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. Full CV

Currently, I run the ERC Starting Grant COeXISTENCE where I bridge between ML and transportation - we simulate how intelligent machines compete with humans for limited urban resources (space) and what can we expect. See details here I am Assistant Professor in the Faculty of Mathematics and Computer Science of Jagiellonian University in Krakow (Poland) with the Group of Machine Learning Research GMUM. I run the NCN Opus Grant on Shared Mobility in the pandemic times, ERC StG and Horizon Europe projects.

Before, I worked (2019-2021) with prof. Oded Cats at TU Delft in his ERC Starting Grant Critical MaaS. I modelled two-sided mobility platforms, specifcally focusing on ride-pooling (ExMAS) and agent-based simulator for Uber-like systems (MaaSSim). I did PhD at Cracow University of Technology with an excellent goup of prof. Andrzej Szarata and working closely with Guido Gentile from La Sapienza on 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.

drawing drawing drawing drawing

Teaching materials

I run the seminar on Complex Social Systems (transport is one of them) at Jagiellonian University - materials and papers are on my github

news

Apr 3, 2024	🗞 Complex social systems simulations – human behavior modeling, lecture on Friday at Auditorium Maximum I will be talking about how challenging is introducing the human factor into models and simulations considering humans have their unpredictability and free will what makes simulations more complex. The main question is how do we incorporate these aspects into modeling. Together with the audience I will go on a journey through various examples, from road traffic to pedestrian traffic simulations, presenting how human decisions can shape social phenomena. Finally, I will show how to use artificial intelligence in simulations where people compete for limited resources with intelligent machines. See you there! Presentation
Mar 13, 2024	Young blood at the Faculty of Mathematics and Computer Science during the 16th edition of the Faculty Day! So many inspiring lectures, fruitful discussions and workshops including mine conducted together with Zoltán Varga. Complex social systems simulation at the example of road traffic occured to be very vivid theme for all participants. Such brainstorming atmosphere urges us for further action. I am very proud I could be the part of this initiative. Let me take you there
Feb 28, 2024	Great honour to meet with the Jagiellonian University Representatives and my colleagues from other faculties, who also run ERC grants. I talked about the progress in my COeXISTENCE grant, future plans and challenges. Check out more here
Dec 12, 2023	Such a wonderful evening at #Krakow’s Kazimierz. Not only a Christmas Party, but also the first time when the complete team of #ERC #COeXISTENCE had a chance to meet and integrate with themselves as well as with ‘older’ friends. 👐 Ten brilliant minds, working now at Jagiellonian University to contribute towards future urban mobility. Great pride of you, wish all the best in the coming year!
Dec 11, 2023	🗞 When ride-pooling is attractive/efficient for all the parties involved (passengers, platforms/providers, city/environment/sustainability)? We examined half a year (1.5 million trip sample) from NYC, run 9 000 experiments with our 𝐄𝐱𝐌𝐀𝐒 and report 6 KPIs to move beyond the well-known 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐦𝐚𝐬𝐬 and better understand ride-pooling potential. See our results in 𝐉𝐨𝐮𝐫𝐧𝐚𝐥 𝐨𝐟 𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 𝐆𝐞𝐨𝐠𝐫𝐚𝐩𝐡𝐲: https://lnkd.in/dW5hBvhC or: https://lnkd.in/dGWVbjGN with Olha Shulika Michał Bujak and Farnoud Ghasemi
Sep 9, 2023	Today I gave a keynote speech at the TSTP Conference - I gave an overview of future equilibira with CAVs - slides

selected publications

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

Abs HTML

Ride-pooling systems, despite being an appealing urban mobility mode, still struggle to gain momentum. While we know the significance of critical mass in reaching system sustainability, less is known about the spatiotemporal patterns of system performance. Here, we use 1.5 million NYC taxi trips (sampled over a six-month period) and experiment to understand how well they could be served with pooled services. We use an offline utility-driven ride-pooling algorithm and observe the pooling potential with six performance indicators: mileage reductions, travellers’ utility gains, share of pooled rides, occupancy, detours, and potential fleet reduction. We report distributions and temporal profiles of about 35 thousand experiments covering weekdays, weekends, evenings, mornings, and nights. We report complex spatial patterns, with gains concentrated in the core of the network and costs concentrated on the peripheries. The greatest potential shifts from the North in the morning to the Central and South in the afternoon. Offering pooled rides at the fare 32% lower than private ride-hailing seems to be sufficient to attract pooling yet dynamically adjusting it to the demand level and spatial pattern may be efficient. The patterns observed in NYC were replicated on smaller datasets in Chicago and Washington, DC, the occupancy grows with the demand with similar trends.
PLOS

