Farnoud Ghasemi

List of main publications and preprints

1. 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.

2. J. public transp.

   SimFLEX: A methodology for comparative analysis of urban areas for implementing new on-demand feeder bus services

   Vasiutina, Hanna, Shulika, Olha, Bujak, Michał, Ghasemi, Farnoud, and Kucharski, Rafał

   Journal of Public Transportation 2025

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   On-demand feeder bus services present an innovative solution to urban mobility challenges, yet their success depends on a thorough assessment and strategic planning. Despite their potential, a systematic methodology for selecting suitable service areas remains underdeveloped. Simulation Framework for Feeder Location Evaluation (SimFLEX) utilizes spatial, demographic, and transportation-specific characteristics to conduct simulations at a microscopic level and compute various key performance indicators (KPIs), including service attractiveness, waiting time reduction, and added value. To address the stochastic nature of demand and uncertainty embedded in mode choice, we leverage Monte Carlo analysis, capturing variability across simulated scenarios. Our framework integrates several methods for a complete assessment of feeder potential: microscopic demand generation, creation of shared rides, public transport modeling, and iterative traveler learning and stabilization approach. Once the system stabilizes, KPIs are computed for comparative and sensitivity analyzes. As a showcase for our method, we apply SimFLEX to compare two remote districts in Krakow, Poland – Bronowice and Skotniki – designated as candidates for service deployment. Despite similar urban characteristics, our analysis revealed notable differences in KPIs between the analyzed areas: Skotniki exhibited higher service attractiveness (around 30 %) and added value (up to 7 %), whereas Bronowice showed greater potential for reducing waiting times (around 77 %). To assess the robustness of SimFLEX outputs under uncertain behavioral assumptions, we conducted a sensitivity analysis across a range of alternative-specific constants. The results consistently confirmed Skotniki as the more suitable candidate for feeder service implementation, demonstrating that this conclusion holds despite variations in a key preference parameter. The SimFLEX framework can be instrumental for decision makers to estimate feeder potential for a designated area. The model’s flexibility and modular characteristics make it a versatile tool for policymakers and urban planners to enhance urban mobility.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.