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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Tweets by Rafal_Lab

selected publications

preprint

Optimising network efficiency in the epidemic scenario

Magdalena, Proszewska, Michal, Bujak, Rafal, Kucharski, Jacek, Tabor, and Marek, Smieja

2024

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We consider the problem of reducing virus spreading in the system network (graph) while keeping the utility of the whole system at the maximal level. To balance the above two opposite goals, we propose Deep Epidemic Efficiency Network (DEEN), an unsupervised clustering method, which optimises graph efficiency in an epidemic scenario using Graph Convolutional Neural Networks and a novel loss function. Given the desired virus transmission, it constructs a graph partition for which the predefined transmission rate is not exceeded and utility function is maximised. We show that proposed method successfully solves three real-life problems: ride-pooling service in New York City, economic exchange between regions in Poland, and information sharing via peer-to-peer network. In particular, by dividing 150 New York taxi travellers into four groups our method increases epidemic threshold more than twofold at the cost of reducing utility only by 13%.The model can be instrumental in future pandemic outbreaks when we need to balance between maintaining efficiency and preventing the spread of the virus.
npj

Hyper pooling private trips into high occupancy transit like attractive shared rides

Kucharski, Rafał, and Cats, Oded

npj Sustainable Mobility and Transport 2024

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The size of the solution space associated with the trip-matching problem has made the search for high-order ride-pooling prohibitive. We introduce hyper-pooled rides along with a method to identify them within urban demand patterns. Travellers of hyper-pooled rides walk to common pick-up points, travel with a shared vehicle along a sequence of stops and are dropped off at stops from which they walk to their destinations. While closely resembling classical mass transit, hyper-pooled rides are purely demand-driven, with itineraries (stop locations, sequences, timings) optimised for all co-travellers. For 2000 trips in Amsterdam the algorithm generated 40 hyper-pooled rides transporting 225 travellers. They would require 52.5 vehicle hours to travel solo, whereas in the hyper-pooled multi-stop rides, it is reduced sixfold to 9 vehicle hours only. This efficiency gain is made possible by achieving an average occupancy of 5.8 (and a maximum of 14) while remaining attractive for all co-travellers.
Transportation

Ride-pooling service assessment with heterogeneous travellers in non-deterministic setting

Bujak, Michal, and Kucharski, Rafal

Transportation 2024

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Ride-pooling remains a promising emerging mode with a potential to contribute towards urban sustainability and emission reductions. Recent studies revealed complexity and diversity among travellers’ ride-pooling attitudes. So far, ride-poling analyses assumed homogeneity of ride-pooling travellers. This, as we demonstrate, leads to a false assessment of ride-pooling system performance. We experiment with an actual NYC demand from 2016 and classify travellers into four groups of various ride-pooling behaviours (value of time and penalty for sharing), as reported in the recent SP study from Netherlands. We replicate their behavioural characteristics, according to the population distribution, to obtain meaningful performance estimations. Results vary significantly from the homogeneous benchmark: mileage savings were lower, while the utility gains for travellers were greater. Observing performance of heterogeneous travellers, we find that those with a low value of time are most beneficial travellers in the pooling system, while those with an average penalty for sharing benefit the most. Notably, despite the highly variable travellers’ behaviour, the confidence intervals for the key performance indicators are reasonably narrow and system-wide performance remains predictable. Our results show that the incorrect assumption of homogeneous traits leads to a high dissatisfaction of 18.5% and a cancellation rate of 36%. Such findings shed a new light on the expected performance of large scale ride-pooling systems.
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

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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.
AAMAS

Modelling the Rise and Fall of Two-sided Markets

Ghasemi, Farnoud, and Kucharski, Rafal

In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems 2024

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Two-sided markets disrupted our economies, reshaping markets as diverse as tourism (airbnb), mobility (Uber) and food deliveries (UberEats). New market leaders arose leveraging on platform-based business model, questioning well-established paradigms. The underlying processes behind their growth are non-trivial, inherently microscopic, and leverage on complex human interactions. Platforms need to reach critical mass of both supply and demand to trigger the so-called cross-sided network effects. To this end, platforms adopt a variety of strategies to first create the market, then expand it and finally successfully compete with others. Such a complex social system with many non-linear interactions and learning processes calls for a dedicated modelling approach. State-of-the-art methods well estimate the macroscopic equilibrium conditions, but struggle to reproduce the complex growth patterns and individual human behaviour behind. To bridge this gap, we propose the microscopic S-shaped learning model where agents build their perception on the new service with time, affected by both endogenous (service quality) and exogenous (marketing and word-of-mouth) factors cumulated from experiences. We illustrate it with the case of two-sided mobility platform (Uber), where the platform applies a series of marketing actions leading to rise and then fall on the market where 200 drivers serve 2000 travellers on the complex urban network of Amsterdam. Our model is the first to reproduce not only behaviourally sound, but also empirically observed growth trajectories, it remains sensitive to a variety of marketing strategies, allows reproducing the competition between platforms and is designed to be integrated with machine learning algorithms to identify the optimal market entry strategy.
SNAM

Network structures of urban ride-pooling problems and their properties

Bujak, Michal, and Kucharski, Rafal

Social Network Analysis and Mining 2023

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Travellers, when sharing their rides in a so-called ride-pooling system, form complex networks. Despite being the algorithmic backbone to the ride-pooling problems, the shareability graphs have not been explicitly analysed yet. Here, we formalise them, study their properties and analyse relations between topological properties and expected ride-pooling performance. We introduce and formalise two representations at the two crucial stages of pooling analysis. On the NYC dataset, we run two simulations with the link generation formulas. One is when we increase discount offered to the travellers for shared rides (our control variable) and observe the phase transition. In the second, we replicate the non-deterministic behaviour of travellers in ride-pooling. This way, we generate probabilistic, weighted networks. We observed a strong correlation between the topological properties of ride-pooling networks and the system performance. Introduced class of networks paves the road to applying the network science methods to a variety of ride-pooling problems, like virus spreading, optimal pricing or stability analysis.
RTBM

Should I stay or should I board? Willingness to wait with real-time crowding information in urban public transport

Drabicki, Arkadiusz, Cats, Oded, Kucharski, Rafał, Fonzone, Achille, and Szarata, Andrzej

Research in Transportation Business & Management 2023

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Overcrowding is a major phenomenon affecting travel experience in urban public transport, whose negative impacts can be potentially mitigated with real-time crowding information (RTCI) on public transport vehicle departures. In this study, we investigate the willingness to wait (WTW) with instantaneous RTCI to avoid the in-vehicle (over)crowding the passenger faces, focusing specifically on urban crowding context (i.e. bus and tram systems). We conduct a stated-preference survey in Krakow (Poland), where we examine the choice probability between boarding now a more crowded vehicle vs. waiting at the stop for a less-crowded PT departure, and estimate a series of discrete choice models. Results show that 50–70% of respondents consider skipping a first departure which is excessively overcrowded and 10–30% would skip a vehicle with moderate standing crowding on-board. Acceptable waiting times typically range between 2 and 13 min, depending on crowding level and propensity to arrive on-time, but may even exceed 20 min in individual cases. These findings indicate that RTCI can induce a substantial WTW, affecting travel behaviour. We discuss its implications for mitigating service disruptions and demand management policies, including prospective support for public transport recovery in the aftermath of covid-19 crisis.
PLOS

Simulating two-sided mobility platforms with MaaSSim

Kucharski, Rafał, and Cats, Oded

PLoS ONE 2022

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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

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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.
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

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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.
SciRep

Modelling virus spreading in ride-pooling networks

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

Scientific Reports 2021

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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

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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

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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.