Postgraduate Diploma in Sharing Operations and Urban Data
Online 800-hour Postgraduate Diploma in Sharing Operations and Urban Data. 8 modules covering Regulation (EU) 2016/679, the General Bikeshare Feed Specification (GBFS) and Python. Assigned tutor and open enrolment.
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Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Analyse the business models of shared mobility.
Organise the daily operation of shared fleets.
Manage mobility data using GBFS and MDS standards.
Forecast demand using machine learning techniques.
Optimise fleet rebalancing and operator logistics.
Apply the GDPR and municipal regulation to data processing.
Assess the sustainability of shared mobility services.
Who it's for
- Shared mobility operations manager who wants to specialise in Sharing Operations and Urban Data.
- Urban mobility data scientist who wants to specialise in Sharing Operations and Urban Data.
- Mobility analyst for public administrations who wants to specialise in Sharing Operations and Urban Data.
- Sharing fleet manager who wants to specialise in Sharing Operations and Urban Data.
- Mobility data privacy specialist who wants to specialise in Sharing Operations and Urban Data.
- Sustainable urban mobility consultant who wants to specialise in Sharing Operations and Urban Data.
- Engineering or science graduates looking to enter shared mobility.
What you'll work with
- Regulation (EU) 2016/679
- General Bikeshare Feed Specification (GBFS)
- Mobility Data Specification (MDS)
- Regulation (EU) 2023/2854
- Delegated Regulation (EU) 2017/1926
- ISO 37120
- ISO/IEC 27001
- Law 7/2021 on Climate Change and Energy Transition
8 modules · 800 h
1.1Carsharing, motosharing and bikesharing1.2Free-floating services versus fixed stations1.3Shared e-scooters1.4Operators and market structure1.5Sharing business models1.6Municipal regulation of the service1.7Integration with public transport1.8Mobility as a Service (MaaS)1.9Environmental impact of shared mobility1.10Case study: analysis of an urban service
2.1Rebalancing vehicles between zones2.2Charging management and battery swapping2.3Fleet maintenance and cleaning2.4Damage and vandalism management2.5Street operator logistics2.6Service zones and geofencing2.7Customer service2.8Road safety for users2.9Operational service indicators2.10Practice: daily rebalancing plan
3.1Data sources: GPS, apps and sensors3.2The GBFS open specification3.3The Mobility Data Specification (MDS)3.4Open urban traffic data3.5Data quality and cleansing3.6Trajectory anonymisation techniques3.7Data storage and processing3.8Geospatial visualisation of trips3.9Mobility data governance3.10Laboratory: analysis of a GBFS feed
4.1Spatiotemporal usage patterns4.2Clustering of trips and zones4.3Origin-destination trip matrices4.4Demand forecasting with machine learning4.5Time series models4.6Effect of weather and events4.7Demand-based dynamic pricing4.8Price elasticity of demand4.9Evaluation of forecasting models4.10Laboratory: demand forecasting in Python
5.1Formulating the rebalancing problem5.2Operator route optimisation5.3Optimal station location5.4Fleet size sizing5.5Battery charging scheduling5.6Discrete event simulation5.7Heuristic optimisation algorithms5.8Available optimisation tools5.9Operational efficiency indicators5.10Workshop: rebalancing optimisation with OR-Tools
6.1Applying the GDPR to mobility6.2Location data and associated risks6.3Data sharing agreements with city councils6.4Municipal licences and tenders6.5Management of urban public space6.6The European Data Act6.7Ethics in data use6.8Algorithmic transparency6.9Smart city urban platforms6.10Case study: data agreement with a city council
7.1Shared electric fleets7.2Charging infrastructure for sharing7.3Durability of shared vehicles7.4Life cycle analysis of the service7.5Circular economy and refurbishment7.6Service sustainability indicators7.7Modal shift and rebound effects7.8Operator ESG reporting7.9Continuous improvement of environmental impact7.10Practice: environmental impact assessment
8.1Market and city analysis8.2Operational service design8.3Data architecture8.4Demand forecasting model8.5Operational optimisation plan8.6Privacy framework and agreements8.7Sustainability assessment8.8Service business plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python, QGIS, Google OR-Tools, PostGIS, Kepler.gl, in free, academic or evaluation versions. Tests are studied using real operating procedures for e-scooter and shared bicycle services, analysis of mobility data for city councils, and optimisation of shared fleet rebalancing. Assessment is continuous, module by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Analyse the business models of shared mobility.
Organise the daily operation of shared fleets.
Manage mobility data using GBFS and MDS standards.
Forecast demand using machine learning techniques.
Optimise fleet rebalancing and operator logistics.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient command of Spanish to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with full-time work.
The programme uses Python, QGIS, Google OR-Tools, PostGIS and Kepler.gl. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrative project. This is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or diploma in the same area can be credited towards the advanced programme. Please consult the credit recognition policy when enrolling.
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