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

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.
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.
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.
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.