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Master’s in Business Analytics Engineering for Mobility Operations

Online master's programme of 1,900 hours in business analytics engineering for mobility operations. 12 modules covering mobility data, GTFS, NeTEx and SIRI standards, operations analytics, demand forecasting, network optimisation, geospatial analytics, fleet management, fares and scenario simulation, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Identify and manage mobility data sources.

02

Apply the GTFS, NeTEx and SIRI standards.

03

Build operational indicators and dashboards.

04

Develop demand forecasting models.

05

Optimise networks, timetables and resources using operations research.

06

Simulate scenarios and assess their economic and environmental impact.

07

Support management decisions with business analytics.

Profile

Who it's for

  • Mobility data analysts working with validation, GPS or sharing-platform data.
  • Transport planners designing networks, timetables and services.
  • Fleet operations managers looking to apply predictive and optimisation methods.
  • Transport data scientists building forecasting and routing models.
  • Mobility consultants advising operators and public authorities.
  • Operations managers at bus, rail or shared mobility operators.
  • Engineering or science graduates seeking to enter public transport.
Standards & tools

What you'll work with

  • Delegated Regulation (EU) 2017/1926 (EU-wide multimodal travel information services)
  • EN 12896 Transmodel (public transport reference data model)
  • CEN/TS 16614 NeTEx (network and timetable exchange)
  • Regulation (EU) 2016/679 (General Data Protection Regulation)
  • CEN/TS 15531 SIRI (real-time service interface)
  • EN 13816:2002 (public passenger transport service quality)
  • ISO 14083:2023 (greenhouse gas emissions of transport chains)
  • ISO 39001:2012 (road traffic safety management)
  • Python with pandas
  • QGIS
  • SUMO
  • Google OR-Tools
  • Power BI
Syllabus

