Urban Transport Demand Modelling Training Course

 Urban Transport Demand Modelling Training Course 

Introduction 

Urban Transport Demand Modelling is a strategic planning discipline that supports evidence-based decisions on mobility, infrastructure investment, traffic management, and sustainable urban development. Urban Transport Demand Modelling Training Course provides practical knowledge of transport demand forecasting, travel behaviour analysis, trip generation, trip distribution, mode choice, traffic assignment, transport network modelling, and scenario evaluation. Participants will explore modern transport modelling techniques, data analytics, GIS integration, transport simulation, and demand forecasting methods used to address congestion, accessibility, public transport efficiency, and sustainable mobility challenges. 

The course emphasizes the application of urban transport demand models to real-world planning and investment decisions, including projects delivered through Public-Private Partnerships. Participants will develop capabilities to evaluate transport policies, infrastructure alternatives, public transport systems, land-use changes, and mobility strategies using quantitative modelling approaches. Global case studies from major cities will demonstrate how transport demand modelling supports resilient, low-carbon, inclusive, and economically efficient urban transport systems. 

Course Objectives 

By the end of the course, participants will be able to: 

1. Apply advanced urban transport demand modelling principles and frameworks.
2. Conduct travel demand forecasting using contemporary analytical techniques.
3. Analyse trip generation, distribution, mode choice, and traffic assignment.
4. Develop transport models using real-world mobility and socioeconomic data.
5. Apply GIS and spatial analytics to transport demand modelling.
6. Evaluate public transport demand and multimodal mobility patterns.
7. Use scenario modelling for urban transport infrastructure planning.
8. Analyse congestion, accessibility, travel behaviour, and network performance.
9. Apply transport modelling to sustainable and climate-smart mobility planning.
10. Evaluate transport investments and Public-Private Partnership projects using demand evidence.
11. Interpret modelling outputs for policy and infrastructure decisions.
12. Apply data-driven techniques to transport forecasting and scenario analysis.
13. Develop evidence-based urban mobility strategies using model results.

Organizational Benefits
 

1. Improved transport infrastructure planning and investment decisions.
2. Better forecasting of travel demand and network capacity.
3. Reduced congestion through evidence-based mobility planning.
4. Improved public transport planning and service optimization.
5. Stronger appraisal of infrastructure and Public-Private Partnership projects.
6. Enhanced GIS, modelling, and transport data capabilities.
7. Improved climate-resilient and sustainable mobility strategies.
8. More effective traffic and network management.
9. Stronger institutional capacity for transport policy development.
10. Improved evaluation of alternative transport investment scenarios.

Target Audiences
 

1. Urban and transport planners.
2. Civil and transport engineers.
3. Traffic and mobility specialists.
4. Government transport and infrastructure officials.
5. Public transport operators and managers.
6. Infrastructure investment and Public-Private Partnership professionals.
7. GIS, data analytics, and modelling specialists.
8. Consultants, researchers, and urban development professionals.

Course Duration: 5 days
 
Course Modules

Module 1: Foundations of Urban Transport Demand Modelling
 

• Transport systems, mobility trends, and demand modelling fundamentals.
• Urbanization, land use, accessibility, and travel demand relationships.
• Transport supply, demand, capacity, and network interactions.
• Four-step transport modelling framework.
• Data requirements and model development principles.
• Global Case Study: London integrated transport planning.

Module 2: Travel Behaviour and Data Collection
 

• Travel behaviour, activity patterns, and mobility choices.
• Household travel surveys and stated preference surveys.
• Traffic counts, GPS, mobile, and smart-card data.
• Socioeconomic and demographic transport datasets.
• Data cleaning, validation, and quality assurance.
• Global Case Study: Singapore travel data systems.

Module 3: Trip Generation and Trip Distribution
 

• Trip production and attraction modelling.
• Land-use and socioeconomic determinants of travel demand.
• Regression and cross-classification techniques.
• Origin-Destination matrices and travel flows.
• Gravity models and distribution forecasting.
• Global Case Study: Nairobi metropolitan travel demand analysis.

Module 4: Mode Choice and Multimodal Demand
 

• Mode choice theory and behavioural modelling.
• Logit models and discrete choice analysis.
• Public transport, private vehicle, walking, and cycling demand.
• Fare, travel time, reliability, and service quality impacts.
• Multimodal and integrated transport demand analysis.
• Global Case Study: Bogotá bus rapid transit demand planning.

Module 5: Traffic Assignment and Network Modelling
 

• Transport network representation and coding.
• All-or-nothing, incremental, and equilibrium assignment.
• Congestion, capacity constraints, and network performance.
• Route choice and travel time estimation.
• Network calibration and validation techniques.
• Global Case Study: Singapore road network modelling.

Module 6: GIS, Simulation, and Advanced Modelling
 

• GIS applications in transport demand modelling.
• Spatial analysis and accessibility mapping.
• Transport simulation and scenario development.
• Big data, artificial intelligence, and machine learning applications.
• Model integration with planning and decision-support systems.
• Global Case Study: Barcelona smart mobility analytics.

Module 7: Scenario Planning and Transport Investment Appraisal
 

• Baseline, growth, policy, and infrastructure scenarios.
• Demand forecasting under alternative land-use futures.
• Cost-benefit analysis and economic appraisal.
• Infrastructure investment and Public-Private Partnership demand assessment.
• Risk, sensitivity, and uncertainty analysis.
• Global Case Study: London Crossrail demand forecasting.

Module 8: Sustainable Urban Mobility and Model Applications
 

• Low-carbon transport planning and sustainable mobility.
• Congestion management and demand management strategies.
• Transit-oriented development and accessibility planning.
• Equity, resilience, and climate adaptation considerations.
• Model outputs for policy and investment decisions.
• Global Case Study: Copenhagen sustainable mobility planning.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical transport modelling exercises using realistic datasets.
• GIS-based demonstrations and spatial analysis activities.
• Group-based scenario modelling and problem-solving exercises.
• Global case studies and comparative urban transport analysis.
• Practical project development, presentations, and feedback.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.