AI-powered Predictive Mobility Modeling Course
About our
The Predictive Mobility Models with AI course focuses on the analysis and prediction of urban mobility behavior using artificial intelligence (AI). It explores machine learning (ML) and predictive modeling techniques applied to mobility data, including the analysis of traffic data, traffic patterns, and route optimization. Emphasis is placed on the implementation of models to predict transport demand, simulate future scenarios, and improve the efficiency of transport systems.
The course provides practical experience in the use of data analysis tools and AI platforms, as well as in data visualization for decision-making in the field of mobility. It focuses on the application of these techniques for the development of innovative solutions in areas such as urban planning, traffic management, and sustainable mobility, considering the impact of connectivity and the development of smart cities.
AI-powered Predictive Mobility Modeling Course
- Format: Online
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
649 $
Competencies and results
What you will learn
1. Mobility Prediction with AI: Advanced Models and Applications
- Master AI-based predictive mobility modeling techniques.
- Understand AI algorithms and architectures applied to trajectory and movement prediction.
- Evaluate and select appropriate AI models for different mobility scenarios.
- Apply AI models to predict the behavior of naval systems in motion.
- Analyze historical and real-time data to train and validate predictive models.
- Interpret model results and draw meaningful conclusions.
- Identify and mitigate risks associated with mobility and navigation.
- Develop innovative solutions to improve the efficiency and safety of naval operations.
2. Development of Predictive Mobility Models with Artificial Intelligence
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**2. Development of Predictive Mobility Models with Artificial Intelligence**
You will learn to:
- Apply AI techniques to predict trajectories and movement patterns.
- Use machine learning algorithms to detect mobility anomalies.
- Develop predictive models to optimize routes and resources.
- Implement AI systems for traffic and logistics management.
- Analyze mobility data to identify trends and improve planning.
- Integrate predictive models with navigation and control systems.
- Evaluate the impact of AI on urban mobility and transportation.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. AI in Mobility: Building High-Performance Predictive Models
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4. AI in Mobility: Building High-Performance Predictive Models
Master the use of machine learning algorithms to predict the behavior of moving vehicles.
Explore advanced data processing techniques to optimize the accuracy of predictive models.
Apply artificial intelligence to improve energy efficiency and road safety.
Build predictive models that simulate complex traffic and weather conditions.
Implement AI solutions for collision detection and prevention.
Learn to evaluate model performance and optimize them for high-impact results.
Use simulation and analysis tools to validate predictive models in real-world environments.
Understand the ethical and legal implications of AI in mobility.
Develop skills to communicate the results of predictive models effectively.
Explore future trends in AI for mobility, such as autonomous vehicles and smart cities.
5. Mastering Mobility Prediction: An AI-Powered Course
- Understand the fundamentals of artificial intelligence and machine learning applied to mobility prediction.
- Explore advanced data analysis techniques for identifying mobility patterns.
- Use AI-based predictive simulation tools to model mobility scenarios.
- Learn to interpret and visualize the results of AI-generated mobility predictions.
- Apply AI-based optimization strategies to improve mobility efficiency and safety.
- Evaluate the impact of AI on decision-making related to mobility planning and management.
- Develop skills for implementing and maintaining AI-based mobility prediction systems.
- Analyze case studies and practical examples of the application of AI in mobility prediction in various contexts.
Become familiar with the ethical and legal considerations related to the use of AI in mobility.
6. Construction of Predictive Mobility Models: AI for Optimization
- You will master the fundamentals of artificial intelligence (AI) applied to mobility.
- You will learn to collect, clean, and prepare mobility data for predictive analysis.
- You will develop predictive models using AI algorithms, such as neural networks and time series models.
- You will interpret the results of the models and extract valuable information about mobility patterns.
- You will apply predictive models to optimize transport planning, traffic management, and logistics.
- You will explore practical use cases, such as predicting public transport demand and optimizing delivery routes.
- You will understand the ethical and privacy challenges related to the use of AI in mobility.
- You will learn to evaluate and improve the accuracy and performance of predictive models.
- You will become familiar with AI tools and platforms for data analysis Mobility.
You will build robust and scalable predictive models, ready to be deployed in real-world environments.
Who this program is for:
AI-powered Predictive Mobility Modeling Course
- Engineers with degrees in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation, or related disciplines.
- Industry experts in key roles in OEMs of rotor/eVTOL aircraft, MRO (Maintenance, Repair, and Overhaul), consulting firms, and technology research centers.
