Data-Driven Modelling and Impact Assessment for Energy-Efficient Urban Public Transport and Shared Mobility

Okan University brings strong interdisciplinary expertise in sustainable urban mobility, energy-efficient transport systems and shared mobility integration, with a particular focus on micromobility and public transport complementarities.

Our core strength lies in data-driven modelling and advanced analytics, including machine learning-based mode choice and preference models, behavioural analysis, and scenario-based simulations. We have extensive experience in analysing how shared mobility services (e-scooters, shared bikes, MaaS solutions) interact with urban and sub-urban public transport systems, and how these interactions affect energy efficiency, emissions, accessibility and modal shift.

Within recent national and international research projects, Okan University has developed and validated high-accuracy predictive models using methods such as Artificial Neural Networks, Random Forests, Gradient Boosting and decision-tree-based approaches. These models are used to generate synthetic populations, support traffic and mobility simulations, and assess future policy and operational scenarios. We also have experience in pilot design, evaluation frameworks, KPI definition, and impact assessment aligned with the needs of public authorities and transport operators.

Okan University can contribute to Horizon Europe projects by:

• Developing behavioural and demand models for shared and public transport integration

• Assessing energy, emission and system-level impacts of innovative mobility solutions

• Supporting pilot demonstrations through simulations and ex-ante/ex-post evaluations

• Designing policy-oriented decision-support tools for cities and regions

• Contributing to WPs on modelling, impact assessment, validation and policy uptake

We are particularly interested in collaborating with cities, public transport operators, shared mobility providers and technology partners, and we are open to taking active roles in technical Work Packages related to modelling, evaluation and decision support.