Modelling and controlling virus spread processes in shared mobility networks 2021-2024, 1mln PLN funded by National Science Centre under scheme OPUS 19
We anticipate substantial changes to post-corona urban mobility, when safety concerns will drive individual, as well as, public decision makers. Travellers’ choices are likely to shift towards modes of low exposure to viruses. Consequently, the sustainable mobility paradigm needs to drift into ’sustainable, yet safe’, unleashing a novel trade-off dilemmas spanned between: cost-comfort-safety for individuals and: sustainability-efficiency-safety for policymakers. Outcomes of those decision patterns are likely to disturb landscape of urban mobility. When sustainable mass transit modes start being avoided by risk averse travellers, it may have devastating consequences on performance of transport systems (congestion and traffic delays) and its externalities (emissions, pressure on public space, etc.), which shall be counteracted. Notably, due to recent, disruptive changes in urban mobility, the, so-called, shared mobility (where two or more travellers share the same vehicle to reach the destination), provided via two-sided platforms (like Uber and Lyft), has proven to be an appealing alternative. Whether it will remain attractive solution for emerging mobility problems remains unknown. In particular, it is not known: a) how travellers willingness-to-share will change b) how viruses spread through the shareability graph c) how can we redesign shared rides system to control spreading and make sharing rides safe. This calls for a new set of models, theories and analyses to understand how shared rides can contribute to post-corona urban mobility. To this end, in this project we aim to:
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WP1:forecast demand for post-corona shared mobility Data-driven travel behaviour modelling. Series of stated preference experiments to estimate the post-corona willingness-to-share among the virus-aware travellers. Predict a presumably non-deterministic, heterogeneous travellers’ reaction to applied measures and virus exposure.
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WP2: model virus spreading on shared mobility networks Epidemic simulations with stochastic, time evolving contact networks. Reproduce and understand contact networks emerging from shared mobility to better model and predict spreading processes. Analyse structure of underlying network connectivity and identify hubs, communities, size and depth of diffusion trees and giant components.
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WP3: propose efficient strategies to trace and control it Control, trace and halt spreading with a proactive strategic, tactical and operational system management to make sharing safe again. Demand management to keep system attractive, yet controlling the contact to prevent a future outbreaks.
Two PhD students involved: Michal Bujak and Farnoud Ghasemi
Two PostDocs involved: Olha Shulika and Usman Akthar
Publications linked to the project
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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
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.
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Network structures of urban ride-pooling problems and their properties
Bujak, Michal,
and Kucharski, Rafal
Social Network Analysis and Mining
2023
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.
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Exploring shareability networks of probabilistic ride-pooling problems
Bujak, Michal,
and Kucharski, Rafał
arXiv preprint arXiv:2206.12259
2022
Travellers sharing rides in ride-pooling systems form various kinds of networks. While the notions of the so-called shareability graphs, has been in the core of many ride-pooling algorithms, so far they have not been explicitly analysed. Here, we introduce and examine four kinds of networks resulting from ride-pooling problems. We use 147 NYC taxi requests from 2016 pooled into attractive shared-rides with our utility based ExMAS algorithm and explore resulting shareability networks. To cover unknown individual properties of pooling travellers, we run 1000 replications of probabilistic pooling process, resulting in richer representation of weighted graphs instrumental to reveal complex network structures. Our findings reveal substantial differences between network structures and topologies. Properties of ride-pooling networks may be further analysed to better understand and improve ride-pooling systems.
