PostCorona Shared Mobility

Ride-pooling research studies and contribution

drawing

Modelling and controlling virus spread processes in shared mobility networks 2021-2024, 1mln PLN funded by National Science Centre under scheme OPUS 19

Ride-pooling is a complex topic that incorporates various research branches. It combines perspctives of operations research, stochastic analysis, choice modelling, agent-based models, network science, epidemiology etc. Rafał Kucharski started research on this topic at TU Delft with prof. Oded Cats and continued at Jagiellonian University, mainly with Michał Bujak.

The contributions to this field are as follows.

Utility-based, strategic algorithm

Kucharski, Rafał, and Oded Cats. “Exact matching of attractive shared rides (ExMAS) for system-wide strategic evaluations.” Transportation Research Part B: Methodological 139 (2020): 285-310.

The premise of ride-sharing is that service providers can offer a discount, so that travellersare compensated for prolonged travel times and induced discomfort, while still increasingtheir revenues. While recently proposed real-time solutions support online operations, al-gorithms to perform strategic system-wide evaluations are crucially needed. We proposean exact, replicable and demand-, rather than supply-driven algorithm for matching tripsinto shared rides. We leverage on delimiting our search for attractive shared rides only,which, coupled with a directed shareability multi-graph representation and efficient graphsearches with predetermined node sequence, narrows the (otherwise exploding) search-space effectively enough to derive an exact solution. The proposed utility-based formula-tion paves the way for model integration in travel demand models, allowing for a cross-scenario sensitivity analysis, including pricing strategies and regulation policies.

Ride-pooling potential

Shulika, Olha, Michał Bujak, Farnoud Ghasemi, and Rafał Kucharski. “Spatiotemporal variability of ride-pooling potential–Half a year New York City experiment.” Journal of Transport Geography 114 (2024): 103767.

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.

Compounding delay

Kucharski, Rafał, Andres Fielbaum, Javier Alonso-Mora, and Oded Cats. “If you are late, everyone is late: late passenger arrival and ride-pooling systems’ performance.” Transportmetrica A: Transport Science 17, no. 4 (2021): 1077-1100.

Sharing rides in on-demand systems allow passengers to reducetheir fares and service providers to increase revenue, though at thecost of adding uncertainty to the system. Notably, the uncertaintyof ride-pooling systems stems not only from travel times but alsofrom unique features of sharing, such as the dependency on otherpassengers’ arrival time at their pick up points. In this work, we theo-retically and experimentally analyse how late arrivals at pick up loca-tions impact shared rides’ performance. We find that the total delayis equally distributed among sharing passengers. However, delaycomposition gradually shifts from on-board delay only for the firstpassenger to waiting delay at the origin for the last passenger. Sadly,trips with more passengers are more adversely impacted. Strate-gic behaviour analysis reveals Nash equilibria that might emerge.We analyse the system-wide effects and find that when latenessincreases passengers refrain from sharing and eventually opt-out.

Networks emergence, properties, and new representations

Bujak, Michal, and Rafal Kucharski. “Network structures of urban ride-pooling problems and their properties.” Social Network Analysis and Mining 13, no. 1 (2023): 89.

Graph (network) structures, starting with the seminal paper by Santi et al. (2014), were the algorithmic backbone of the proposed algorithms. Various methods introduce ride-pooling travellers and drivers as nodes in simple and bipartite representations. Network algorithms were leveraged to find optimal results. Although networks were an intermediate tool, neither their emergence nor their properties had been studied so far. In the this article, for the first time, we formalised four distinct network structures within ride-pooling. We introduce new, weighted representations that contain more information, based on stochastic analysis. To understand the role of the topologies of ride-pooling networks, we studied the correlation between a sharing discount (the control variable), network properties, and system performance indicators. Our analysis highlighted the existence of a critical mass embedded in network topology. This means that based on certain topological properties, we can assess the demand level required for the ride-pooling to become efficient.

Modelling virus spreading

Kucharski, Rafał, Oded Cats, and Julian Sienkiewicz. “Modelling virus spreading in ride-pooling networks.” Scientific Reports 11, no. 1 (2021): 7201.

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.

Virus mitigation in an efficient system

Proszewska, Magdalena, Michał Bujak, Rafał Kucharski, Marek Śmieja, and Jacek Tabor. “Optimising network efficiency in the epidemic scenario.” Available at SSRN 4976648 (2024).

