Urban Big Data and Traffic Prediction Course
Online course of 300 hours in urban big data and traffic prediction: 6 modules covering EN 16157 (DATEX II), Directive 2010/40/EU and Delegated Regulation (EU) 2022/670 applied to real cases, with an assigned technical tutor.
Urban Big Data and Traffic Prediction Course — studying from Ireland
Ireland's civil engineering sector is driven by public infrastructure investment in roads, water, housing and public transport, alongside rail projects such as Dublin's light rail and commuter rail upgrades. Transport Infrastructure Ireland sets standards for national roads, while Irish Rail and Iarnród Éireann operate and develop the rail network. Smart city initiatives in Dublin and other urban areas create demand for engineers working on sensors, data platforms and integrated mobility systems. Typical employers include contractors, consultancies, local authorities and public agencies, with Engineers Ireland providing professional recognition.
Fees for Ireland: €448 (Euro), with instalments and employer sponsorship available.
Relevant bodies in Ireland
Typical roles
Questions from Ireland
Is a SEIUM qualification recognised for chartered civil or rail engineering status in Ireland?
Chartered engineer status in Ireland is administered by Engineers Ireland, which sets its own criteria for membership and registration. Rail is regulated in part by the National Transport Authority and Irish Rail-related bodies, while building and infrastructure standards involve other authorities. SEIUM's qualifications are private and not accredited by any national body, so recognition is for those bodies and employers to decide.
Do Irish civil, rail and smart-city employers consider private online engineering study?
Irish infrastructure, consultancy and local authority employers usually weigh chartered status, project experience and relevant degrees heavily. A private online qualification may be considered as supporting evidence of skills, but its value is at each employer's discretion. We cannot promise any outcome, so discuss it with prospective employers.
Can I study alongside a site or office job, and is employer funding common?
The programme is online and taught in English, so you can study from home without travel or a student visa. Plan for 1,900 hours on a master's, 800 on a postgraduate diploma or 300 on a specialist course. Each student has an assigned technical tutor, fees can be paid in instalments, and employer sponsorship may be possible. Many Irish employers have continuing professional development budgets, so ask yours.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify traffic data sources and their characteristics.
Design big data architectures for real-time mobility data.
Pre-process and analyse traffic data rigorously.
Build statistical and deep learning traffic prediction models.
Validate predictions with appropriate metrics and temporal schemes.
Integrate predictions into operational management applications.
Develop a traffic prediction project for an urban corridor.
Who it's for
- Mobility data scientists.
- Traffic engineers.
- Big data analysts.
- Traffic control centre technicians.
- Smart mobility consultants.
- Data engineers building real-time pipelines.
- Engineering or science graduates looking to enter mobility and transport.
What you'll work with
- EN 16157 (DATEX II) (data exchange specifications for traffic management and information)
- Directive 2010/40/EU (deployment of intelligent transport systems)
- Delegated Regulation (EU) 2022/670 (EU-wide real-time traffic information services)
- Regulation (EU) 2016/679 (GDPR) (General Data Protection Regulation)
- ISO/IEC 20547-3 (big data reference architecture)
- ISO 14817-1 (ITS central data dictionaries)
- Delegated Regulation (EU) 2017/1926 (EU-wide multimodal travel information services)
- Directive (EU) 2023/2661 (amendment of the ITS Directive)
- Apache Spark
- Apache Kafka
- Python with PyTorch
- PostGIS
- Kepler.gl
6 modules
1.1Inductive loops and traffic counting stations1.2Cameras and computer vision1.3Radar and Bluetooth sensors1.4Floating car and GPS data1.5Mobile phone data1.6Transport cards and validations1.7Navigation app data1.8DATEX II format for data exchange1.9Fundamental variables: flow, speed, occupancy1.10Case study: inventory of traffic data sources
2.1Lambda and kappa architectures2.2Distributed storage and data lakes2.3Batch processing with Apache Spark2.4Stream processing with Apache Kafka and Flink2.5Time-series databases2.6Large-scale geospatial data2.7Cloud computing for mobility2.8Traffic data governance and quality2.9Privacy in vehicle trajectories2.10Lab: real-time ingestion of loop detector data
3.1Sensor fault detection3.2Imputation of missing data3.3Map matching of GPS traces3.4Origin-destination matrices from passive data3.5The fundamental diagram of traffic3.6Daily, weekly and seasonal patterns3.7Incident detection through anomalies3.8Spatio-temporal visualisation of congestion3.9Calculation of travel times3.10Practical exercise: origin-destination matrix from mobile phone data
4.1Short-term prediction: defining the problem4.2ARIMA statistical models and Kalman filters4.3Machine learning with gradient boosting4.4LSTM recurrent neural networks4.5Graph neural networks for traffic4.6Travel time prediction4.7Influence of weather and events4.8Error metrics and temporal validation4.9Uncertainty in predictions4.10Workshop: traffic flow prediction with LSTM
5.1Real-time traveller information5.2Integration with control centres5.3Management of special events5.4Recommended routes and diversions5.5Support for traffic signal control5.6Mobility planning with big data5.7Urban congestion indicators5.8Evaluation of mobility measures5.9Ethics in the use of travel data5.10Case study: congestion alert system
6.1Selection of the urban corridor6.2Acquisition of historical data6.3Pre-processing and quality6.4Exploratory analysis of patterns6.5Training predictive models6.6Comparison and validation6.7Visualisation of results6.8Proposal for operational integration6.9Report and presentation6.10Final project: final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: you progress at your own pace within the intake, making it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Spark, Apache Kafka, Python with PyTorch, PostGIS and Kepler.gl, in free, academic or evaluation versions. Real procedures are studied: short-term prediction of traffic flows, derivation of origin-destination matrices from passive data and automatic incident detection through anomalies. Assessment is continuous by module plus the final integrating project.
Standards covered: EN 16157 (DATEX II), Directive 2010/40/EU, Delegated Regulation (EU) 2022/670, Regulation (EU) 2016/679 (GDPR), ISO/IEC 20547-3, ISO 14817-1, Delegated Regulation (EU) 2017/1926, Directive (EU) 2023/2661.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- No prior qualification is required.
- Internet connection and a computer capable of running the programme 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.
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 Apache Spark, Apache Kafka, Python with PyTorch, PostGIS and Kepler.gl, 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 need a computer able to run the programme 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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