- Professionals with experience in Flight Testing, Aeronautical Certification, Avionics, Control Systems, and Flight Dynamics who wish to deepen their knowledge.
- Officials and technical staff of Regulatory bodies and aeronautical authorities, as well as Urban Air Mobility (UAM) / eVTOL specialists who need to acquire skills in compliance.
Recommendations: Prior knowledge of aerodynamics, control systems, and structures is suggested. English (ES/EN) level B2+ or C1. Bridging tracks are provided to meet any specific training needs.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
1.1 Fundamentals of Mobility Prediction: Introduction to key concepts.
1.2 Relevant Data Types: Sources and characteristics of mobility data.
1.3 Introduction to Artificial Intelligence in Mobility: Algorithms and applications.
1.4 Data Preprocessing: Cleaning and preparation for analysis.
1.5 Evaluation Metrics: How to measure the performance of predictive models.
1.6 Tools and Platforms: Introduction to AI tools for mobility.
1.7 Ethics and Data Privacy: Important considerations in mobility prediction.
1.8 Introductory Case Studies: Examples of successful applications.
1.9 The Future of Predictive Mobility: Trends and perspectives.
1.10 Project Planning: Defining objectives and scope.
2. Introduction to Mobility Prediction: Foundations and Concepts
3. Data Types in Mobility Prediction: Sources and Characteristics
4. Data Preprocessing Techniques: Cleaning and Transformation
5. Feature Selection: Identifying Relevant Variables
6. Machine Learning Algorithms for Prediction: An Overview
7. Evaluating Predictive Models: Metrics and Validation
8. Overfitting and Underfitting: Strategies for Effective Modeling
9. Ethical Considerations in Mobility Prediction
20. Use Cases: Applications of Mobility Prediction
22. Future Trends in AI-Powered Mobility Prediction
3.3 Fundamentals of Artificial Intelligence: Key Concepts and Applications in Mobility.
3.2 The Role of AI in Mobility Prediction: Challenges and Opportunities.
3.3 Data Types in Mobility: Sources, Formats, and Preprocessing.
3.4 Introduction to Predictive Models: Basic Concepts and Model Types.
3.5 The Lifecycle of an AI Project for Mobility Prediction.
3.6 Essential Tools and Technologies for AI in Mobility.
3.7 Ethics and Privacy Considerations in AI Applied to Mobility.
3.8 Current and Future Trends in Mobility Prediction with AI.
3.9 Real-World Use Cases: Examples of AI Applications in Mobility.
3.30 Introduction to the Following Course Modules.
4.4 Key Concepts of Predictive Mobility: Definition, Objectives, and Applications
4.2 Introduction to Artificial Intelligence (AI) in Mobility: Relevant Types of AI
4.3 The Value of Prediction in Mobility: Benefits and Challenges
4.4 Current Landscape of Mobility: Trends and Opportunities
4.5 Data Collection and Preparation for Predictive Mobility
4.6 Tools and Platforms for Data Analysis and AI
4.7 Performance Metrics for Evaluating Predictive Models
4.8 Ethical Framework and Privacy Considerations in Predictive Mobility
4.9 Use Cases and Success Stories in Predictive Mobility
4.40 Introduction to the Workflow of a Predictive Mobility Project
5.5 Fundamentals of Artificial Intelligence and its Application in Mobility
5.5 Introduction to Data in Mobility: Sources and Types
5.3 The Role of AI in Transport and Logistics Optimization
5.4 Challenges and Opportunities in Implementing AI in Mobility
5.5 Ethics and Legal Considerations in the Use of AI in Mobility
5.6 Introduction to AI Tools and Platforms for Mobility
5.7 Case Study Analysis: AI in Public Transport, Private Transport, and Logistics
5.8 Future Trends and the Impact of AI on Sustainable Mobility
5.9 Introduction to Predictive Models and their Importance in Mobility
5.50 General Framework for the Predictive Model Workflow
6.6 Definition and Scope of Predictive Mobility
6.2 Importance of AI in Mobility Prediction
6.3 Types of Data Relevant to Predictive Models
6.4 Data Sources: Collection and Preparation
6.5 Key AI Concepts: Supervised and Unsupervised Learning
6.6 Introduction to Predictive Algorithms
6.7 Model Evaluation and Performance Metrics
6.8 Tools and Platforms for Model Development
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).