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Master’s in Traffic and Mobility Twins Engineering

Online master's programme of 1,900 hours in traffic and mobility digital twin engineering. 12 modules covering traffic engineering, traffic models, mobility data, real-time traffic twins, AI-based prediction, traffic control, public transport, connected and automated vehicles, mobility planning, emissions and road safety, and data governance, 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

Analyse the capacity and level of service of road infrastructure.

02

Build and calibrate traffic models at different scales.

03

Fuse mobility data sources.

04

Develop real-time traffic digital twins.

05

Predict traffic with machine learning models.

06

Evaluate traffic management and control strategies.

07

Assess the environmental and safety impacts of mobility scenarios.

Profile

Who it's for

  • Traffic engineers who want to build live, data-driven models of their networks.
  • Transport modellers moving from static studies to real-time twins.
  • Mobility twin engineers working on urban and interurban networks.
  • Mobility data analysts handling sensor, floating car and public transport data.
  • Transport planners evaluating policies such as low-emission zones.
  • ITS systems engineers working on traffic management and C-ITS.
  • Engineering or science graduates aiming to work in local government.
Standards & tools

What you'll work with

  • Directive 2010/40/EU (deployment of Intelligent Transport Systems)
  • Delegated Regulation (EU) 2017/1926 (EU-wide multimodal travel information services)
  • CEN/TS 16157 (DATEX II traffic data exchange)
  • UNE-EN 12896 (Transmodel public transport reference data model)
  • CEN/TS 16614 (NeTEx public transport network and timetable exchange)
  • Regulation (EU) 2016/679 (GDPR)
  • ETSI EN 302 637-2 (C-ITS cooperative awareness basic service)
  • SUMO
  • PTV Vissim
  • Aimsun Next
  • Python with PyTorch Geometric
  • QGIS
Syllabus