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Modelling the Rise and Fall of Two-Sided Mobility Markets with Microsimulation
Ghasemi, Farnoud,
and Kucharski, Rafał
arXiv preprint arXiv:2208.02496
2022
In this paper, we propose a novel modelling framework to reproduce the market entry strategies for two-sided mobility platforms. In the MaaSSim agent-based simulator, we develop a co-evolutionary model to represent day-to-day dynamics of the two-sided mobility market with agents making rational decisions to maximize their perceived utility. Participation probability of agents depends on utility, composed of: experience, word of mouth and marketing components adjusted by agents every day with the novel S-shaped formulas - better suited (in our opinion) to reproduce market entry dynamics than previous approaches. With such a rich representation, we can realistically model a variety of market entry strategies and create significant network effects to reproduce the rise and fall of two-side mobility platforms. To illustrate model capabilities, we simulate a 400-day evolution of 200 drivers and 2000 travelers on a road-network of Amsterdam. We design a six-stage market entry strategy with consecutive: kick-off, discount, launch, growth, maturity and greed stages. After 25 days the platform offers discounts, yet it starts gaining market share only when the marketing campaign launches at day 50. Campaign finishes after 50 days, which does not stop the growth, now fueled mainly with a positive word of mouth effect and experiences. The platform ends discounts after 200 days and reaches the steady maturity period, after which its greedy strategy leads to collapse of its market share and profit. All above simulated with a single behavioral model, which well reproduces how agents of both sides adapts to platform actions.
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Modelling virus spreading in ride-pooling networks
Kucharski, Rafał,
Cats, Oded,
and Sienkiewicz, Julian
Scientific Reports
2021
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.
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Ride-pooling service assessment with rational, heterogeneous, non-deterministic travellers
Bujak, Michal,
and Kucharski, Rafal
arXiv preprint arXiv:2307.10827
2023
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 aptitudes. So far, ride-poling analyses assumed homogeneity and/or determinism 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. We replicate their random behaviour to obtain meaningful distributions. Unsurprisingly, results vary significantly from the deterministic benchmark: expected 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. Such findings shed a new light on the expected performance of large scale ride-pooling systems. We argue, that the policy recommendations shall be revised to accommodate behavioural heterogeneity.
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Can we start sharing our rides again? The postpandemic ride-pooling market
Shulika, Olha,
and Kucharski, Rafał
arXiv preprint arXiv:2209.02229
2022
Before the pandemic ride-pooling was a promising emerging mode in urban mobility. It started reaching the critical mass with a growing number of service providers and the increasing number of travellers (needed to ensure ride-pooling efficiency and sustainability). However, the COVID pandemic was disruptive for ride-pooling. Many services were cancelled, several operators needed to change their business models and travellers started avoiding those services. In the postpandemic period, we need to understand what is the future of ride-pooling: whether the ride-pooling system can recover and remain a relevant part of future mobility. Here we provide an overview of the postpandemic ride-pooling market based on the analysis of three components: a) literature review, b) empirical pooling availability survey and c) travellers’ behaviour studies. We conclude that the core elements of the ride-pooling business model were not affected by the pandemic. It remains a promising option for all the parties involved, with a great potential to become attractive for travellers, drivers, TNC platforms and policymakers. The travel behaviour changes due to the pandemic seem not to be long-lasting, our virus awareness is no anymore the key concern and our willingness to share and reduce fares seem to be high again. Yet, whether ride-pooling will get another chance to grow remains open. The number of launches of ride-pooling start-ups is unprecedented, yet the financial perspectives are unclear.
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Optimizing Ride-Pooling Revenue: Pricing Strategies and Driver-Traveller Dynamics
Akhtar, Usman,
Ghasemi, Farnoud,
and Kucharski, Rafal
arXiv preprint arXiv:2403.13384
2024
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.
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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
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.
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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
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.
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The Implications of Drivers’ Ride Acceptance Decisions on the Operations of Ride-Sourcing Platforms
Ashkrof, Peyman,
Ghasemi, Farnoud,
Kucharski, Rafał,
Correia, Gonçalo Homem de Almeida,
Cats, Oded,
and Arem, Bart
Available at SSRN 4760834
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, 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.
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MoMaS: Two-sided Mobility Market Simulation Framework for Modeling Platform Growth Trajectories
Kucharski, Rafal,
and Ghasemi, Farnoud
2024
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 behaviour 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. MoMaS allows to reproduce a variety of market phenomena, including reluctancy, neutrality, and loyalty at the individual level, as well as critical mass, bandwagon effect, positive and negative cross-side network effects at the aggregated level, which are crucial to reproduce realistic growth trajectories.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.
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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
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.