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CourseCivil, Rail & Smart Cities Applications open

AI-powered Predictive Mobility Modeling Course

Online course of 300 hours in AI predictive models for mobility. 6 modules covering data and prediction problems, statistical models and machine learning, deep learning for mobility, prediction applications, deployment and operation, and a final project, with an assigned technical tutor.

Duration3 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Formulate mobility prediction problems with real data.

02

Apply statistical and machine learning models to traffic series.

03

Develop spatio-temporal deep learning models.

04

Evaluate models with appropriate metrics and validation.

05

Interpret results to support management decisions.

06

Deploy and monitor models in operational environments.

07

Meet the ethical and regulatory requirements for artificial intelligence.

Profile

Who it's for

  • Mobility data scientists.
  • Machine learning engineers moving into transport applications.
  • Transport analysts who want to add predictive modelling to their toolkit.
  • Urban AI consultants.
  • MLOps engineers.
  • Transport planning officers.
  • Engineering or science graduates aiming to join transport operators.
Standards & tools

What you'll work with

  • Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
  • Regulation (EU) 2016/679 (General Data Protection Regulation)
  • ISO/IEC 23894 (AI risk management)
  • ISO/IEC 42001 (AI management systems)
  • Directive (EU) 2019/1024 (open data and re-use of public sector information)
  • Delegated Regulation (EU) 2022/670 (EU-wide real-time traffic information services)
  • Python (scikit-learn)
  • PyTorch
  • XGBoost
  • MLflow
  • Apache Spark
Syllabus

6 modules

  1. 1.1Types of prediction in mobility
  2. 1.2Short-, medium- and long-term horizons
  3. 1.3Traffic and transport data sources
  4. 1.4Mobile phone, GPS and ticket validation data
  5. 1.5Preparing time series
  6. 1.6Exogenous variables: weather, events and calendar
  7. 1.7Representing networks as graphs
  8. 1.8Data bias and representativeness
  9. 1.9Ethics and privacy in mobility data
  10. 1.10Case study: data inventory of a city
  1. 2.1ARIMA and SARIMA models
  2. 2.2Regression and generalised linear models
  3. 2.3Decision trees and random forests
  4. 2.4Gradient boosting with XGBoost and LightGBM
  5. 2.5Cross-validation for time series
  6. 2.6Temporal feature engineering
  7. 2.7MAE, RMSE and MAPE metrics
  8. 2.8Interpretability with SHAP
  9. 2.9Probabilistic forecasting and intervals
  10. 2.10Lab: traffic count prediction with XGBoost
  1. 3.1LSTM and GRU recurrent networks
  2. 3.2Temporal convolutional networks
  3. 3.3Graph neural networks
  4. 3.4DCRNN and STGCN spatio-temporal models
  5. 3.5Transformers for time series
  6. 3.6METR-LA and PeMS benchmark datasets
  7. 3.7Transfer learning between cities
  8. 3.8Training and hyperparameter tuning
  9. 3.9Computational cost and efficiency
  10. 3.10Practical: LSTM network for traffic speed
  1. 4.1Public transport demand forecasting
  2. 4.2Vehicle and station occupancy
  3. 4.3Bike-sharing demand
  4. 4.4Journey times and congestion
  5. 4.5Incident detection
  6. 4.6Mode choice with machine learning
  7. 4.7Electric vehicle charging demand
  8. 4.8Parking occupancy prediction
  9. 4.9Generating origin-destination matrices
  10. 4.10Case study: hourly demand at bike-sharing stations
  1. 5.1The MLOps life cycle
  2. 5.2Real-time data pipelines
  3. 5.3Monitoring data and model drift
  4. 5.4Retraining and versioning
  5. 5.5Integration with control centres
  6. 5.6Explainability for public managers
  7. 5.7Requirements of the EU AI Act
  8. 5.8Assessing algorithmic fairness
  9. 5.9Documentation and model cards
  10. 5.10Workshop: deploying a model with MLflow
  1. 6.1Problem formulation
  2. 6.2Data acquisition and cleaning
  3. 6.3Exploratory data analysis
  4. 6.4Model selection and training
  5. 6.5Validation and comparison
  6. 6.6Interpreting results
  7. 6.7Deployment proposal
  8. 6.8Conclusions for management
  9. 6.9Report and presentation
  10. 6.10Final project: final review with your tutor
Methodology, software & labs

How you'll learn

Delivered 100% online 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 the programme fits around full-time work.

Each module combines theory, guided reading of the original standards and applied exercises. The working software is Python (scikit-learn), PyTorch, XGBoost, MLflow and Apache Spark, in free, academic or evaluation versions. Exercises are based on real procedures: demand forecasting with real data, model comparison on benchmark datasets and model deployment in test environments. Assessment is continuous by module, plus a final capstone project.

Standards covered: Regulation (EU) 2024/1689; Regulation (EU) 2016/679; ISO/IEC 23894; ISO/IEC 42001; Directive (EU) 2019/1024; Delegated Regulation (EU) 2022/670.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2024/1689 (AI Act), Regulation (EU) 2016/679 (GDPR) and ISO/IEC 23894 applied to real cases, not as a theoretical annex.
Industry software:Python (scikit-learn), PyTorch, XGBoost, MLflow and Apache Spark, in free, academic or evaluation versions.
Final project:a complete mobility predictive model, from problem formulation and data preparation to model validation, interpretation and a deployment proposal, reviewed by your tutor.
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.

Problem and data
Formulate the prediction problem and acquire and clean the data.
Exploratory analysis
Explore the data and engineer the temporal and exogenous features.
Model training
Select, train and tune statistical, machine learning and deep learning models.
Validation and interpretation
Compare the models with suitable metrics and interpret the results with SHAP.
Final project
Present the chosen model with its deployment proposal and management conclusions, reviewed by your tutor.
Career opportunities

Where this takes you

Mobility data scientistMachine learning engineerTransport analystUrban AI consultantMLOps engineerTransport planning officer
Admissions & scholarships

Requirements

  • No prior qualification is required.
  • 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 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python (scikit-learn), PyTorch, XGBoost, MLflow and Apache Spark, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

No prior qualification is required: you only need an internet connection, a computer capable of running 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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