Simulating two-sided mobility platforms with MaaSSim

Kucharski, Rafał, and Cats, Oded

PLoS ONE 2022

Abs HTML

Two-sided mobility platforms, such as Uber and Lyft, widely emerged in the urban mobility landscape. Distributed supply of individual drivers, matched with travellers via intermediate platform yields a new class of phenomena not present in urban mobility before. Such disruptive changes to transportation systems call for a simulation framework where researchers from various and across disciplines may introduce models aimed at representing the complex dynamics of platform-driven urban mobility. In this work, we present MaaSSim, a lightweight agent-based simulator reproducing the transport system used by two kinds of agents: (i) travellers, requesting to travel from their origin to destination at a given time, and (ii) drivers supplying their travel needs by offering them rides. An intermediate agent, the platform, matches demand with supply. Agents are individual decision-makers. Specifically, travellers may decide which mode they use or reject an incoming offer; drivers may opt-out from the system or reject incoming requests. All of the above behaviours are modelled through user-defined modules, allowing to represent agents’ taste variations (heterogeneity), their previous experiences (learning) and available information (system control). MaaSSim is a flexible open-source python library capable of realistically reproducing complex interactions between agents of a two-sided mobility platform. MaaSSim is available from a public repository, along with a set of tutorials and reproducible use-case scenarios, as demonstrated with a series of illustrative examples and a comprehensive case study.
CIiT

Using city-bike stopovers to reveal spatial patterns of urban attractiveness

Banet, Krystian, Naumov, Vitalii, and Kucharski, Rafał

Current Issues in Tourism 2022

Abs HTML

We demonstrate how digital traces of city-bike trips may become useful to identify urban space attractiveness. We exploit their unique feature – stopovers: short, non-traffic-related stops made by cyclists during their trips. As we demonstrate with the case study of Kraków (Poland), when applied to a big dataset, meaningful patterns appear, with hotspots (places with long and frequent stopovers) identified at both the top tourist and leisure attractions as well as emerging new places. We propose a generic method, applicable to any spatiotemporal city-bike traces, providing results meaningful to understand the general urban space attractiveness and its dynamics. With the proposed filtering (to mitigate a selection bias) and empirical cross-validation (to rule-out false-positive classifications) results effectively reveal spatial patterns of urban attractiveness. Valuable for decision-makers and analysts to enhance understanding of urban space consumption patterns by tourists and residents.
SciRep

Modelling virus spreading in ride-pooling networks

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

Scientific Reports 2021

Abs HTML

Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.
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

Abs HTML

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.
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

Abs HTML

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.
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

Abs HTML

Emerging on-demand sharing alternatives, in which one resource is utilised simultaneously by a circumstantial group of users, entail several challenges regarding how to coordinate such users. A very relevant case refers to how to form groups in a mobility system that offers shared rides, and how to split the costs within the travellers of a group. These are non-trivial tasks, as two objectives conflict: 1) minimising the total costs of the system, and 2) finding an equilibrium where each user is content with her assignment. Aligning both objectives is challenging, as users are not aware of the externalities induced to the rest. In this paper, we propose protocols to share the costs within a ride so that optimal solutions can also constitute equilibria. To do this, we model the situation as a non-cooperative game, which can be seen as a game-version of the well-known set cover problem. We show that the traditional notions of equilibrium in game theory (Nash and Strong) are not useful here, and prove that determining whether a Strong Equilibrium exists is an NP-Complete problem, by reducing it to the k-Exact-Cover problem. Hence, we propose three alternative equilibrium notions (stronger than Nash and weaker than Strong), depending on how users can coordinate. For each of these equilibrium notions, we propose a (possibly overcharging) cost-sharing protocol that yields the optimal solutions equilibria. Simulations for Amsterdam reveal that our protocols can achieve stable solutions that are close to the optimum, and that having a central coordinator can have a large impact.