Efficiency of a system network often relies on high connectivity. However, strongly connected networks are vulnerable in a case of a spreading virus. In the study, we propose a clustering method which balances the two opposing factors: maintains a high system efficiency yet minimises the spreading potential. Our Deep Epidemic Efficiency Network (DEEN) model leverages Graph Convolutional Neural Networks and a novel loss function. In an unsupervised setting, we seek a partition that maximises the system utility while restraining the transmission rate to a desired level. We show that proposed method successfully solves three reallife problems: ride-pooling service in New York City, economic exchange between regions in Poland, and information sharing via peer-to-peer network.

Impact of the behavioural heterogeneity

Bujak, Michał, and Rafał Kucharski. “Ride-pooling service assessment with heterogeneous travellers in non-deterministic setting.” Transportation (2024): 1-24.

Certain aspects of a trip, such as distance, travel time, and waiting time, are physical measures known to an operator. However, how these characteristics influence the perception of an individual traveller, expressed via utility equations, is subject to their individual behavioural traits, such as value-of-time and willingness-to-share. These traits are typically latent to the operator. In most studies, authors simplify the setting and assume that the population is homogeneous. This leads to deterministic utility equations with constant and homogeneous parameters. Population diversity and model uncertainty remain uncaptured, which leads to a high cancellation rate. As a result, ride-pooling systems often do not reach critical mass which limits their profitability and ultimately leads to failure.

As we show in this study, the assumption that population is homogeneous in value-of-time and willingness-to-share results in an underperforming system (cancellation rate over one-third). In utility equations, it is crucial to recognise heterogeneous characteristics. Many travellers, with high time sensitivity and/or who are more reluctant towards sharing, reject proposed pooled rides. On the other hand, many clients who gladly sacrifice their travel time for monetary savings are discarded as potential co-travellers. We extend the utility equations to allow for diverse population characteristics. We employ Monte Carlo stochastic analysis to show that heterogeneity yields significantly different modelling results compared to the baseline. The key performance indicators of the system change: the savings in mileage are lower, while the gains in utility for travellers are greater. As another highlight of the study, we used the network representations introduced in our earlier research to find travellers who are crucial to service performance.

Personalised pricing

Bujak, Michał, and Rafał Kucharski. “Balancing Profit and Traveller Acceptance in Ride-Pooling Personalised Fares.” arXiv preprint arXiv:2411.03370 (2024).

Certain travellers, whose trips are long and well aligned with other clients, are particularly valuable to the operator. To ensure they are satisfied with the offer, the operator is inclined to offer a higher discount. In our second study on heterogeneity [M4], we develop a stochastic framework for the assessment of pricing policies and propose a personalised pricing method. Based on an external preference study, we derive and use a population distribution of the value-of-time and the willingness-to-share (penalty-for-sharing). We assume that the operator does not know the individual traits of clients, yet applies the population distribution for calculations. We introduce an acceptance probability function which, for a given sharing discount level, determines how likely a traveller is to accept a shared ride. On top of the acceptance probability, we examine the resulting acceptance and rejection configurations along with the associated revenues and costs given by a pricing policy. This framework helps us to mimic a real world scenario where heterogeneous behavioural preferences are latent and can be assessed solely in a probabilistic setting. Next, we propose a personalised pricing policy. We dissect the process into two levels which, as we rigorously prove, leads to efficient and optimal results. We introduce the expected profitability measures that incorporates travellers’ perspective into economic assessment by the operator. Our method successfully recognises contribution of individuals to the system. As we show in a numerical analysis, our method not only maximises the expected profitability in the system but also encourages compound rides and results in a high acceptance rate. Compared to other pricing policies, it proves to be superior in economic and environmental aspects, and leads to higher attractiveness for travellers.

Adaptive pricing

Bujak, Michał, and Rafał Kucharski. “Adaptive Optimisation of Ride-Pooling Personalised Fares in a Stochastic Framework.” arXiv preprint arXiv:2508.20723 (2025).

