Opens in a new tab
ORBIT Lab logo
Mahsa Farhani
PhD Graduate · 2026

Mahsa Farhani

Mahsa Farhani is a PhD alumna of Delft University of Technology. Her research examines demand-management strategies for shared mobility services by integrating behavioural models with operations research. She studies how social influence, service quality, and context-dependent preferences shape adoption and operational decisions, and develops optimization methods for phased service design and behaviourally informed service menus. She defended her doctoral dissertation, Demand Management Strategies for Operations of Shared Mobility Services, at TU Delft in 2026.

Research interestsShared mobility · Demand management · Behavioral modeling
External profiles
TU Delft Research PortalLinkedInORCID

Publications

Research in Transportation Business & Management journal cover
Service design for shared micromobility: Long-term planning under gradual user adoption
Research in Transportation Business & Management2026
BibTeX
% VERIFIED; verified 2026-10-11
@article{orbit-5b40dc103a823723,
  title = {Service design for shared micromobility: Long-term planning under gradual user adoption},
  author = {Farhani, Mahsa and Rezaei, Jafar and Azadeh, Shadi Sharif and Maknoon, Yousef},
  journal = {Research in Transportation Business \& Management},
  year = {2026},
  volume = {69},
  pages = {101818},
  eid = {101818},
  doi = {10.1016/j.rtbm.2026.101818},
  url = {https://doi.org/10.1016/j.rtbm.2026.101818},
  issn = {2210-5395, 2210-5409},
  publisher = {Elsevier BV},
  abstract = {Service design for shared micromobility services, such as e-mopeds, involves decisions on service location, fleet size, pricing, and resource allocation, which are typically based on potential demand estimates. In practice, however, potential users adopt the service gradually, leading to market penetration over time. Ignoring this gradual penetration can lead to suboptimal decisions and service failures. We propose a phased optimization model to maximize profitability over a planning horizon divided into discrete periods. The model jointly determines service locations, fleet size, and spatio-temporal resource allocation while ensuring a targeted service level. To model gradual adoption, we apply an extended Bass diffusion framework in which adoption depends on service quality, defined by accessibility and availability, and social influence, reflecting the impact of current users on others’ adoption. The phased deployment strategy aligns service expansion and investment decisions with adoption growth (market penetration) over multiple periods. Applied to a bike-sharing case in Manhattan, the proposed approach improves resource allocation and increases profitability by up to 47\% compared with an upfront strategy under parameter misestimation. Furthermore, ignoring service quality in the adoption model leads to a profit loss of up to 54\% when user expectations are high The results shed light on the value of synchronizing service design decisions with user adoption dynamics.},
  keywords = {Bass diffusion, Phased planning, Service design, Shared micromobility, Social influence},
}