12 modules · 1900 h

  1. 1.1Fundamental variables: flow, density and speed
  2. 1.2The fundamental diagram of traffic
  3. 1.3Capacity and level of service
  4. 1.4Capacity of signalised intersections
  5. 1.5Roundabouts and priority
  6. 1.6Motorways and weaving sections
  7. 1.7Shockwave theory
  8. 1.8Traffic counts and data collection
  9. 1.9Highway Capacity Manual (HCM)
  10. 1.10Case study: capacity analysis of an urban corridor
  1. 2.1LWR macroscopic models
  2. 2.2Cell transmission models
  3. 2.3Mesoscopic queue models
  4. 2.4Microscopic car-following models
  5. 2.5Lane-changing models
  6. 2.6Parameter calibration
  7. 2.7Traffic assignment and equilibrium
  8. 2.8Dynamic traffic assignment
  9. 2.9Comparison of modelling scales
  10. 2.10Lab: microscopic model of an intersection in SUMO
  1. 3.1Loop detectors and conventional sensors
  2. 3.2Cameras and computer vision
  3. 3.3Floating car data
  4. 3.4Mobile phone data
  5. 3.5Bluetooth and Wi-Fi
  6. 3.6Public transport data and ticket validations
  7. 3.7Shared bicycles and e-scooters
  8. 3.8Source fusion
  9. 3.9Data quality and privacy
  10. 3.10Practical exercise: fusion of loop detector and floating car data
  1. 4.1Architecture of a mobility twin
  2. 4.2Real-time state estimation
  3. 4.3Data assimilation in traffic models
  4. 4.4Dynamic origin-destination matrices
  5. 4.5Simulation synchronised with the real network
  6. 4.6Short-term prediction
  7. 4.7Incident scenarios
  8. 4.8Commercial and open platforms
  9. 4.9Scaling to metropolitan level
  10. 4.10Workshop: real-time twin of a corridor
  1. 5.1Statistical time-series models
  2. 5.2Recurrent networks for traffic
  3. 5.3Spatio-temporal graph networks
  4. 5.4Incident prediction
  5. 5.5Transport demand prediction
  6. 5.6Event and weather data
  7. 5.7Evaluation of prediction models
  8. 5.8Transfer between cities
  9. 5.9Explainability for traffic managers
  10. 5.10Lab: prediction with graph neural networks
  1. 6.1Fixed-time signal control
  2. 6.2Actuated and adaptive control
  3. 6.3Green wave coordination
  4. 6.4Motorway management and ramp metering
  5. 6.5Variable speed signage
  6. 6.6Public transport priority
  7. 6.7Reinforcement learning for traffic signals
  8. 6.8Traffic management centres
  9. 6.9Evaluating strategies in the twin
  10. 6.10Practical exercise: adaptive control evaluated in simulation
  1. 7.1Network and timetable planning
  2. 7.2Public transport simulation
  3. 7.3Interchanges and transfers
  4. 7.4Multimodal demand modelling
  5. 7.5Mobility as a Service
  6. 7.6Micromobility and shared vehicles
  7. 7.7Urban last-mile logistics
  8. 7.8Parking and demand management
  9. 7.9Accessibility and inclusion
  10. 7.10Case study: simulation of a new bus line
  1. 8.1C-ITS cooperative systems
  2. 8.2V2X communications between vehicles and infrastructure
  3. 8.3Impact of automation on capacity
  4. 8.4Truck platooning
  5. 8.5Simulation of autonomous fleets
  6. 8.6Robotaxis and shared mobility
  7. 8.7Digital road infrastructure
  8. 8.8Road safety and scenarios
  9. 8.9European ITS regulations
  10. 8.10Workshop: simulation of connected vehicle penetration
  1. 9.1Four-step models
  2. 9.2Activity-based models
  3. 9.3Travel surveys
  4. 9.4Sustainable urban mobility plans
  5. 9.5Low-emission zones
  6. 9.6Evaluation of future scenarios
  7. 9.7Cost-benefit analysis of interventions
  8. 9.8Public participation in planning
  9. 9.9Equity in mobility
  10. 9.10Practical exercise: evaluation of a low-emission zone
  1. 10.1Traffic emission models
  2. 10.2Energy consumption and electrification
  3. 10.3Traffic noise
  4. 10.4Urban air quality
  5. 10.5Accident analysis
  6. 10.6Surrogate safety models
  7. 10.7Conflicts in simulation
  8. 10.8Sustainability indicators
  9. 10.9Electric vehicle charging planning
  10. 10.10Lab: scenario emissions with SUMO
  1. 11.1Open mobility data
  2. 11.2National access point
  3. 11.3GTFS, NeTEx and DATEX II standards
  4. 11.4Urban data platforms
  5. 11.5Privacy of location data
  6. 11.6Cybersecurity of road infrastructure
  7. 11.7Procurement of intelligent systems
  8. 11.8Governance across administrations
  9. 11.9Interoperability of ITS systems
  10. 11.10Case study: mobility data architecture for a city
  1. 12.1Definition of the study area
  2. 12.2Data and characterisation of mobility
  3. 12.3Model construction
  4. 12.4Calibration and validation
  5. 12.5Real-time integration
  6. 12.6Scenarios and strategies
  7. 12.7Impact assessment
  8. 12.8Conclusions and mobility recommendations
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

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

Each module combines theory, guided reading of the original standard and applied exercises. The working software is SUMO, PTV Vissim, Aimsun Next, Python with PyTorch Geometric and QGIS, in free, academic or evaluation versions. Practical work is based on real procedures: microscopic simulation of urban corridors, real-time traffic prediction and evaluation of signal control strategies. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Directive 2010/40/EU, Delegated Regulation (EU) 2017/1926, CEN/TS 16157, UNE-EN 12896, CEN/TS 16614, Regulation (EU) 2016/679, ETSI EN 302 637-2.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Directive 2010/40/EU, Delegated Regulation (EU) 2017/1926 and CEN/TS 16157 (DATEX II) applied to real cases, not as a theoretical annex.
Industry software:SUMO, PTV Vissim, Aimsun Next, Python with PyTorch Geometric and QGIS, in free, academic or evaluation versions.
Master's Thesis with defence:A calibrated, real-time traffic and mobility twin of a real study area, with scenario and impact assessment, 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.

Model an intersection
Build and calibrate a microscopic intersection model in SUMO.
Fuse mobility data
Combine loop detector and floating car data to characterise demand.
Run a corridor twin
Synchronise a real-time corridor twin and forecast traffic with graph neural networks.
Evaluate control strategies
Test adaptive signal control and estimate scenario emissions in simulation.
Master's Thesis
A calibrated, real-time mobility twin with scenarios and impact assessment, defended before a panel.
Career opportunities

Where this takes you

Traffic engineerTransport modellerMobility twin engineerMobility data analystTransport plannerITS systems engineer
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 SUMO, PTV Vissim, Aimsun Next, Python with PyTorch Geometric and QGIS, 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 programme's 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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