Although the personalised pricing algorithm maximises the short-term profitability of the service, it does not address the long-term consequences. A satisfied client is more likely to remain loyal to the operator and requests rides more often. The long-term demand acquisition is vital to ensure that the ride-pooling critical mass is reached and maintained. However, so far researchers have focused solely on a single-day operations where role of the long-term attraction and loyalty was neglected. In this study, we propose a pricing policy to support the operator who aims to learn individual parameters and attract travellers while providing an efficient service. In this explore-exploit setting, we need to balance the short- and long-term perspective to attain all goals. We propose an adaptive personalised pricing model with Bayesian inference of individual behavioural traits. Taking the role of the operator, we enrich our objective function in the personalised discounts optimisation. Our method supplements the pricing optimisation with additional components which ensure the long-term demand attraction. While conducting day-to-day operations, we observe travellers’ choices (who either accept or reject our offer) and increase operator’s knowledge. We estimate current satisfaction (loyalty) levels and, by adopting the S-shaped learning curve, assess future value of a client in a stochastic setting. To uncover individual traits of travellers, we employ Bayesian learning which allows for a stronger personalisation. The proposed pricing policy provides good economic indicators since day one, attracts more clients daily, and generates more satisfaction among travellers.

See main achievements, results and impact of our project.

Publications linked to the project

T&T

Can we start sharing our rides again? The post-pandemic ride-pooling market

Shulika, Olha, and Kucharski, Rafal

Transport and Telecommunication 2025

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Before the pandemic, ride-pooling was a promising mode of urban mobility, marked by increasing service providers and increasing traveller adoption, critical to its efficiency and sustainability. However, the COVID-19 pandemic caused significant disruption, with services suspended, business models altered, and reduced traveller confidence. In the post-pandemic era, understanding the future of ride-pooling is crucial. This article reviews the market through literature, pooling availability, and traveller behaviour studies. We find that the core elements of the ride-pooling model remain intact, with potential to appeal to travellers, drivers, platforms, and policymakers. Changes in travel behaviour due to the pandemic appear temporary, with a high willingness to share rides and reduce costs. However, whether ride-pooling can regain its growth remains uncertain. Despite unprecedented start-up activity, the financial prospects are unclear, posing challenges to its resurgence as a sustainable mobility solution.
ITSC

Reinforcement Learning Approach for Improving Platform Performance in Two-Sided Mobility Markets

Ghasemi, Farnoud, Tabatabaei, Seyed Hassan, Ghanadbashi, Saeedeh, Kucharski, Rafał, and Golpayegani, Fatemeh

In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) 2024

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Two-sided mobility markets, with platforms like Uber and Lyft, are complex systems by nature due to intricate, non-linear interactions between the platform and the involved parties including travelers and drivers. These interactions give rise to phenomena underlying market evolution, mainly cross-side network effects. Currently, such platforms rely on rule-based (RB) strategies with a constant commission rate to grow and achieve sustainability in terms of market share and profitability. However, the constant commission rate significantly constrains the platform’s ability to leverage network effects, leading to inefficient growth. In this study, a Reinforcement Learning-based (RLB) strategy is proposed to improve the platform performance through strategic levers. We employ a Deep Q-Network (DQN) within an agent-based framework, enabling the platform to adjust the commission rate on a day-to-day basis while learning the complex, non-linear interactions in the market. The results show that the RL-based strategies successfully generate and control the essential cross-side network effects in the market enhancing the platform performance via dynamic commission rate. Our results indicate 12% improvement in the platform revenue with the RL-based strategy in comparison to the rule-based strategy without significantly compromising the platform market share which can essentially impact the platform’s viability in the long term.
TR:C

MoMaS: Two-sided Mobility Market Simulation Framework for Modeling Platform Growth Trajectories

Ghasemi, Farnoud, and Kucharski, Rafal

Transportation Research Part C: Emerging Technologies 2025

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Mobility platforms such as Uber and DiDi have been introduced in cities worldwide, each demonstrating varying degrees of success, employing diverse strategies, and exerting distinct impacts on urban mobility. We have observed various growth trajectories in two-sided mobility markets and understood the underlying mechanisms. However, to date, a realistic microscopic model of these markets including phenomena such as network effects has been missing. State-of-the-art methods well estimate the macroscopic equilibrium conditions in the market, but struggle to reproduce the individual human behavior behind and complex growth patterns sensitive to platform strategy and policies. To bridge this gap, we introduce the MoMaS (two-sided Mobility Market Simulation) framework to represent growth mechanism in two-sided mobility markets based on the realistic behavior adjustment of drivers and travelers reactive to platform strategy. In the proposed framework, traveler and driver agents learn the platform utility from multiple channels: their own experience, peers’ word-of-mouth, and the platform’s marketing, all-together constituting the agent’s perceived utility of the platform. Each of these channels is modeled and updated by our S-shaped learning model day-to-day which stabilizes, and at the same time, remains sensitive to the system changes. The platform can simulate any strategy on five levers: trip fare, commission rate, discount rate, incentive rate, and marketing. While detailed empirical data and actual strategies for platform growth remain largely unknown, MoMaS allows to reproduce series of plausible growth trajectories that were previously unattainable. The framework facilitates the modeling of individual-level behaviors such as reluctance, neutrality, and loyalty, alongside aggregate-level dynamics like critical mass, bandwagon effects, and both positive and negative cross-side network effects. We illustrate the capabilities of MoMaS through an extensive set of real-world experiments. Our results demonstrate that once the platform acquires critical mass, it triggers a significant positive cross-side network effect, accelerating growth. However, this can be reversed if a negative cross-side network effect is triggered, leading to the collapse of the platform. MoMaS is applicable for real-sized problems and available on public repository along with reproducible experiments.
TR:A