12 modules · 1900 h

  1. 1.1Transport modes and trip chains
  2. 1.2Public and private transport operators
  3. 1.3Shared and on-demand mobility
  4. 1.4Mobility data sources
  5. 1.5Ticket validations, GPS and automatic counts
  6. 1.6Mobile phone and smart card data
  7. 1.7Quality and bias in mobility data
  8. 1.8Trajectory privacy
  9. 1.9Key operational indicators
  10. 1.10Case study: data inventory of an urban operator
  1. 2.1Static GTFS and GTFS Realtime
  2. 2.2The European Transmodel model
  3. 2.3NeTEx for timetables and fares
  4. 2.4SIRI for real-time information
  5. 2.5MDS for micromobility
  6. 2.6National multimodal access point
  7. 2.7Open transport data
  8. 2.8Interoperability between operators
  9. 2.9Feed validation
  10. 2.10Lab: validating a GTFS feed
  1. 3.1Service punctuality and regularity
  2. 3.2Occupancy and load per section
  3. 3.3Origin-destination matrices from validations
  4. 3.4Travel times and commercial speed
  5. 3.5Transfer analysis
  6. 3.6Operational dashboards
  7. 3.7Geospatial visualisation of indicators
  8. 3.8Comparisons between lines and periods
  9. 3.9EN 13816 quality indicators
  10. 3.10Practical exercise: punctuality dashboard in Power BI
  1. 4.1Classical ARIMA time series
  2. 4.2Machine learning models
  3. 4.3Calendar and event effects
  4. 4.4Influence of weather variables
  5. 4.5Demand in shared mobility
  6. 4.6Hierarchical forecasting by line and stop
  7. 4.7Evaluating prediction errors
  8. 4.8Real-time forecasting
  9. 4.9Managing uncertainty
  10. 4.10Lab: demand forecasting with Python
  1. 5.1Line network design
  2. 5.2Timetable scheduling
  3. 5.3Vehicle and block assignment
  4. 5.4Driver rostering
  5. 5.5Rebalancing shared fleets
  6. 5.6On-demand vehicle routing
  7. 5.7Integer linear programming
  8. 5.8Heuristics and metaheuristics
  9. 5.9Commercial and open-source solvers
  10. 5.10Workshop: shift optimisation with OR-Tools
  1. 6.1Reference systems and projections
  2. 6.2PostGIS spatial databases
  3. 6.3Coverage and accessibility analysis
  4. 6.4Isochrones and catchment areas
  5. 6.5Heat maps and hexagonal aggregation
  6. 6.6Map matching of GPS traces
  7. 6.7Road networks with OpenStreetMap
  8. 6.8Territorial equity analysis
  9. 6.9Visualisation with QGIS
  10. 6.10Practical exercise: transport accessibility study in QGIS
  1. 7.1Telemetry and vehicle data
  2. 7.2Availability and reliability indicators
  3. 7.3Predictive maintenance of buses
  4. 7.4Failure and root cause analysis
  5. 7.5Energy consumption and efficiency
  6. 7.6Fleet electrification
  7. 7.7Charging planning
  8. 7.8Total cost of ownership
  9. 7.9Spare parts management
  10. 7.10Case study: predictive maintenance in a bus fleet
  1. 8.1Ticketing and validation systems
  2. 8.2Price elasticity of demand
  3. 8.3Integrated fare design
  4. 8.4Fare evasion and access control
  5. 8.5Customer segmentation
  6. 8.6Satisfaction and surveys
  7. 8.7Loyalty and travel passes
  8. 8.8Mobility as a Service
  9. 8.9Revenue sharing between operators
  10. 8.10Workshop: simulating a fare change
  1. 9.1Four-stage models
  2. 9.2Agent-based models
  3. 9.3Simulation with MATSim and SUMO
  4. 9.4Traffic microsimulation
  5. 9.5Evaluating new services
  6. 9.6Cost-benefit analysis of alternatives
  7. 9.7Environmental impact of scenarios
  8. 9.8Carbon footprint according to ISO 14083
  9. 9.9Communicating results
  10. 9.10Practical exercise: simulating a new line with SUMO
  1. 10.1Data architecture of an operator
  2. 10.2Real-time data ingestion
  3. 10.3Data warehouses and data lakes
  4. 10.4Data quality and governance
  5. 10.5APIs for third parties
  6. 10.6Security and data protection
  7. 10.7Business intelligence tools
  8. 10.8Model deployment
  9. 10.9Costs and scalability
  10. 10.10Lab: data architecture for an operator
  1. 11.1Analytics culture in operators
  2. 11.2Communicating with public decision-makers
  3. 11.3Service contracts and indicators
  4. 11.4Evaluating mobility policies
  5. 11.5Ethics in the use of passenger data
  6. 11.6Change management
  7. 11.7Prioritising analytics initiatives
  8. 11.8Return on analytics investment
  9. 11.9Organising teams
  10. 11.10Case study: report to a transport authority
  1. 12.1Identifying the operational problem
  2. 12.2Data collection and preparation
  3. 12.3Exploratory data analysis
  4. 12.4Model development
  5. 12.5Evaluating results
  6. 12.6Operational improvement proposal
  7. 12.7Estimating economic impact
  8. 12.8Implementation plan
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is delivered 100% online on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standards and applied exercises. The working software is Python with pandas, QGIS, SUMO, Google OR-Tools and Power BI, in free, academic or evaluation versions. Exercises are based on real procedures: analysing punctuality and occupancy on a bus network, forecasting demand for a shared mobility service and optimising timetables and driver shifts. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Delegated Regulation (EU) 2017/1926, EN 12896 (Transmodel), CEN/TS 16614 (NeTEx), Regulation (EU) 2016/679 (GDPR), CEN/TS 15531 (SIRI), EN 13816:2002, ISO 14083:2023 and ISO 39001:2012.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Delegated Regulation (EU) 2017/1926, Transmodel and NeTEx applied to real cases, not as a theoretical annex.
Industry software:Python with pandas, QGIS, SUMO, Google OR-Tools and Power BI, in free, academic or evaluation versions.
Master's Thesis with defence:an analytics project on a real operational problem, with models, an improvement proposal and an economic impact estimate, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

GTFS feed validation
Validate and analyse a GTFS feed from a real operator.
Punctuality dashboard
Build a punctuality and occupancy dashboard in Power BI.
Demand forecasting model
Develop a demand forecasting model in Python for lines and stops.
Shift and line optimisation
Optimise driver shifts with OR-Tools and simulate a new line with SUMO.
Master's Thesis
An analytics solution to a real operational problem, with an improvement proposal and economic impact estimate, defended before a panel.
Career opportunities

Where this takes you

Mobility data analystTransport plannerFleet operations managerTransport data scientistMobility consultantOperations manager
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python with pandas, QGIS, SUMO, Google OR-Tools and Power BI, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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