Rafal Kucharski Lab

I research complex social systems: urban mobility. Congested, urban multimodal networks used by millions of agents to reach their destinations and leaving huge sets of mobility traces ready to be applied for modelling, optimization, understanding and control.

Currently, as an Assistant Professor in the Faculty of Mathematics and Computer Science of Jagiellonian University in Krakow (Poland) I am part of the Group of Machine Learning Research GMUM. I run the NCN Opus Grant on Shared Mobility in the pandemic times and build a team to research complex social systems of urban mobility within the DigiWorld scheme. We are looking for two prospective PhD students willing to research in Network Science and Behavioural Modelling in Kraków, Poland, see details in the ‘jobs’ tab.

Lately, I worked (2019-2021) with Oded Cats at TU Delft in his ERC Starting Grant Critical MaaS. I model two-sided mobility platforms, specifcally focusing on ride-pooling (making people share their Uber with co-travellers). I did PhD with Guido Gentile in non-equilibrium dynamic traffic assignment. In the interdisciplinary field of urban mobility I did research 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.

Outside of academia I was:

Data Scientist at NorthGravity developing AWS-based protfolio or real-time production ready ML solutions
R&D Developer at PTV SISTeMA implementing algorithms into PTV Optima real-time traffic forecasting models.
Transport Modeller at Department of Transportation Systems using travel-diaries to estimate demand models and create strategic planning tools, e.g. for Warsaw and Kraków

drawing

news

Dec 1, 2021	I presented our virus spreading model from SciRep at the complex networks conference in Madrid
Nov 25, 2021	Our paper on game-theoretical ride-pooling and new formulations of Equilibria got accepted to European Journal of Operational Research (quite renowned in the OR field). With Oded Cats, Javier Alonso-Mora and Andres Fielbaum
Nov 17, 2021	We grow - Farnoud will join as a PhD student to dig deeper into learning and adaptation in MaaSSim. Welcome!
Nov 5, 2021	On the weekly seminar of GMUM we kicked-off the project to predict trajectories with GNN and RL techniques. With Lukasz Brzozowski, Jacek Tabor, Przemek Spurek and Lukasz Kucinski we will try to predict city-bike trajectories in Krakow.
Oct 12, 2021	I submitted Marie Sklodowska Curie grant proposal. Quite exciting on the intersection of ML and transportation - figners crossed.
Oct 10, 2021	I am giving a talk at MIMUW Seminar on urban mobility, I will introduce mobility problems and how the ML/AI can be applied to solve them.
Sep 30, 2021	new member of the team Michal Bujak joined us to research on topologies of ride-pooling networks. Welcome
Jun 16, 2021	Today the MT-ITS conference started - see our presentation with Oded Cats about MaaSSim. Great Conference, with the previous edition hosted in Krakow and chaired by me in 2019.
Jun 16, 2021	Two conferences at the same time…. At INSTR in the (virtual) Stockholm we present our work lately published in Transportmetrica:A: If you are late, everyone is late: late passenger arrival and ride-pooling systems’ performance - see our Today the MT-ITS conference started - see our presentation with Oded Cats, Andres Fielbaum and Javier Alonso-Mora.
May 20, 2021	I gave a seminar on Urban Mobility at Machine Learning Group at Jagiellonian University GMUM - slides
May 7, 2021	Can ride-pooling be fair? A game-theory approach for forming groups of co-travellers and splitting the costs now at arXiv with Andres Fielbaum, Oded Cats and Javier Alonso-Mora
Apr 29, 2021	We confirmed details of our job opening for PhD students at Jagiellonian University. See details on ‘open positions’ tab - deadline 8th June.

selected publications

Modelling virus spreading in ride-pooling networks

Kucharski, Rafał, Cats, Oded, and Sienkiewicz, Julian

Scientific Reports 2021
Exact matching of attractive shared rides (ExMAS) for system-wide strategic evaluations

Kucharski, Rafał, and Cats, Oded

Transportation Research Part B: Methodological 2020

Abs

The premise of ride-sharing is that service providers can offer a discount, so that travellers are compensated for prolonged travel times and induced discomfort, while still increasing their revenues. While recently proposed real-time solutions support online operations, algorithms to perform strategic system-wide evaluations are crucially needed. We propose an exact, replicable and demand-, rather than supply-driven algorithm for matching trips into shared rides. We leverage on delimiting our search for attractive shared rides only, which, coupled with a directed shareability multi-graph representation and efficient graph searches with predetermined node sequence, narrows the (otherwise exploding) search-space effectively enough to derive an exact solution. The proposed utility-based formulation paves the way for model integration in travel demand models, allowing for a cross-scenario sensitivity analysis, including pricing strategies and regulation policies. We apply the proposed algorithm in a series of experiments for the case of Amsterdam, where we perform a system-wide analysis of the ride-sharing performance in terms of both algorithm computations of shareability under alternative demand, network and service settings as well as behavioural parameters. In the case of Amsterdam, 3000 travellers offered a 30% discount form 1900 rides achieving an average occupancy of 1.67 and yielding a 30% vehicle-hours reduction at the cost of halving service provider revenues and a 17% increase in passenger-hours. Benchmarking against time-window constrained approaches reveals that our algorithm reduces the search-space more effectively, while yielding solutions that are substantially more attractive for travellers.
Simulation of rerouting phenomena in Dynamic Traffic Assignment with the Information Comply Model

Kucharski, Rafał, and Gentile, Guido

Transportation Research Part B: Methodological 2019

Abs

We present the Information Comply Model (ICM) which extends the framework for macroscopic within-day DTA proposed by Gentile (2016) to represent the rerouting of drivers wrt a single traffic event. Rerouting is reproduced as a two-stage process: first, drivers become aware about the event and its consequences on traffic; second, drivers may decide to change path. At each arc, unaware drivers have a probability of being informed by multiple ATIS sources (radio, VMS, mobile apps), which depends not only on devise penetration rates, but also on users space and time coordinates wrt the position and interval of the event. At each node, aware drivers, who are somehow reluctant to change, may finally modify their path based on a random rerouting utility, which is composed of expected gains and avoided losses. ICM is thus capable of representing the evolution of rerouting phenomena in time and space when the information about a traffic event and its consequences on congestion spread among drivers and onto the network. This way, ICM extends the concept of dynamic user equilibrium to a case of imperfect information related to availability and awareness rather than to individual perception, as well as to a case of bounded rationality with prudent drivers. Besides the model architecture and specification, this paper provides a workable methodology which can be applied both off-line for transport planning and in real-time for traffic management on large size networks.