

  1. JoTG
    Spatiotemporal variability of ride-pooling potential – Half a year New York City experiment
    Shulika, Olha, Bujak, Michal, Ghasemi, Farnoud, and Kucharski, Rafal
    Journal of Transport Geography 2024
  2. PLOS
    Simulating two-sided mobility platforms with MaaSSim
    Kucharski, Rafał, and Cats, Oded
    PLoS ONE 2022
  3. CIiT
    Using city-bike stopovers to reveal spatial patterns of urban attractiveness
    Banet, Krystian, Naumov, Vitalii, and Kucharski, Rafał
    Current Issues in Tourism 2022
  4. SciRep
    Modelling virus spreading in ride-pooling networks
    Kucharski, Rafał, Cats, Oded, and Sienkiewicz, Julian
    Scientific Reports 2021
  5. TR:B
    Exact matching of attractive shared rides (ExMAS) for system-wide strategic evaluations
    Kucharski, Rafał, and Cats, Oded
    Transportation Research Part B: Methodological 2020
  6. TR:B
    Simulation of rerouting phenomena in Dynamic Traffic Assignment with the Information Comply Model
    Kucharski, Rafał, and Gentile, Guido
    Transportation Research Part B: Methodological 2019
  7. EJOR
    How to split the costs and charge the travellers sharing a ride? Aligning system’s optimum with users’ equilibrium
    Fielbaum, Andres, Kucharski, Rafał, Cats, Oded, and Alonso-Mora, Javier
    European Journal of Operational Research 2021