The implications of drivers’ ride acceptance decisions on the operations of ride-sourcing platforms

Ashkrof, Peyman, Ghasemi, Farnoud, Kucharski, Rafał, Almeida Correia, Gonçalo Homem, Cats, Oded, and Arem, Bart

Transportation Research Part A: Policy and Practice 2025

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As a two-sided digital platform, ride-sourcing has disruptively penetrated the mobility market. Ride-sourcing companies provide door-to-door transport services by connecting passengers with independent service suppliers labelled as “driver-partners”. Once a passenger submits a ride request, the platform attempts to match the request with a nearby available driver. Drivers have the freedom to accept or decline ride requests. The consequences of this decision, which is made at the operation level, have remained largely unknown in the literature. Using agent-based simulation modelling on the realistic case study of the city of Amsterdam, the Netherlands, we study the impacts of drivers’ ride acceptance behaviour, estimated from unique empirical data, on the ride-sourcing system where the platform applies regular and surge pricing strategies, and riders may revoke their requests and reject the received offers. Furthermore, we delve into the implications of various supply–demand intensities, a centralised fleet (i.e., mandatory acceptance on each ride request) versus a decentralised fleet (i.e., ride acceptance decision by each driver), ride acceptance rates, and surge pricing settings. We find that the ride acceptance decision of ride-sourcing drivers has far-reaching consequences for system performance in terms of passengers’ waiting time, driver’s revenue, operating costs, and profit, all of which are highly dependent on the ratio between demand and supply. As the system undergoes a transition from undersupplied (i.e., real-time demand locally exceeds available drivers) to balanced and then oversupplied state (i.e., more available drivers than real-time demand), ride acceptance decisions result in higher income inequality. A high acceptance rate among drivers may lead to more rides, but it does not necessarily increase their profit. Surge pricing is found to be asymmetrically in favour of all the parties despite adverse effects on the demand side due to higher trip fare. This study offers insights into both the aggregated and disaggregated levels of ride-sourcing system operations and outlines a series of transport policy and practice implications in cities that offer such ride-sourcing systems.
arXiv

Balancing Profit and Traveller Acceptance in Ride-Pooling Personalised Fares

Bujak, Michal, and Kucharski, Rafal

arXiv preprint arXiv:2411.03370 2024

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In a ride-pooling system, travellers experience discomfort associated with a detour and a longer travel time, which is compensated with a sharing discount. Most studies assume travellers receive either a flat discount or, in rare cases, a proportional to the inconvenience. We show the system benefits from individually tailored fares. We argue that fares that optimise an expected profit of an operator also improve system-wide performance if they include travellers’ acceptance. Our pricing method is set in a heterogeneous population, where travellers have varying levels of value-of-time and willingness-to-share, unknown to the operator. A high fare discourages clients from the service, while a low fare reduces the profit margin. Notably, a shared ride is only realised if accepted by all co-travellers (decision is driven by the latent behavioural factors). Our method reveals intriguing properties of the shareability topology. Not only identifies rides efficient for the system and supports them with reduced fares (to increase their realisation probability), but also identifies travellers unattractive for the system (e.g. due to incompatibility with other travellers) and effectively shifts them to private rides via high fares. Unlike in previous methods, such approach naturally balances the travellers satisfaction and the profit maximisation. With an experiment set in NYC, we show that this leads to significant improvements over the flat discount baseline: the mileage (proxy for environmental externalities) is reduced by 4.5% and the operator generates more profit per mile (over 20% improvement). We argue that ride pooling systems with fares that maximise profitability are more sustainable and efficient if they include travellers’ satisfaction.
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.
arXiv

Optimizing Ride-Pooling Revenue: Pricing Strategies and Driver-Traveller Dynamics

