Urban Big Data and Traffic Prediction Course
About our
The Urban Big Data and Traffic Prediction Course explores the use of big data, machine learning, and predictive modeling to analyze and predict traffic patterns in urban environments. It focuses on the collection, processing, and analysis of data from various sources, such as traffic sensors, GPS data, and social media. Participants will learn to apply data science techniques to optimize traffic management, reduce congestion, and improve urban mobility, using tools such as Python and data visualization tools.
The course offers practical knowledge in implementing machine learning algorithms for traffic prediction, bottleneck identification, and scenario simulation. Topics related to intelligent traffic management, route optimization, and data-driven urban planning will be covered. The training equips professionals for roles in urban planning, traffic engineering, and data analytics, driving informed decision-making and improved efficiency in cities.
Urban Big Data and Traffic Prediction 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
249 $
Competencies and results
What you will learn
1. Urban Traffic Prediction: Big Data Analysis and Predictive Modeling
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Identify and understand relevant big data sources for urban traffic.
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Apply Big Data analytics techniques to clean, transform, and explore urban traffic datasets.
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Use Big Data tools and platforms for data processing and analysis.
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Select and apply appropriate predictive modeling algorithms for urban traffic forecasting, including time series models, machine learning, and neural networks.
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Train, validate, and evaluate predictive models using relevant performance metrics.
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Interpret and communicate the results of traffic prediction models, including identifying patterns and trends.
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Use predictive models for decision-making in urban traffic management, such as traffic signal optimization and route planning.
Explore and analyze real-world case studies of urban traffic prediction.
Understand the ethical and privacy considerations related to urban traffic data analysis.
2. Analysis of Big Urban Data to Predict Traffic Flow
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Identify and understand relevant urban big data sources for traffic flow analysis (sensors, GPS devices, social media, etc.).
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Apply descriptive analysis methods to characterize traffic flow (speeds, densities, volumes, travel times) and its spatiotemporal patterns.
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Use data-driven predictive models, such as time series, machine learning, and neural networks, to forecast short- and long-term traffic flow.
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Evaluate and validate the accuracy of predictive models using relevant metrics (MAE, RMSE, MAPE) and cross-validation techniques.
- Interpret the results of analyses and models to identify factors that influence traffic flow (events, weather, infrastructure, etc.) and draw meaningful conclusions.
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Implement data visualizations and interactive dashboards to effectively communicate findings and results to different audiences.
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Explore practical applications of traffic flow analysis in traffic management, urban planning, public transportation, and route optimization.
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Understand the ethical and privacy considerations related to the use of big urban data and traffic flow analysis.
Master big data processing and cleaning techniques, including managing missing data, outliers, and noise.
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. Unraveling Urban Big Data: Traffic Prediction with Advanced Models
4. Unraveling Urban Big Data: Traffic Prediction with Advanced Models
Understanding the sources of massive datasets in urban environments: traffic sensors, mobility data, social networks, and GPS systems.
Mastering data preprocessing and cleaning techniques to prepare complex datasets for analysis.
Applying supervised machine learning algorithms: linear regression, support vector machines, and neural networks for traffic prediction.
Exploring time series models: ARIMA, Prophet, and LSTM networks, to predict traffic patterns based on time.
Using unsupervised machine learning techniques: clustering to identify patterns and anomalies in traffic.
Implementing traffic simulation models to evaluate scenarios and optimize traffic management.
Analyze and visualize traffic data using advanced visualization tools for better understanding.
Evaluate and validate traffic prediction models using relevant performance metrics.
Understand the impact of special events and external factors on urban traffic and design mitigation strategies.
Apply knowledge to real-world use cases: route optimization, congestion management, and urban mobility planning.
5. Urban Traffic Prediction: Big Data Analysis and Advanced Predictive Modeling
- **Fundamentals of Traffic Prediction:** Understand the key concepts, terminology, and importance of urban traffic prediction.
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**Collection and Preparation of Big Data:** Learn to identify, collect, clean, and process big datasets relevant to traffic, including data from sensors, GPS, social media, and more.
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**Exploratory Data Analysis (EDA):** Use EDA techniques to explore and understand the characteristics of traffic data, identify patterns, trends, and anomalies.
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**Statistical Modeling and Machine Learning:** Develop predictive models using statistical techniques and machine learning algorithms (linear regression, decision trees, neural networks, etc.) to forecast traffic.
- **Real-World Applications and Case Studies:** Explore practical applications of urban traffic prediction, such as traffic light optimization, congestion management, public transport planning, and smart mobility.
**Time Series Models:** Apply specialized time series analysis models (ARIMA, Prophet, etc.) to predict traffic over time.
**Agent-Based Models and Simulation:** Learn to simulate traffic behavior using agent-based models and simulation techniques.
**Data and Model Integration:** Combine multiple data sources and predictive models to improve the accuracy and robustness of predictions.
**Model Evaluation and Validation:** Evaluate the performance of predictive models using relevant metrics (MAE, RMSE, etc.) and validate the results.
**Data Visualization and Results Communication:** Use visualization tools to effectively communicate prediction results.
6. Mastering Urban Big Data: Traffic Prediction and Vehicle Flow Optimization
6. Mastering Urban Big Data: Traffic Prediction and Vehicle Flow Optimization
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Urban Big Data Fundamentals: Introduction to key concepts, data sources (traffic sensors, GPS, social media, etc.), and the underlying technological infrastructure.
