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Course in Risk Modelling in Smart Urban Mobility

Online 300-hour course in risk modelling in smart urban mobility. 6 modules covering Directive 2010/40/EU (Intelligent Transport Systems), Regulation (EU) 2019/2144 (General Vehicle Safety) and Python (geopandas). Assigned tutor and open enrolment.

Duration300 h
ModeHybrid
LanguageEnglish / Spanish
Start datesOctober / March
LocationEurope (Madrid)
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Characterise the risks of smart urban mobility.

02

Integrate mobility, traffic and accident data.

03

Apply statistical and machine learning models to risk.

04

Simulate scenarios with connected and automated vehicles.

05

Apply the models to pricing and prevention.

06

Assess ethical and data governance implications.

07

Develop a risk model for a real mobility service.

Profile

Who it's for

  • Mobility risk modeller wishing to specialise in Risk Modelling in Smart Urban Mobility.
  • Mobility insurance actuary wishing to specialise in Risk Modelling in Smart Urban Mobility.
  • Road safety analyst wishing to specialise in Risk Modelling in Smart Urban Mobility.
  • Urban data scientist wishing to specialise in Risk Modelling in Smart Urban Mobility.
  • Shared mobility product manager wishing to specialise in Risk Modelling in Smart Urban Mobility.
  • Smart cities consultant wishing to specialise in Risk Modelling in Smart Urban Mobility.
  • Engineering or science graduates seeking to enter mobility insurance.
Standards & tools

What you'll work with

  • Directive 2010/40/EU (Intelligent Transport Systems)
  • Regulation (EU) 2019/2144 (General Vehicle Safety)
  • ISO 39001 (Road Traffic Safety Management)
  • Real Decreto 970/2020 (personal mobility vehicles)
  • Real Decreto Legislativo 8/2004 (motor vehicle insurance)
  • SAE J3016 (levels of automation)
  • Regulation (EU) 2024/1689 on artificial intelligence
Syllabus

6 modules · 300 h

  1. 1.1Components of the connected mobility system
  2. 1.2Vehicles, infrastructure and platforms
  3. 1.3Data generated and its use
  4. 1.4New modes and their coexistence
  5. 1.5Public and private stakeholders
  6. 1.6Emerging risks in the system
  7. 1.7Sector vocabulary and acronyms
  8. 1.8Differences from conventional mobility risk
  9. 1.9Available data sources
  10. 1.10Exercise: system map of a city
  1. 2.1Accident data and its quality
  2. 2.2Traffic and occupancy data
  3. 2.3Mobility platform data
  4. 2.4Weather and calendar data
  5. 2.5Infrastructure and roadworks data
  6. 2.6Geolocation and its accuracy
  7. 2.7Merging sources and standardisation
  8. 2.8Biases in available data
  9. 2.9Data protection and anonymisation
  10. 2.10Practice: preparing a dataset
  1. 3.1Frequency and severity models
  2. 3.2Spatial models and black spots
  3. 3.3Temporal models and seasonality
  4. 3.4Machine learning and its limits
  5. 3.5Validation and overfitting
  6. 3.6Model explainability
  7. 3.7Urban traffic simulation
  8. 3.8Mobility change scenarios
  9. 3.9Prediction uncertainty
  10. 3.10Practice: frequency model by zone
  1. 4.1Pricing by zone and by use
  2. 4.2Prevention and action on black spots
  3. 4.3Product design for operators
  4. 4.4Data-driven municipal planning
  5. 4.5Evaluation of road safety measures
  6. 4.6Shared fleet management
  7. 4.7Real-time alerts
  8. 4.8Assessing the effect of new modes
  9. 4.9Communicating results to the city
  10. 4.10Practice: intervention proposal for a black spot
  1. 5.1Territorial equity in price and service
  2. 5.2Risk of penalising disadvantaged neighbourhoods
  3. 5.3Model transparency
  4. 5.4Applicable artificial intelligence regulation
  5. 5.5User data protection
  6. 5.6Data agreements with the municipality
  7. 5.7Model governance and review
  8. 5.8Public communication of findings
  9. 5.9Collaboration between insurers and the city
  10. 5.10Practice: data collaboration agreement
  1. 6.1Choosing the city and scope
  2. 6.2Dataset design
  3. 6.3Model construction and validation
  4. 6.4Application to a specific decision
  5. 6.5Ethical and equity assessment
  6. 6.6Municipal collaboration proposal
  7. 6.7Monitoring indicators
  8. 6.8Implementation plan
  9. 6.9Methodological report
  10. 6.10Project defence
Methodology, software & labs

How you'll learn

100 % online training on the SEIUM Institute campus, with downloadable material, recorded classes and assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, making it easy to combine with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (geopandas), SUMO (traffic simulation), QGIS, R (mgcv), in free, academic or evaluation versions. Tests are studied using real procedures for micromobility accident models, traffic scenario simulation and spatial analysis of black spots. Assessment is continuous by module plus the final integrating project.

Accreditable laboratories(EN ISO/IEC 17025) with rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented final project:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format:(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Capstone project

Build it end to end

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

Choosing the city and scope

Characterise the risks of smart urban mobility.

Dataset design

Integrate mobility, traffic and accident data.

Model construction and validation

Apply statistical and machine learning models to risk.

Application to a specific decision

Simulate scenarios with connected and automated vehicles.

Ethical and equity assessment

Apply the models to pricing and prevention.

Career opportunities

Where this takes you

Mobility risk modellerMobility insurance actuaryRoad safety analystUrban data scientistShared mobility product managerSmart cities consultant
Admissions & scholarships

Requirements

  • No prior degree required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish to follow technical regulatory reading.

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 comprises 300 total hours spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

The course uses Python (geopandas), SUMO (traffic simulation), QGIS, R (mgcv). Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An in-house diploma from SEIUM Institute upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry qualification.

Yes. Modules passed in a course or postgraduate diploma in the same field are recognised in the advanced programme. Consult the recognition policy when enrolling.

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