Akhtar, Usman, Ghasemi, Farnoud, and Kucharski, Rafal

arXiv preprint arXiv:2403.13384 2024

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Ride-pooling, to gain momentum, needs to be attractive for all the parties involved. This includes also drivers, who are naturally reluctant to serve pooled rides. This can be controlled by the platform’s pricing strategy, which can stimulate drivers to serve pooled rides. Here, we propose an agent-based framework, where drivers serve rides that maximise their utility. We simulate a series of scenarios in Delft and compare three strategies. Our results show that drivers, when they maximize their profits, earn more than in both the solo-rides and only-pooled rides scenarios. This shows that serving pooled rides can be beneficial as well for drivers, yet typically not all pooled rides are attractive for drivers. The proposed framework may be further applied to propose discriminative pricing in which the full potential of ride-pooling is exploited, with benefits for the platform, travellers, and (which is novel here) to the drivers.
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.
arXiv

Ride Acceptance Behaviour Investigation of Ride-sourcing Drivers Through Agent-based Simulation

Ghasemi, Farnoud, Ashkrof, Peyman, and Kucharski, Rafal

arXiv preprint arXiv:2310.05588 2023

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Ride-sourcing platforms such as Uber and Lyft offer drivers (i.e., platform suppliers) considerable freedom of choice in multiple aspects. At the operational level, drivers can freely accept or decline trip requests that can significantly impact system performance in terms of travellers’ waiting time, drivers’ idle time and income. Despite the extensive research into the supply-side operations, the behavioural aspects, particularly drivers’ ride acceptance behaviour remains so far largely unknown. To this end, we reproduce the dynamics of a two-sided mobility platform on the road network of Delft using an agent-based simulator. Then, we implement a ride acceptance decision model enabling drivers to apply their acceptance strategies. Our findings reveal that drivers who follow the decision model, on average, earn higher income compared to drivers who randomly accept trip requests. The overall income equality between drivers with the acceptance decision is higher and travellers experience lower waiting time in this setting.
arXiv

Dynamics of the Ride-Sourcing Market: A Coevolutionary Model of Competition between Two-Sided Mobility Platforms

Ghasemi, Farnoud, Drabicki, Arkadiusz, and Kucharski, Rafał

arXiv preprint arXiv:2310.05543 2023

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There is a fierce competition between two-sided mobility platforms (e.g., Uber and Lyft) fueled by massive subsidies, yet the underlying dynamics and interactions between the competing plat-forms are largely unknown. These platforms rely on the cross-side network effects to grow, they need to attract agents from both sides to kick-off: travellers are needed for drivers and drivers are needed for travellers. We use our coevolutionary model featured by the S-shaped learning curves to simulate the day-to-day dynamics of the ride-sourcing market at the microscopic level. We run three scenarios to illustrate the possible equilibria in the market. Our results underline how the correlation inside the ride-sourcing nest of the agents choice set significantly affects the plat-forms’ market shares. While late entry to the market decreases the chance of platform success and possibly results in "winner-takes-all", heavy subsidies can keep the new platform in competition giving rise to "market sharing" regime.
arXiv

Exploring Computational Complexity Of Ride-Pooling Problems

Akhtar, Usman, and Kucharski, Rafal

arXiv preprint arXiv:2208.02504 2022

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Ride-pooling is computationally challenging. The number of feasible rides grows with the number of travelers and the degree (capacity of the vehicle to perform a pooled ride) and quickly explodes to the sizes making the problem not solvable analytically. In practice, heuristics are applied to limit the number of searches, e.g., maximal detour and delay, or (like we use in this study) attractive rides (for which detour and delay are at least compensated with the discount). Nevertheless, the challenge to solve the ride-pooling remains strongly sensitive to the problem settings. Here, we explore it in more detail and provide an experimental underpinning to this open research problem. We trace how the size of the search space and computation time needed to solve the ride-pooling problem grows with the increasing demand and greater discounts offered for pooling. We run over 100 practical experiments in Amsterdam with 10-minute batches of trip requests up to 3600 trips per hour and trace how challenging it is to propose the solution to the pooling problem with our ExMAS algorithm. We observed strong, non-linear trends and identified the limits beyond which the problem exploded and our algorithm failed to compute. Notably, we found that the demand level (number of trip requests) is less critical than the discount. The search space grows exponentially and quickly reaches huge levels. However, beyond some level, the greater size of the ride-pooling problem does not translate into greater efficiency of pooling. Which opens the opportunity for further search space reductions.
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.