Adjunct (assistant professor) at Jagiellonian University (May 2022 - January 2024), where I researched complex social systems such as urban mobility. Always curious about technological innovations and therefore earned a Ph.D. in Computer Science & Engineering. I strived for a complete understanding of Cloud Computing/Big Data and always anxious to learn something new. My research interests and competencies encompassed application aspects of distributed systems, cloud computing and Cloud-centric Data Acquisition. I focused on enabling cloud for data acquisition, synchronization and persistence.
My Research Focus:
| Cloud-centric IoT |
Data Acquisition & Curation |
Cloud Computing |
Wellness-based Ubiquitous Platforms |
Performance-based Applications |
Software Engineering & Architecture |
Parallel & Distributed Systems |
Urban Mobility |
List of main publications and preprints
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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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Exploring Computational Complexity Of Ride-Pooling Problems
Akhtar, Usman,
and Kucharski, Rafal
arXiv preprint arXiv:2208.02504
2022
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.