- Data Collection and Preprocessing: Techniques for extracting, transforming, and loading (ETL) massive datasets. Cleaning, filtering, and preparing data for analysis.
- Exploratory Data Analysis (EDA) in the Urban Context: Visualizing and analyzing data to identify patterns, trends, and anomalies in vehicular traffic.
- Predictive Traffic Modeling: Applying machine learning algorithms (linear regression, decision trees, neural networks) to predict vehicle flow, congestion, and travel times.
- Vehicle Flow Optimization: Strategies and algorithms for optimizing vehicle flow, including real-time traffic light control, alternative route management, and road capacity optimization.
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Time Series Analysis: Application of time series analysis techniques for traffic prediction and the identification of cyclical patterns.
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Tools and Technologies: Use of software tools and big data platforms (Python, R, Hadoop, Spark) for data analysis and visualization.
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Case Studies: Analysis of real-world cases of big data solution implementation for traffic management in different cities and urban environments.
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Data Visualization: Creation of interactive dashboards and reports to communicate data analysis results and facilitate decision-making.
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Ethical and Privacy Considerations: Discussion of the ethical and privacy aspects related to the use of traffic data.
Who this program is for:
Urban Big Data and Traffic Prediction Course
- Professionals and students interested in urban data analysis and traffic prediction, including:
- Data Engineers
- Data Scientists
- Data Analysts
- Urban Planners
- Professionals in sectors related to urban transport and mobility, such as:
- Urban Planners
- Traffic Managers
- Transport Companies
- Public Transport Authorities
- Individuals with basic knowledge in:
- Statistics
- Programming (Python, R, or others)
- Databases
- 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 Introduction to Big Data in the Urban Context
1.2 Importance of Urban Traffic Analysis
1.3 Sources of Big Data in the Urban Environment
1.4 Challenges and Opportunities of Big Data in Traffic Management
1.5 Key Concepts: Big Data, Predictive Modeling, and Data Analysis
1.6 Tools and Technologies for Big Data Analysis
1.7 The Role of Artificial Intelligence and Machine Learning
1.8 Ethics and Privacy in Traffic Data Analysis
1.9 Case Study: Success Stories in Traffic Management
1.10 Future Trends in the Use of Big Data for Urban Traffic
2.2 Urban Big Data Sources: Types and Origins
2.2 Extraction, Transformation, and Loading (ETL) of Traffic Data
2.3 Data Cleaning and Preprocessing: Managing Missing and Anomalous Values
2.4 Storing and Organizing Big Data for Analysis
2.5 Tools and Technologies for Data Preparation: Hadoop, Spark, etc.
2.6 Data Quality: Validation and Integrity Assurance
2.7 Data Integration: Merging Multiple Information Sources
2.8 Data Structuring: Designing Schemas for Efficient Analysis
2.9 Scalability: Preparing Data for Future Growth
2.20 Privacy and Security Considerations in Data Collection and Preparation
3.3 Introduction to Big Data in the Urban Context: Data Sources and Types
3.2 Data Collection and Preparation: Cleaning and Transformation Techniques
3.3 Exploratory Data Analysis (EDA) in Urban Traffic
3.4 Predictive Modeling: Key Concepts and Model Selection
3.5 Predictive Models: Regression, Time Series, and Machine Learning
3.6 Validation and Evaluation of Predictive Models
3.7 Implementation of Predictive Models: Tools and Platforms
3.8 Data Visualization and Communication of Results
3.9 Data-Driven Strategies for Optimizing Traffic Flow
3.30 Case Studies: Real-World Applications of Big Data in Urban Traffic Management
4.4 Introduction to Big Data in Urban Traffic
4.2 Big Data Sources for Traffic Analysis
4.3 Cleaning and Preprocessing Urban Data
4.4 Predictive Traffic Models: Fundamentals
4.5 Advanced Predictive Modeling: Regression and Time Series
4.6 Machine Learning Techniques for Traffic Prediction
4.7 Evaluation and Validation of Predictive Models
4.8 Implementation of Traffic Prediction Systems
4.9 Traffic Flow Optimization Based on Predictions
4.40 Case Studies and Practical Applications
5.5 Introduction to Big Data in Urban Traffic Prediction
5.5 Collection and Cleaning of Massive Traffic Data
5.3 Data Sources: Sensors, Cameras, Social Networks, and Mobile Devices
5.4 Exploratory Data Analysis (EDA) to Identify Patterns
5.5 Predictive Modeling: Regression, Time Series, and Machine Learning
5.6 Evaluation and Validation of Predictive Traffic Models
5.7 Model Optimization for Prediction Accuracy
5.8 Implementation of Real-Time Traffic Prediction Systems
5.9 Data Visualization Strategies and Results Communication
5.50 Case Studies: Real-World Applications of Traffic Prediction
6.6 Collection and Processing of Big Urban Data
6.2 Data Cleaning and Preparation for Traffic Analysis
6.3 Exploration and Visualization of Urban Traffic Data
6.4 Predictive Traffic Modeling: Regression and Time Series
6.5 Advanced Models: Neural Networks and Machine Learning for Traffic
6.6 Validation and Evaluation of Predictive Models
6.7 Implementation of Traffic Flow Optimization Strategies
6.8 Use of Big Data Tools and Platforms
6.9 Sensitivity Analysis and Scenarios in Traffic Prediction
6.60 Case Studies: Real-World Applications and Challenges in Urban Traffic
- 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”).