ORBIT
Orbit · TU Delft TPM

Computational Models for Behavioral Decision-Making in Transportation

Operations research & behavioural informatics for urban mobility and logistics.
Fig. 01 · choice-response surface
after Sharif Azadeh et al., 2022
price →↑ wait timeP(accept) = 0.83
P(accept | price, wait)
The probability a traveller accepts a demand-responsive offer as price and wait time vary. The dashed track follows one rider crossing the field of plausible offers; the terracotta mark is the accepted itinerary.
Pillar 01
Orbit advances the science of decision-making with a focus on behavioural informatics — building models, algorithms, and hands-on tools that put human behaviour inside the optimization, for urban logistics and transportation planning.
01
Research
Three pillars — each anchored by a figure.
requestacceptreject
01 · MOBILITY & LOGISTICS
Integrative urban mobility & logistics systems
Choice models embedded inside routing and matching. Demand-responsive mobility, ride-sharing, carpooling, and crowd-shipping — designed so the optimization respects how travellers actually decide.
tdock-time →ETA-driven slots
02 · DECISION SUPPORT
Decision-support tools for urban planning
Predictive–proactive decision tools for operators: ETA-informed slot management, incentive design for congestion, and platform mechanisms that hold up under real demand.
capacitycharge cycles →fast-charge
03 · SUSTAINABILITY
Sustainable transportation solutions
Methods for the energy transition in transport: bus-fleet electrification that accounts for battery degradation, EV-charging incentives, and the trade-offs operators face when greening a network.
02
Selected output
Recent work in the literature.
All publications →
Get involved
Work with the lab — as a student or a partner.
Students
Master theses across all three pillars; rolling intake.
Companies
Applied projects turning operational problems into methods.