Sara Momen is a researcher at ORBIT Lab, working on resilient and sustainable transport and logistics systems. She holds an MSc in Industrial Engineering from the University of Tehran and has undertaken doctoral research at Delft University of Technology on electrified multimodal transport planning and operations under uncertainty.
Her research integrates mathematical optimization, data-driven uncertainty modeling, and behavioural considerations into transportation and logistics decision-making. Her doctoral work addresses charging infrastructure design for electric buses, operational management of shared electric micromobility, and reliable multimodal trip planning. She is currently contributing to the ReSolVe project, which investigates resilient and regulatory-ready supply chain design.
Transportation Research Part C: Emerging Technologies2027
BibTeX
% VERIFIED; automatic policy
@article{orbit-auto-b8612ceceddbc9f0,
title = {Hybrid predictive rebalancing control of perceived usability in shared e-scooter systems},
author = {Momen, Sara and Maknoon, Yousef and van Arem, Bart and Sharif Azadeh, Shadi},
journal = {Transportation Research Part C: Emerging Technologies},
year = {2027},
volume = {194},
pages = {106093},
eid = {106093},
doi = {10.1016/j.trc.2026.106093},
url = {https://doi.org/10.1016/j.trc.2026.106093},
}
Transportation Research Part D: Transport and Environment2025
BibTeX
% VERIFIED; verified 2026-10-11
@article{orbit-fa571a0660cd53ee,
title = {Behavioral impact of range anxiety and unlock fees on shared electric-moped usage},
author = {Burghardt, Sören Paul and Momen, Sara and Maknoon, Yousef and Azadeh, Shadi Sharif and Kavta, Kuldeep},
journal = {Transportation Research Part D: Transport and Environment},
year = {2025},
volume = {145},
pages = {104772},
eid = {104772},
doi = {10.1016/j.trd.2025.104772},
url = {https://doi.org/10.1016/j.trd.2025.104772},
issn = {1361-9209, 1879-2340},
publisher = {Elsevier BV},
abstract = {The evolving field of electric moped sharing systems is shaped by various determinants influencing user preferences, including range anxiety, pricing strategies, and regulatory changes. Utilizing a stated preference approach with a hybrid choice model, this research explores how these factors, along with attitudinal constructs, impact user decisions. The findings reveal that remaining driving range plays a critical role, with significant individual variability in its sensitivity, while perceived range anxiety did not significantly influence choices. Recent changes in helmet regulations have shifted preferences towards faster vehicles. Furthermore, dynamic pricing strategies, such as adjusting ride or unlock fees, can incentivize the use of less desirable vehicles with lower battery range or aid in user-based relocation. Nevertheless, low-range vehicles are less likely to be chosen, even with incentives. These insights provide valuable guidance for operators of electric moped sharing system to improve fleet management and optimize user satisfaction through strategic pricing and battery management.},
keywords = {Electric moped sharing systems, Hybrid choice model, Pricing, Range anxiety, Shared micromobility, Stated preferences},
}
Transportation Research Part C: Emerging Technologies2025
BibTeX
% VERIFIED; verified 2026-10-11
@article{orbit-2caf7c2d102c189f,
title = {Designing a robust and cost-efficient electrified bus network with sparse energy consumption data},
author = {Momen, Sara and Maknoon, Yousef and van Arem, Bart and Azadeh, Shadi Sharif},
journal = {Transportation Research Part C: Emerging Technologies},
year = {2025},
volume = {171},
pages = {105020},
eid = {105020},
doi = {10.1016/j.trc.2025.105020},
url = {https://doi.org/10.1016/j.trc.2025.105020},
issn = {0968-090X, 1879-2359},
publisher = {Elsevier BV},
abstract = {This paper addresses the challenges of charging infrastructure design (CID) for electrified public transport networks using Battery Electric Buses (BEBs) under conditions of sparse energy consumption data. Accurate energy consumption estimation is critical for cost-effective and reliable electrification but often requires costly field experiments, resulting in limited data. To address this issue, we propose two mathematical models designed to handle uncertainty and data sparsity in energy consumption. The first is a robust optimization model with box uncertainty, addressing variability in energy consumption. The second is a data-driven distributionally robust optimization model that leverages observed data to provide more flexible and informed solutions. To evaluate these models, we apply them to the Rotterdam bus network. Our analysis reveals three key insights: (1) Ignoring variations in energy consumption can result in operational unreliability, with up to 55\% of scenarios leading to infeasible trips. (2) Designing infrastructure based on worst-case energy consumption increases costs by 67\% compared to using average estimates. (3) The data-driven distributionally robust optimization model reduces costs by 28\% compared to the box uncertainty model while maintaining reliability, especially in scenarios where extreme energy consumption values are rare and data exhibit skewness. In addition to cost savings, this approach provides robust protection against uncertainty, ensuring reliable operation under diverse conditions.},
keywords = {Distributionally robust optimization, Energy consumption variability, Minimum cost deployment, Operational reliability with sparse data, Public transport electrification},
}
